Prediksi Diabetes Melitus Tipe-2 Menggunakan Sequential Forward Selection (SFS) Dengan Algoritma Support Vector Machine (SVM)

Abstract: Diabetes is a chronic disease and can cause long-term complications if not handled properly. To prevent this, a machine learning model is needed to predict diabetes with high accuracy. This study aims to see the effect of reducing feature dimensions on model performance and to see the effect of data cleaning on model performance. This study used the Pima Indian Dataset, two models were created with different preprocessing stages. The first model was created without performing data cleansing, and the second model was created by performing data cleansing. After the next preprocessing stage, the number of features that produce the best performance is sought using Sequential Forward Selection and the model is drilled using the Support Vector Machine algorithm. After going through the training stage, the two models will be tested and their performance will be compared. The results showed that reducing the number of features made the model have better performance. And of the two types of models, the model that uses the data cleaning stage shows better performance.


The effect of the family environment, school environment, and community environment on students' independence through distance learning

Abstract: This research aimed to investigate the effect of the three education centers on students' independence through distance learning. This study used a quantitative approach using a correlational survey method. This study involved 2263 students from Banten Province, Indonesia, who were selected using random cluster sampling. Research data was collected through independent learning questionnaires and family, school, and community environments. All data were analyzed using multiple linear regression analysis. The results of the descriptive statistical analysis show that the average score of female students' learning independence (96.45) is higher than the average score of male students' learning independence (93.38) through distance learning, and the average junior high school students’ learning independence score (95.81) is higher than the average score of learning independence for high school/equivalent students (93.42) and tertiary institutions (94.21) through distance learning. While the results of inferential statistical testing show that in the implementation of distance learning, (1) the family environment has a significant positive influence on independence learning, (2) the school environment has no significant effect on independence learning, and (3) the community environment has a significant positive effect on learning independence. The findings can have implications for the assessment form in the learning process, which is not only the learning achievement from the content assessment but can also use another form, such as students’ independence, like in these findings.


Ability to organize experiential activities of teachers in primary schools

Abstract: This study examines the capacity of primary school teachers to design, organize, and evaluate experiential activities, utilizing a robust measurement framework grounded in the 2018 General Education Program. The evaluation encompasses 540 teachers, drawing on diverse sources, including questionnaires, regulatory documents, and input from educational stakeholders. The findings highlight commendable strengths in planning and organizing experiential activities, with 324 teachers demonstrating excellent or good capacity. However, critical gaps are evident, particularly in contextual analysis, testing tool design, and information processing, reflecting a need for targeted professional development. The measurement framework, calculated with a distance value of 0.8, employs a scale categorization that offers nuanced insights into the varying impact of experiential activities. The study underscores the significance of targeted professional development initiatives tailored to address identified gaps. Curriculum adaptations are recommended to leverage successful strategies, and informed decision-making is facilitated by evidence-based policies. The findings advocate for a culture of continuous improvement in educational practices. While the study provides valuable insights, limitations include contextual specificity, potential subjectivity in self-reported data, and the exclusion of student perspectives. A quantitative emphasis, reliance on a single methodology, and a sample size of 540 teachers may impact generalizability. This research informs educational stakeholders, policymakers, and practitioners about the current state of experiential education in primary schools. The study, within its limitations, contributes actionable insights to enhance the effectiveness of teacher training and curriculum design, fostering an environment conducive to experiential learning practices.


Digital library: Lecturers' perceptions of facilitating learning resources in the industrial era 4.0

Abstract: This research explores lecturers' perceptions of digital libraries as a learning resource for the industrial era 4.0 that can be used anytime and anywhere to solve the low interest in developing lecturers because of the difficulty of accessing references to conventional libraries, which require lecturers to visit the library building. Data was collected through semi-structured interviews with 15 lecturers, and data analysis was performed using thematic analysis. The analysis results found four main themes: benefits, challenges, recommendations, and positive and negative impacts of digital libraries. The research concluded that the existence of a digital library can make it easy for lecturers to browse the references needed quickly, easily, and flexibly just by entering the library's website page. This research contributes to solving the problem of lecturers in Indonesia over the limited learning resources in conventional libraries that are difficult to access online and require particular visiting time because they have to come directly during library operating hours. Therefore, support from the government, university leaders, and the wider community is needed to develop a digital library system to facilitate lecturers in Indonesia having adequate learning resources for the industrial era 4.0.


Analysing the Nexus between the Financial Sector and Economic Growth in Nigeria: A Comparative Investigation using, BVAR, Linear Regression (OLS), and PPML Models

Abstract:
Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).
 Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.
 Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.
 Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.


Study of Academic Achievement of Mathematics Subject in the Context of Study Habits of Students

Abstract: Students have different study habits from one another and from location to location. It is a crucial component of education since students' study practices have a big effect on their achievement in academics. Parents, educators, administrators, and members of the public are increasingly concerned about the poor level of mathematical comprehension in our country. Researchers and educators have worked very hard to determine the causes of the low performance in the subject. Despite all of these initiatives, the issues persist. Students' study habits are a powerful indicator of both their academic performance in mathematics and the quality of their education. It is unrealistic to expect students to acquire all there is to know about a subject from their teachers in the classroom; students' study habits are a combination of what they learn in and outside of the classroom. Sorenson (1991) listed several beneficial study habits, one of which is that the main goal of study should be understanding. One must focus on this for an extended period of time rather than rushing through. Effective study techniques, according to Crow & Crow (1992), include setting up a specific workspace, creating a timetable, and taking concise, well-organized notes.


Analyze and Forecast the Cyber Attack Detection

Abstract: Abstract: The relentless evolution of digital technologies has ushered in an era of unprecedented connectivity and convenience, but it has also exposed our digital infrastructure to a growing spectrum of cyber threats. In this context, the importance of robust cyber attack detection mechanisms cannot be overstated.The increasing complexity and sophistication of cyber threats necessitate equally advanced detection techniques. Cyber attack detection plays a pivotal role in safeguarding digital assets and maintaining the integrity of critical systems. This abstract delves into the foundational principles and contemporary strategies employed in this domain. Detecting cyber attacks presents a multifaceted challenge due to the ever-evolving tactics employed by malicious actors.


PENGARUH PENGGUNAAN MEDIA TORSO TERHADAP HASIL BELAJAR IPA SISWA KELAS IV SD YPK ELIM MALANU KOTA SORONG

Abstract: This research aims to find out and describe whether there is an influence of Torso Media on improving the learning achievement of science subjects at fourth grade students, at SD YPK Elim Malanu, Sorong City. In this study, researchers used a multiple choice test which was given twice (pretest and posttest) to 32 students as the research sample. The method used is a quantitative method, with data analysis techniques using the paired sample t test. The results of the research show that there is an influence of Torso Media on improving the learning outcomes of science subjects at fourth grade students. It was seen from 13,806>2.0422, or tcount > ttable, so that Ho is rejected and Ha is accepted. The conclusion is the using of Torso media has a significant effect on improving student learning outcomes in science subjects and based on the sig value, namely 0.00 < 0.05, it can be concluded that Ho is rejected and Ha is accepted. It means that there is an average difference between the pre-test and post-test, and there is an influence from learning using Torso Media on Learning outcomes of fourth grade students at YPK Elim Malanu Elementary School, Sorong City.


PEMBENTUKAN KARAKTER PEDULI SOSIAL MELALUI PEMBELAJARAN IPS DI SDI ENDE 14

Abstract: This research aims to describe the formation of social caring character through learning Social Sciences at SDI Ende 14, Mautapaga Village, East Ende District, Ende Flores Regency, East Nusa Tenggara. This study uses a qualitative method. Data collection techniques in this research were interviews, observation and documentation as well as questionnaires. The data analysis techniques in this research are data reduction, data presentation and drawing conclusions. The results of this research show that the formation of a socially caring character through learning Social Sciences is able to change the character of students for the better. Apart from learning in class, social care character formation is carried out through direct learning practices. After getting the theory from the teacher and being able to practice it in direct learning, students apply the character of social care, it becomes a habit and becomes a culture at SDI Ende 14, Mautapaga Village, East Ende District, Ende Flores Regency, East Nusa Tenggara.


MENINGKATKAN KEMAMPUAN BERFIKIR KRITIS PESERTA DIDIK MELALUI MODEL PEMBELAJARAN PROBLEM BASED LEARNING (PBL) DI KELAS VII C SMPN 7 MATARAM PADA ELEMEN NEGARA KESATUAN REPUBLIK INDONESIA TAHUN PELAJARAN 2022/2023

Abstract: This study aims to improve students' critical thinking skills through the application of the Problem Based Learning (PBL) learning model on the material about the elements of the Unitary State of the Republic of Indonesia in class VII C SMPN 7 MATARAM in the 2022/2023 academic year. The research method used is classroom action research with a cycle of planning, implementation, observation, and reflection. The subjects of this study were students of class VII C consisting of 30 students. Data were obtained through observation, written tests, interviews and documentation to measure students' critical thinking skills. The results showed that the application of the Problem Based Learning (PBL) learning model was effective in improving students' critical thinking skills. In the first cycle, only 43.33% of students reached the minimum completion criteria. However, after applying the PBL model in the second cycle, the percentage of students who reached the minimum completeness increased to 73.33%. And in the third cycle the percentage of students who reached the minimum completeness increased to 93.33%. In addition, the observation results also showed an increase in students' activities and participation during learning. Learners actively discuss, exchange opinions, and find solutions to the problems given. This shows an increase in students' critical thinking skills. Based on the results of this study, it can be a reference for teachers and other researchers who are interested in improving students' critical thinking skills through innovative learning approaches.


IMPLEMENTASI MODEL PEMBELAJARAN COOPERATIVE LEARNING TIPE JIGSAW DALAM MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR MATEMATIKA

Abstract: The aim of this research is to determine the increase in students' Mathematics learning activities and outcomes by implementing the Jigsaw type cooperative learning model in class XI IPA.1 of SMA Negeri 1 Ampel for the 2021/2022 academic year. The research was carried out in the even semester of the 2021/2022 academic year for 2 months, namely from April to May 2022. The data collection method in this research used the test method and observation method. Data collection tools include test items, observation guidelines, and test results sheets. The research results are presented as follows: (1) Implementation of the Jigsaw type cooperative learning model can increase the mathematics learning activities of class XI IPA.1 students at SMA Negeri 1 Ampel for the 2021/2022 academic year. The student activity score in cycle I was 6.86 (fair), cycle II was 7.43 (good) and cycle III was 8.29. (2) Implementation of the Jigsaw type cooperative learning model can improve the mathematics learning outcomes of class XI IPA.1 students at SMA Negeri 1 Ampel for the 2021/2022 academic year. The number of students who achieved the KKM score in cycle I to cycle III was 18 people or 95%. Apart from that, the average class score in cycle I was 73.46; in cycle II was 74.62; and in cycle III it was 77.31.


HOW READING HABIT SUPPORT STUDENTS SELF REGULATED (SRL) IN LITERAL READING SUBJECT AT THE 1ST SEMESTER IN ACADEMIC YEAR 2022/2023 UNIVERSITAS PANCA SAKTI BEKASI

Abstract: In this day reading habit was low performance especially in the Z generation who was born in the late 1990s and early 2000s. They didn’t familiar with newspaper or magazine but they look for the information by internet. So, this study aim to find out “How Reading Habit can Support Students Self Regulated in Literal Reading Subject.” This research used descriptive qualitative Methods. Creswell (2012, p.274) explained the purposes of descriptive method is to find the detailed explanation and description about the object of the research systematically. This research was conducted at the first semester of Universitas Panca Sakti in Bekasi. The subjects of this research were fifty six students at the first semester on literal reading class. The data collection get by two instruments namely interviews and questionnaire. This finding revealed during learning took place; the student who has reading habit can easier to do all Reading Test than the student hasn’t reading habit. They agreed that the students need to have their own learning skills during learning. The result proves that how reading habit can support the students’ self regulated learning especially in literal reading subject.


MENINGKATKAN NILAI KARAKTER PADA SISWA SEKOLAH DASAR DENGAN MELESTARIKAN BUDAYA GENDANG BELEQ BERBASIS TRI HITA KARANA

Abstract: Education with low character causes a crisis of national identity among the younger generation. The purpose of this article is an attempt to improve. The method used in the article is to conduct a literature review of various previous studies (literature review). The purpose of writing this article is to analyze various relevant theories, the literature used is research or reviews that have existed in scientific works and are published in journals. The result of this research is implementing Tri Hita Karana in schools through the implementation of Education and Training (Soft Skills and Entrepreneurship) with a cultural content and encouraging students to always explore the knowledge and skills of successful people and collaborate with people who have great intelligence, guide and invites students to have faith and act exemplary by always living cleanly and keeping the environment clean. Implementing Tri Hita Karana in schools through the provision of Education and Training (Soft Skills and Entrepreneurship) with a cultural content and encouraging students to always explore the knowledge and skills of successful people and collaborate with people who have great intelligence, guiding and inviting students to believe and act exemplary by always living cleanly and keeping the environment clean.


MENINGKATKAN ETIKA BERSOSIAL MEDIA MELALUI BIMBINGAN KLASIKAL TEKNIK BRAINSTORMING SISWA KELAS XI MIPA 3

Abstract: The aim of this research is to improve social media ethics by using classical guidance on brainstorming techniques for class XI MIPA 3 students at SMA Negeri 1 Ampel. This research is classroom action research. The subjects of this research were class XI MIPA 3 students with a total of 36 students. This research consists of three cycles with data collection techniques in this research using observation techniques. The data analysis technique used is qualitative descriptive analysis. The research results show that there has been an increase in social media ethics through classical guidance services using brainstorming techniques. In cycle I, there was an increase from pre-cycle 45.7% to 50% in cycle I and experienced another increase in cycle II of 60% and in cycle III to 81.25%. So, with classical guidance services, brainstorming techniques can improve social media ethics in class XI MIPA 3 students at SMA Negeri 1 Ampel Semester 2 for the 2021/2022 academic year.


Factors Affecting the Success of Agile Software Teams

Abstract: Thousands of teams are using agile software development methods, especially Scrum. Few of them are really “living” Scrum and therefore successful. This paper discusses the factors that led the Scrum team to the successful. A survey about the success factors in agile projects especially that related to the Scrum teams is presented. We proposed a new framework for the success factors of the Scrum team. According to a questionnaire is distributed to evaluate the Scrum team performance, we concluded that some of the success factors are strongly or moderated applied, but others are applied with a low degree. We calculated the Adherence degree for each success factor according to our evaluation to know its importance in the success of the software project.


Using Prototypes in Agile Software Development

Abstract: Prototypes have become an essential aspect of the development process in software development processes. Prototypes are used to generate ideas and test them with speed and flexibility in accepting changing requirements throughout the system’s life cycle. They constitute a clear difference in dealing with agile development processes in effectively addressing fluctuating requirements through continuous cooperation between developers and users. They allow developers to collect feedback from stakeholders early, ensuring that their requirements and expectations for the final product are met. Prototypes also allow testing and validation of additional requirements and functionality. In Agile software development, Prototypes are often used from the beginning of a project to accommodate recurring and fluctuating requirements and allow for flexible adaptation throughout the development process. This approach allows development teams to quickly respond to changing requirements and modify the prototype based on end-user feedback.


Approximation by frame-like multiwavelets

Abstract: Approximation properties of multivariate quasi-projection operators [Formula: see text] generated by vectors of compactly supported functions [Formula: see text], [Formula: see text] are studied. Error estimates in [Formula: see text]-norm are obtained for a wide class of such operators. For refinable function vectors [Formula: see text], [Formula: see text] quasi-projection operators are related to dual multiwavelet systems. Although the general scheme for the construction of dual multiwavelet frames in the multivariate case is known, its realization in practice is a difficult task because of the necessity of providing some additional properties. The notion of frame-like multiwavelets is a relaxed version of multiwavelet frames. Frame-like multiwavelets retain frame-type decompositions waiving the usual frame condition. This simplifies the problem of frame-like multiwavelets construction. Approximation properties of frame-like multiwavelets are established. Algorithms for constructing frame-like multiwavelets with the desired approximation order are suggested.


Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach

Abstract: Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman).


Utilizing Emerging Technology Trends and Artificial Intelligence in Higher Education

Abstract: Today’s higher education is characterized by accelerated technological advances and a growing need for adaptation. This research focuses on the utilization of emerging technological trends and artificial intelligence (AI) as innovative solutions to enhance the quality and effectiveness of teaching and learning. The problem lies in the necessity of strategically harnessing the potential of emerging technologies and AI in higher education. The objective is to thoroughly examine the implementation of emerging technologies and artificial intelligence within the realm of higher education. A comprehensive systematic review was conducted, involving the analysis of 240 articles selected from searches within the Scopus, SpringerLink, and Web of Science databases the selection process employed rigorous inclusion and exclusion criteria, to achieve this, we utilized the PRISMA methodology with a systematic approach. Consequently, our findings indicate that the integration of emerging technologies and artificial intelligence in higher education offers valuable guidance for decision-making and enhances the effectiveness of educational strategies in the digital era.


Cyberbullying Dilingkungan Sekolah : Upaya Pencegahan Dan Penanganannya

Abstract: Modern times bring rapid development of information technology in various parts of the world. In addition, technology also brings various positive and negative impacts in our lives. One of the negative impacts of the advancement of information technology is the emergence of cyberbullying. The rapid development of technology among adolescents increases the risk of cyberbullying behavior and this is a new problem that is a serious challenge in social life and must get full attention in the digital world. In addition, cyberbullying that occurs in the school environment has become an issue that is widely discussed in various situations. Cyberbullying has a negative impact on the mental health and academic condition of the victim. Therefore, further analysis of the factors of cyberbullying behavior is needed to find effective ways to prevent and deal with the phenomenon of cyberbullying in the school environment. This effort is carried out in the hope that students will no longer abuse social media as access to carry out cyberbullying and be wiser in using social media


STUDENT PREFERENCE TO USE QRIS (QUICK RESPONSE CODE INDONESIAN STANDARD) AS A DIGITAL PAYMENT INSTRUMENT

Abstract: Technological developments result in innovations in financial services called financial technology (fintech). The result of fintech development is the existence of QRIS (Quick Response Code Indonesian Standard) as an easy and practical digital payment system. QRIS attracts the attention of young people, namely students, to switch from cash to non-cash payments with QRIS. The problem in this study is how students prefer to use QRIS as a digital payment tool? is the seller involved in students' preferences in using QRIS as a digital payment tool? To answer this problem, researchers used a qualitative approach by collecting data through interviews with three types of informants, namely sellers, students who make cash payments, and students who make cash payments, and students who make payments with QRIS. The result of this study is that students' preferences in using QRIS depend on ingrained payment habits in the canteen. However, even so, the preference for QRIS convenience can support the efficiency of buying and selling transactions in canteens.


Analisis Perilaku Mahasiswa dalam Menemukan Literatur Rujukan Materi Diskusi Tutorial Online

Abstract: This research analyzes students' behavior in searching for reference literature in online tutorial discussions, highlighting various approaches used in deepening understanding of learning materials. The results indicate that students actively utilize online tutorials as their primary source to enhance understanding and skills related to the courses they are taking, as well as actively participate in online discussions. However, some students exhibit less active participation, indicating variations in student engagement in online learning. Some factors influencing low participation include differences in learning preferences and time constraints. Therefore, it is recommended to develop clear participation guidelines, provide incentives to active students, and develop more interactive learning methods in online tutorials. Additionally, educational institutions should provide strong technical support to students to ensure active participation. These efforts are expected to enhance the quality of learning and interaction among members of the academic community in online learning.


Analisis Kesantunan pada Penggunaan Campur Kode oleh Mahasiswa Prodi Pendidikan Bahasa Jepang (Kajian Pragmatik)

Abstract: Politeness is essential in interaction to avoid conflict between the speaker and the hearer. This article describes politeness by students and lecturers in academic interaction at the Japanese Program in Undiksha. This study is qualitatively approached using pragmatics design. The data were collected using the technique of observation by listening, writing notes, and participating. The data was then, analyzed using an interactive model from Miles & Huberman. The data were classi?ed according to the face theory of Brown & Levinson i.e. strategy of positive politeness and ?rategy of negative politeness. Data analysis reveals positive politeness strategies and negative politeness strategies students use in academic interaction at the Japanese Program in Undiksha. Both methods contained a constituent such as code-mixing. The reason for using code mixing in speech between lecturer and students is not only from speech situation but most part because of politeness factor. Using code-mixing of the Indonesian language and Japanese, made the speaker comfortable speaking because the code-mixing can make the utterance still in polite mode and formal form.


THE POWERING OF GUESSING GAME IN STUDENTS' WRITING DESCRIPTIVE TEXT: EFL CLASSROOM ACTIVITIE

Abstract: Enhancing writing skills in descriptive text learning by using the guessing method in class VII SMPN 1 Sugio. The benefit of this research is the guessing game method can improve the writing ability of class VII students of SMPN 1 Sugio, this type of research is Class Action Research which is carried out in cycle stages including action planning, implementation of action, observation and evaluation, and reflection. The subject of this class action research is the seventh grade students of SMPN 1 Sugio. The object of this research is the ability to write with the guessing game method. The instruments used in this study were observation sheets and learning outcomes tests. The data from this study were analyzed descriptive qualitative. The results show that the learning activities of grade VII D SMPN 1 Sugio students who used the guessing method are more active and enthusiastic in learning. In the preliminary results, the average student score is 67.1, only one student managed to reach the KKM. In the first cycle of student learning activities with the criteria of failure, with the results achieved 68.1 of 26 students, students who reached the KKM, 6 students while the second cycle of student learning activities with very good criteria, with the results achieved 76.3 of 26 students. Students who managed to reach the KKM 23 students. Based on the results of the study, it can be concluded that the learning outcomes of seventh grade students of SMPN 1 Sugio using the guessing method have increased.


Influence of School Administrators’ Transformational Leadership on the Innovation Ability of University Teachers

Abstract: With the development of higher education, the improvement of teachers' innovation ability has become an important issue. This article aims to explore the impact of transformational leadership of school administrators on the innovative ability of college teachers. Through literature review and empirical research, we analyze the difficulties and challenges faced by school administrators in improving teachers' innovation capabilities and propose effective countermeasures. The results show that transformational leadership demonstrates significant value and positive impact in higher education. Its core characteristics include being visionary, inspiring, focusing on individual and team growth, and driving organizational change. Transformational leadership plays a role in stimulating teachers' innovative thinking, promoting organizational changes, creating a positive organizational culture, and establishing effective communication mechanisms, which effectively promotes the improvement of university teachers' innovative capabilities. The composition of teachers' innovative ability involves three aspects: teaching, scientific research and practice, and is affected by multiple factors such as individuals, organizations and society. In order to improve teachers' innovation ability, these factors need to be comprehensively considered and targeted measures and support need to be adopted. There are differences in the perception and identification of transformational leadership among university teachers, which may be related to factors such as personal background, organizational culture and external environment. Strengthening communication and exchanges with teachers, establishing a fair and transparent incentive mechanism and evaluation system, providing sufficient resources and support, and advocating an open and inclusive organizational culture are key measures to enhance perception and recognition. In different subject areas and types of universities, the application of transformational leadership needs to be tailored to local conditions. Develop targeted change strategies based on the different characteristics of science and engineering, humanities and social sciences, and the differences between research-oriented and teaching-oriented universities. Finally, transformational leadership has a positive impact on teachers’ professional development, including promoting professional growth, increasing career satisfaction, and increasing intention to stay. However, during implementation, attention should be paid to possible differences in effects due to differences in context and conditions. Therefore, higher education institutions should strengthen the cultivation and introduction of transformational leadership to help teachers achieve better development and satisfaction at different levels.


Research on the Effectiveness of Metaverse-Based Learning in Business Courses: A Systematic Review

Abstract: Metaverse-based learning has emerged as an increasingly popular trend in higher education. However, previous studies on the efficiency of business education within the framework of Metaverse-based Learning is insufficient, despite its profound importance in higher education. To address this problem, the PRISMA methodology are employed to examine recent studies conducted over the last five years. The search encompassed two major databases, namely Scopus and Web of Science, supplemented by the inclusion of Google Scholar as a supporting database. A systematic exploration of the literature yielded a total of 14 articles. The review of these articles indicates that the integration of Metaverse technologies in business education offers enhancements in students' learning experiences and practical skills. Furthermore, the majority of students exhibit a positive attitude towards this educational approach. But there still exit some challenges cannot be ignored.


Sounding Sentiments: A Cognitive AI Approach to Speaker-Linked Emotion Analysis

Abstract: Abstract: Over the past few decades, there has been significant progress in sentiment analysis, primarily focusing on analyzing text. However, the field of sentiment analysis linked to audio remains relatively undeveloped in the scientific community. This study aims to address this gap by introducing sentiment analysis applied to voice transcripts, specifically focusing on distinguishing emotions of individual speakers in conversations. The proposed research article seeks to develop a sentiment analysis system capable of rapidly interacting with multiple users and analyzing the sentiment of each user's audio input. Key components of this approach include speech recognition, Mel-frequency cepstral coefficients (MFCC), dynamic time warping (DTW), sentiment analysis, and speaker recognition.


Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm

Abstract: Investment is an effort to increase economic growth in Indonesia. A popular investment in the community is gold investment. The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR). SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112. So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.


Menumbuhkembangkan Keterampilan 4C Melalui Problem Based Learning (PBL) Terintegrasi Role Playing dan Game Quizziz

Abstract:
This research discusses the use of the Problem Based Learning (PBL) learning model which is integrated with the Role Playing method and the Quizziz game platform in improving 4C skills (Critical Thinking, Communication, Collaboration, and Creativity) in 4th grade elementary school students in understanding the rights and obligations of students. Pancasila Education lessons. In the 21st century era of education, the need to develop these essential skills is crucial to prepare students to face an ever-changing environment. PBL, with a focus on solving problems relevant to everyday life, is integrated with role playing and Quizziz games as an innovative solution in learning. The research method used is descriptive qualitative with a focus on an integrated learning approach. The research was conducted at SD Negeri Semarangan 1 involving 25 grade 4 students as research subjects. Data was collected through documentation and observation techniques. The results of the research show that through structured learning steps, such as student orientation towards problems, student organization for learning, individual and group investigation guidance, development and presentation of work results, as well as analysis and evaluation of the problem solving process, 4C skills can grow and develop in student. Students engage in collaboration, communication, creativity, and critical thinking while undergoing these learning stages.

 Keywords: PBL, role playing, quizziz.


Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis

Abstract: Dengue Hemorrhagic Fever (DHF) is a disease transmitted through the bite of the Aedes Aegypti mosquito. Limapuluh Kota Regency BPS stated that the morbidity rate due to dengue fever was 14.40% per 100,000 population, this figure jumped high from the previous year with a morbidity rate of 3.30% per 100,000 population. The main symptoms of dengue fever are fever that lasts for 2-7 days, pain felt in the muscles and joints accompanied by a rash or no rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms, ranging from dengue fever, dengue hemorrhagic fever, to dengue shock syndrome. Based on this, there is a need for a classification method that can help and facilitate early diagnosis of dengue fever. The method used is Naive Bayes by classifying dengue positive and dengue negative patients. The aim of this research is to determine the results of the classification of patients suffering from dengue fever, as well as to determine the level of accuracy using the Naive Bayes method. Based on research that has been carried out, the results of the classification of patients are 58 correct and 14 patients classified incorrectly. The accuracy results obtained in this algorithm were quite high, namely 80%, while the sensitivity was 65% and the specificity was 86.5%.


Study of Robot Path Planning with Improved A* And DWA Algorithm Fusion

Abstract: For some problems of the traditional A* algorithm in robot path planning, such as inefficient search, many and complex inflection points in the designed and realized paths, as well as the inability to complete real-time dynamic path planning for obstacle avoidance, this paper proposes an improved A algorithm and combines it with Floyd for path rounding, and finally with DWA (Dynamic Window Approach) algorithm to be Fusion. In the improved A* algorithm, a carefully normalized random obstacle environment is used and the search process is optimized by reducing the search direction and redundant nodes. Meanwhile, the evaluation function is adjusted to achieve the optimal global design solution, and path optimization and obstacle avoidance are performed by the DWA algorithm. The experimental results finally show that when the robot is in a complex environment, the fusion algorithm can ensure the completion of the globally optimal route while avoiding obstacles in a timely and effective manner, which realizes more efficient path planning.


Impact of Principals' Informatization Leadership Practices to Advancement in a China Vocational School

Abstract: This study aims to explore teachers’ evaluations of principals’ information leadership and the relationship between evaluation factors. Through questionnaires and face-to-face interviews with teacher respondents, their views and opinions on principals’ information-based leadership practices were collected. First, principals’ information leadership practices were assessed through the International Society for Technology in Education (ISTE) Standards for Educational Leaders, including Equity and Citizenship Advocate, Visionary Planner, Empowering Leader, Systems Designer, and Connected Learning or other aspects. Secondly, statistical analysis methods were used to compare whether there were significant differences in the average scores of different teacher respondents' evaluation of principals' information leadership, and to explore the relationship between evaluation factors. The results show that there are certain differences in teachers' evaluations of principals' information leadership, with factors such as leadership style and vision, IT investment and resource allocation, IT governance practices and policies receiving focus. In addition, the study found that there is a certain correlation between teachers' evaluation of their supervisor's information leadership practices and influencing factors. Finally, based on the research results, recommendations for management development plans are put forward to further improve the school's informatization level and leadership. This study can provide useful reference for school administrators and promote the development and practice of information leadership.


Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province

Abstract: In DKI Jakarta Province, the number of fires increases and decreases every year. For this reason, efforts need to be made to prevent and reduce the risk of fire. BPBD DKI Jakarta is responsible for this matter. However, for these efforts to be effective, information is needed regarding fire patterns that frequently occur. Fire patterns can be seen using K-Modes categorical clustering analysis. The data used is fire data in DKI Jakarta in 2018. The optimal number of clusters was obtained as 6 clusters based on the Davies Bouldin Index value with the smallest DBI value is 6,22. Of the six clusters, cluster 3 is the cluster with the highest number of fire cases. Cluster 3 has a centroid, namely that fire cases occurred on Friday, November, in Cakung District, due to an electrical short circuit, burning down residential houses and rarely causing minor injuries, serious injuries or deaths.


Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5

Abstract: Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.


Clustering-based assessment of solar irradiation and temperature attributes for PV power generation site selection: A case of Indonesia’s Java-Bali region

Abstract: This study presents clustering-based assessments of solar attributes for locating potential solar photovoltaic (PV) power plant sites using k-means and density-based spatial clustering of applications with noise (DBSCAN) by examining the yearly average single-attribute and three-attribute clustering on a dataset of long-term hourly-based direct and diffuse irradiation, ambient temperature, and solar PV power output from 2005 to 2022. Three-attribute clustering enables stakeholders to better understand the characteristics of a cluster by collectively identifying three solar attributes and the magnitude of each attribute in an area or cluster. The presence of this information, which constitutes the clusters, suggests that these attributes have different effects on solar PV output power in different clusters. Although k-means is an effective method for investigating potential locations for PV power plant placements, DBSCAN offers users an alternative method for accomplishing a similar goal. In the case of three-attribute clustering of direct irradiation with k-means and DBSCAN, the 18-year mean value of clusters with the highest yearly average value is achieved at very similar values of 0.305 kW/m2 and 0.310 kW/m2, respectively. It turns out that only six years of direct irradiation had an annual mean value of less than 0.305 kW/m2. This finding implies that in the long run, the solar resources in terms of direct irradiation will typically surpass 0.3 kW/m2/MW installed capacity over all areas suitable for PV power plants. While focusing on the Java-Bali region, Indonesia, the findings, and methods appear to be of broader interest to policymakers, particularly in developing countries where solar PV is considered an option for sustainable energy generation.


Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Abstract: Preserving a robust online community poses a significant difficulty due to the unrestricted flexibility members have in expressing themselves and behaving. This issue can be remedied through the implementation of user behavior monitoring and analysis, followed by the implementation of suitable actions. The objective of this research is to develop an intelligent dictionary using the genetic algorithm to identify and categorize Twitter posts related to criminal activities by collecting data from tweets. Once the data is preserved, graph analysis techniques are employed to evaluate interactions between users. Next, the user behavior is analyzed using metadata analysis, whereby the chronology associated with each user profile is obtained. Furthermore, the study analyzes the behavioral patterns of users over time. Afterward, a method based on rules is used to create a structure for aspect-based analysis of sentiment. This method assesses the subjectivity of the input text, distinguishing between factual information and personal opinions. Additionally, transformer-based sentiment analysis determines whether the tweet evokes positive or negative sentiment. Furthermore, the task involves constructing a model that can accurately categorize a tweet based on its relevance to criminal activity. Ultimately, the intelligent dictionary is utilized to identify and isolate anomalous behavior by selecting provocative profiles. A Twitter profile exhibiting significant similarity with criminal cases presents a perilous risk to society.


Breast Cancer Classification using XGBoost

Abstract: Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary classification (normal/abnormal). Artificial intelligence (AI) has been employed to diagnose breast tumors swiftly and accurately at an early stage. This study employs the Extreme Gradient Boosting (XGBoost) machine learning technique for the detection and analysis of breast cancer. XGBoost provides an accuracy of 94.74% and recall of 95.24% on Wisconsin breast cancer Wisconsin (diagnostic) dataset.


The Effectiveness of Discovery Learning Model on Students’ Writing Skills at Vocational High School Diponegoro Losari

Abstract: This study investigates the impact of the discovery learning model on the writing skills of eleventh-grade students at Vocational High School Diponegoro Losari. The research used a One Group Pretest-Posttest Design and analyzed performance test scores before and after the implementation of the model. The results showed that students with the discovery learning model scored significantly higher in their analytical exposition text writing skills, with an average score of 76.66 compared to 36-45% before the model. The study also found that the use of the discovery learning model significantly improved the students' analytical exposition text writing skills.


Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression

Abstract: Modern massive data with enormous sample size and tremendous dimensionality are usually impossible to process with a single machine. They are typically stored and processed in a distributed manner. In this paper, we propose a distributed bootstrap simultaneous inference for a high-dimensional quantile regression model using massive data. Meanwhile, a communication-efficient (CE) distributed learning algorithm is developed via the CE surrogate likelihood framework and ADMM procedure, which can handle the non-smoothness of the quantile regression loss and the Lasso penalty. We theoretically prove the convergence of the algorithm and establish a lower bound on the number of communication rounds ιmin that warrant statistical accuracy and efficiency. The distributed bootstrap validity and efficiency are corroborated by an extensive simulation study.


Improving the YOLOV7 Algorithm for Object Detection within Recorded Videos

Abstract: Object detection algorithms play an important role in detecting people in surveillance videos. In recent years, with the rapid development of deep learning, the performance of object detection has improved by leaps and bounds, and the scheme of object detection by the YOLOV7 algorithm has also been born. Traditional object detection methods often fail to achieve a balance between speed and accuracy. To address these issues, in this research, an improved YOLOv7 algorithm performance is proposed to get the best speed-to-accuracy balance compared to state-of-the-art object detection within recorded videos using an effective compression method. This method calculates the difference between frames of video, and by using the zero difference approach by removing the duplicate frames from the recorded video and choosing only the meaningful frames based on many variables, including frame size, frame details, and the distance of the frames, influence the choice of a meaningful frame, and this will reduce the size of the video by eliminating the frames comparable to those chosen. Additionally, any other datasets or pre-trained weights have not been used; YOLOv7 has been exclusively trained on the MS COCO dataset from scratch. In order to ensure the effectiveness of this approach, numerous detection systems are used in this work. Additionally, positive performance results to reduce the processing time required for object detection have been attained.


Academic Citizenship Behaviour Mediates Between Green Compensations and Rewarding, Green Recruitment and Selection, and Organization Environmental Performance in The Jordanian Government Universities

Abstract: Background: This paper examines the academic citizenship behaviour mediating the effect between green compensation and rewards, green recruitment and selection, and organization environmental performance in Jordanian government universities. Methods: The paper contributes to filling this research gap by examining these effects. A sample of 404 employees in the Jordanian Government universities was analysed using contemporary variance-based structural equation modeling (Amos - SEM) software. Results: The results indicated a positive association between green compensation and rewards and two variables, namely academic citizenship behavior, and organization environmental performance. They also indicated a positive association between green recruitment and selection and two variables: academic citizenship behavior and organization environmental performance. As well as between academic citizenship behavior and organization environmental performance. Furthermore, they showed mediating effects of academic citizenship behavior in the relationship between green compensation and rewards and organization environmental performance and between green recruitment and selection and organization environmental performance. Conclusion: This paper helps to provide insights to HRM practitioners, shedding light on the importance of GHRM practices and their role in enhancing organization environmental performance.


Analysis of Visual Communication as a Means of Facing the Challenges of Technological Development in a Securities Company

Abstract: This illustrates that all countries have the same goals in seeking growth, development, progress, and continuous learning to face challenges in the future. The visual emails presented in this study aim to expand the understanding of the denotative, connotative, and mythical meanings believed to be true through the application of the value expectation theory. This theory provides a comprehensive approach to understanding how individuals form and interpret the meaning of a concept or idea, considering the aspect of value expectation. In this research, Roland Barthes' semiotics method is employed for analyzing main interpretations and representations to unravel the denotative, connotative, and mythical meanings in the presented visuals. Data collection techniques involve focus group discussions and structured in-depth interviews aligned with investor feedback. Based on the research results, exploration will be conducted on how individual perceptions of value influence their interpretation of visual understanding. This is supported by the expectation from feedback that investors desire visuals and text to support technical knowledge and portfolio growth based on the visual analysis provided by the securities company.


Literasi Dasar Bagi Para Siswa Sekolah Dasar

Abstract: Basic literacy is a basic priority or priority scale that needs to be strengthened and stabilized. The cultivation and formation of basic literacy skills is a real manifestation of the mental revolution program, even mental evolution. Therefore, basic literacy needs to be strengthened because it is the foundation for the lives of future generations. Apart from that, the basic literacy movement that is being intensified aims to support the realization of free humans. What is meant by free humans are free humans who can read and write to gain knowledge and use this knowledge for the good and sustainability of their lives. This article discusses the implementation of Community Service in Rangalaka Village, Kotabaru District, Ende Regency, which will take place from 1 to 30 August 2022. The focus of service activities is the implementation of basic literacy activities in the form of tutoring for students in Rangalaka Village, namely Lewoketo Inpres Elementary School and Satap Ligalejo Middle School. Tutoring is focused on elementary and junior high school students to provide knowledge strengthening, instilling a virtuous attitude in students so that they have good knowledge and attitudes in facing their future.


Exploring the Literature of Data Analytics Services on Cloud Computing: A Comprehensive Summary

Abstract: Data generated from the internet and modern applications is extensive and rapidly expanding. So, all modern applications must successfully perform tasks using this massive data. Therefore, one of the significant factors for the success of any application is understanding and extracting meaningful information using digital analytics tools to positively impact the application's performance and deal with challenges that can be encountered. On the other hand, cloud computing is simply an environment comprising a collection of high-performance services from various vendors. These services can frequently access and process massive amounts of data faster than a traditional computer. One of these services is cloud analytics, which applies cloud-based analytic algorithms against data in a private or public cloud to get the desired outcome. This paper differentiates between several analytics methodologies to create highly consistent, logical, and information-rich summaries. This research has discussed and analyzed many studies related to this field. Therefore, the results of this paper can be utilized to determine the advantages of these methods, which will help future researchers in their research for a more organized and thorough analysis when dealing with such applications. In addition, this paper attempts to discover new directions and propose new guidelines in this evolving field.


Rancangan dan Efektifitas Media Pembelajaran Audiovisual pada Mata Kuliah Bahasa Prancis Pariwisata

Abstract: The Covid-19 pandemic has brought considerable changes in the world of education. Learning methods that were previously carried out face-to-face must switch to online methods. This requires teacher innovation and creativity so that learning material can be conveyed properly. One of the learning media that is very helpful in distance learning is to use audiovisual media. This research is a needs analysis that aims to be able to find out the responses and needs of students towards the audiovisual media used so far in French Language Courses. Qualitative and quantitative methods were used in analyzing data in the form of questionnaires and interviews. The results of the analysis show that the use of audiovisual media as learning material gets a positive response from students. Students also hope that audiovisual material can be developed even better, including short durations, added interesting animations, subtitles so that students understand conversations, as well as repetition in terms of pronunciation and material summaries at the end of the video.


Enhancing Generic Skills Development in Higher Education in the Era of Large Language Model Artificial Intelligence

Abstract: The recently developed large language model artificial intelligence (AI) systems, such as ChatGPT, are likely to become widely used over the next few years as core tools in many areas of business. Higher education graduates will need to be adequately prepared with the necessary generic skills to exploit this technology and a detailed understanding of the risks of using it and related ethical aspects to maximize their employability. Higher education institutions will need to develop training to support this and a conceptual multi-layer framework is proposed for generic skills development in higher education relevant to the emerging new era of large language model AI. This model encapsulates traditional generic skills training for employability, the extended skill sets needed for operating as independent knowledge-workers in increasingly fragmented working contexts, and the new sets of skills in areas such as prompt engineering, output verification and bias detection, which are needed specifically for operating as effective users of advanced AI systems in business.


Analisis Penggunaan Shuujoshi Joseigo oleh Kuroko Shirai pada Anime "Toaru Kagaku No Railgun" Episode 1-3

Abstract: This research aims to describe the types of shuujoshi joseigo and their functions used by Kuroko Shirai in anime entitled Toaru Kagaku no Railgun in 1–3 episodes. The data were analyzed using Sudjianto (2007) & Pestemer's theory (2003). This qualitative descriptive research used listening and note-taking methods to collect the data. The results of this study are 114 data which are divided into nine types of shuujoshi joseigo used by Kuroko Shirai's character, namely 29 data of shuujoshi wa, 10 data of wane, 6 data of wayo, 53 data of no, 11 data noyo, 2 data of mono, as well as 1 data for each none, kashira, and teyo, whose functions are to soften sentences, provide information, show admiration and impression, express subtle question sentences, justify the situation, show assertiveness, express opinions, emphasize opinions, and express doubts


Purified Konjac Glucomannan as Thickener for Substituting Gelatin in Making Panna Cotta

Abstract: A thickener such as gelatin is essential in increasing viscosity and forming a gel system. Nowadays, gelatin is one of the most popular thickeners in food, pharmacy, and cosmetic products. Animal skins which lead to halal issues are still common sources of gelatin. Crude konjac flour (CKF) which is sourced from porang tubers and has a high degree of purity of glucomannan (GM) is a potential substitute for animal skins. Moreover, the use of KGM also supports national programs of processing agricultural products into finished products ready to export. This study applies five different CKF variants as panna cotta (PC) thickener: CKF from the fresh bulb of porang (Amorphophallus oncophyllus sp.) (CH-UP), CKF from chips of Porang (CH-CP), a native CKF, as well as laboratory-purified products of CKF (>90%-w of GM content) (DM-CKF-M3X-50, and DM-CKF-M3X-96), and commercially products of porang flour (K-TPO and K-TPM) were applied to make PC; substituting gelatin. Only CKF, DM-CKF-MX-50, and DM-CKF-M3X-96 successfully thickened PC in terms of appearances. Further, organoleptic analysis was applied to CKF, DM-CKF-MX-50, and DM-CKF-M3X-96. According to the organoleptic analysis, PC with CKF, DM-CKF-MX-50, and DM-CKF-M3X-96 was more preferred by the respondents than commercial PC. In overall, CKF application on PC exhibited improved sensory properties and tastes of PC.


Prediction of Palm Oil Production Results PT.KSI South Solok Using Ensemble k-Nearest Neighbor

Abstract: In 2022 PT. KSI experienced a decline in palm oil production due to replanting steps. In managing palm oil production PT. KSI has problems with palm oil production results not reaching targets so that it can affect the Company's Work Plan and Budget, therefore it is very necessary to predict palm oil production results so that all palm oil production and processing activities can run according to plan. The ensemble technique is a method which has predictive accuracy capabilities and is very efficient to use in the k-NN method, so there is no need to search for the optimal k value. Based on the results of the analysis that has been carried out, it can be seen that by using an ensemble the level of accuracy is 9.36%, which is considered high accuracy compared to just using a single kNN with k = 1 of 10.84%. So it can be concluded that the model has worked well with the data.


Fuzzy K-Nearest Neighbor to Predict Rainfall in Padang Pariaman District

Abstract: Rainfall is the amount of water that falls to the ground surface during a certain period which is measured in millimeters. The amount of rainfall can be estimated or predicted. One method used to predict rainfall is Data Mining, namely computer learning to analyze knowledge automatically so that a perfect new model is obtained. One of the best prediction algorithms in data mining is Fuzzy K-Nearest Neighbor (FK-NN) which uses the largest membership degree value from the test data in each class to determine the class. The number of sample classes obtained from rainfall data in Padang Pariaman Regency experienced unbalanced classes. One way to handle imbalance class cases is to use the Synthetic Minority Over-sampling Technique (SMOTE) technique which produces as much minority data as majority data. The results obtained in this study used the FK-NN classification with a total of 343 test data, parameter K=12 and using the Euclidean distance. The accuracy value was quite good, namely 76,38%.


The Impact of Experiential Learning on Students’ Academic Achievements a Case of Lukaya Town Council in Kalungu District

Abstract: This experimental study was undertaken to assess the effectiveness of experiential learning strategies in influencing students learning. This study was undertaken in the form of a naturalistic inquiry. A naturalistic inquiry is one that is conducted in a natural setting of interest to the researcher using natural methods such as observation, interviewing, thinking, reading, and writing. This investigation was conducted with three senior students at Lukaya Seed School with the purpose of assessing the effectiveness of the experiential learning strategy in realizing the set learning outcomes. The researcher used a convenient sampling technique to select all the one hundred and twelve (112) Form 3 History students to participate in the intervention. Data collected from daily journal recording as well as their scores in activities of integration were analyzed using a frequency table with the intention of establishing the mean score and the failure rate of students in history before the intervention was implemented. At the end of the innovation, students were, to a large extent, able to construct an extensive and flexible historical knowledge base. The students were able to take ownership of their learning, and out of question, the researcher observed that senior three history students had become better problem-solvers. They had transformed into aggressive, self-directed learners who collaborated, and they appeared intrinsically motivated. To a reasonable extent, the innovation engendered specific generic skills needed by students from the historic perspective, including reasoning skills, which they evidently applied to solve their own problems. There was also evidence of students becoming effective collaborators, as evidenced by their establishing common ground, resolving discrepancies, and working in groups to negotiate actions they planned to take to arrive at an agreement. Education planners need to put much emphasis on collaborative learning, at least across subjects, to develop students’ social skills because they learn a lot from their peers. Keywords: Experiential learning, Students, Education planners, History.


“I Thought It [University] was the Only Way to Go:” Competent Agents in Pursuit of Possible Paths beyond University Walls

Abstract: This qualitative study documents the experiences of youths in Montréal during both their university and post-university phases, specifically focusing on individuals whose parents originate from the Caribbean, sub-Saharan Africa, or Latin America. Through semi-structured interviews with six youths who interrupted their university studies, our investigation employs the theory of structuration and the concept of intergenerational temporality. The findings illuminate the challenges these individuals face in deciphering the intricacies of the academic landscape and highlight their struggle to establish a genuine sense of belonging within the university environment. Furthermore, our analysis sheds light on the discernible impact of parental expectations, which contribute to prolonging their educational journey. Notably, following the interruption of their university studies, our examination accentuates the agency displayed by these youths beyond the confines of the university. Their choices to either reenroll in a college or technical program or directly enter the job market underscore their agency. In conclusion, we underscore the imperative need to deconstruct the prevailing university model, advocating for a paradigm shift that prioritizes adapting the educational system to the diverse needs of students. This approach, rooted in principles of equity and social justice, emphasizes the significance of reconfiguring the educational landscape to better serve the student body.


The Application of Game in Junior English Vocabulary Teaching

Abstract: With the form and implementation of the new curriculum, many teaching methods have gradually emerged in junior high school English teaching, and game teaching method has been widely used at this time. Game teaching method is often used in English teaching, which can be taught in games and stimulate students’ interest in learning. Traditional teaching methods cannot arouse students’ interest in learning, and the efficiency of students’ classroom learning is constantly decreasing. Game teaching method can help students to learn actively. Students need to develop their brains and think actively, so as to improve their learning and innovation ability and improve their comprehensive quality.


Research on Google’s Latest Algorithm Rules and Application Strategies

Abstract: With the booming development of the Internet digital economy, the number of corporate websites has increased sharply. In order to ensure the accuracy and relevance of search results to meet the personalized needs of users, search engines must constantly iterate and optimize their algorithms and rules. At the same time, developers are also actively exploring the working mechanism of search engines and the factors that affect search rankings. These studies form the cornerstone of search engine optimization. Therefore, it is crucial for SEO practitioners to deeply analyze the latest algorithm and rule changes of search engines such as Google. This article will review and analyze the algorithm and rule changes officially announced by Google in the past four years, extract the core elements that play a key role in improving search rankings, and form an optimization application strategy.


Human Capital for Economic Development: A Study of Education Production Performance (EPP) in Khairpur

Abstract: The analysis of education production performance involves evaluating the inputs of faculty, teachers, and students to determine the academic achievement of students. This study aims to estimate the education production performance of both state and private higher secondary schools in Khairpur and identify the factors that affect instructional outcomes in the region. Education is crucial for development, as a high-quality education system is essential for increasing people's skills, knowledge, and abilities, ultimately contributing to the growth of human capital. In a developing country like Pakistan, where policymakers face challenges such as a rapidly growing population, low levels of education, and limited resources, improving educational efficiency is critical to achieving progress. A thorough understanding of the factors that contribute to instructional success is essential in this regard. To construct the model, a cross-sectional dataset was used, and a standard least squares method was employed with annual examination results as the output variable and school inputs, student characteristics, family background, and teacher characteristics as the input variables for both private and public schools. The results of the study indicate that student characteristics, teacher qualities, and family background, except for the mother's education, career, and income, have a statistically significant impact on students' academic performance. The findings of this study have significant policy implications for Khairpur and Pakistan as a whole. Improving educational outcomes will be critical in enhancing the human capital stock, which, in turn, is essential for economic growth and improving living standards. By understanding the factors that contribute to instructional success, policymakers can devise effective strategies to address the challenges faced by the education sector and improve overall educational efficiency.


Kazakhstani people’s perception of forming media literacy as one of the essential skills in the digital era

Abstract: This article examines the relevance of media literacy skills caused by the change in its content and reality due to the variety of information distributed through the mass media and the Internet. The purpose of the article is to discuss the level of the development of media literacy in the society of Kazakhstan, the extent to which the population is familiar with this concept, and the problems of teaching it. In the course of differentiating the importance of forming this important skill, surveys and the work of media literacy training centers are analyzed.


Student Motivation In Class VII Educational Learning At Smp Negeri 10 Pekanbaru

Abstract: This research aims to determine the level of student motivation in Physical Education Class VII learning at State Junior High School 10 Pekanbaru. This research is quantitative research, the method used is a descriptive method with a survey type. The population in this study was class VII students at Pekanbaru 10 State Middle School, totaling 279 students. The sampling technique uses total sampling technique. The instrument used in this research used a Likert scale or questionnaire method which was distributed to respondents. Based on research results, Student Motivation in Class VII Physical Education Learning is classified as Very High, this can be seen in the percentage results of 83% which are in the assessment range of 81%-100% with Very High criteria .


Increasing Awareness of the Village Disaster Risk Reduction Forum in Magetan Regency in Realizing Disaster Preparedness

Abstract: Disaster risk reduction forums as a capacity in disaster resilient villages often experience difficulties in realizing disaster preparedness. It is necessary to prioritize any element that is immediately realized, so that what is done truly makes society have resilience and toughness. five disaster preparedness parameters that must be prepared by the forum. These five parameters have 25 elements as constructs. Elements as a construct of disaster preparedness parameters become an instrument called Difficulty and Usefulness of Elements in Disaster Preparedness (DUEDP-Questionnaire). This DUEDP questionnaire serves as a guide enumerator for interviewing forum administrators and communities in 23 disaster-resilient villages in the Magetan district. Data was collected for three months starting May-July 2023. The element prioritization technique uses the Quadrant of Difficulty and Usefulness (QoDU) method. This method focuses on elements in the upper left quadrant, namely elements that are very useful but how to make it happen is very difficult. The research results place the elements of involving vulnerable groups in socializing about disasters, providing village funds for disaster management, preparing contingency plan documents, providing early warning system tools, and training families to provide disaster preparedness bags as the main priorities that must be immediately realized by the forum.


PREDICTION OF INDUCED STRESSES WITHIN SOIL MASS USING ARTIFICIAL NEURAL NETWORK

Abstract: In this paper, an Artificial Neural Network (ANN) is applied to predict the soil stress within a soil mass for a variety of depths and displacements under applied loading. The neural network model is created and applied for two cases, point load, and Uniform rectangular load.Theses two cases were selected among many other cases of loading as a representation of the capabilities of ANN in finding proper solutions. The first case needs one input to get one output, where in the second case we need two inputs, to get one output only.Results revealed that function approximation using neural network can be applied easily and can give accurate results by choosing the appropriate learning algorithm, number of layers, and number of neurons to solve the problem. ANN model can provide reasonable accuracy for civil engineering problems, and a more effective tool for engineering applications.


Unveiling the Catalysts of Digital Transformation Acceptance: Insights from Employees in Online Distance Learning Higher Education Institutions

Abstract: Online distance learning in higher education institutions is experiencing significant growth and is increasingly adopting digital transformation. However, employee acceptance and adoption of these changes present a major challenge. This study investigates the factors influencing employee acceptance of digital transformation in online distance learning institutions. It examines the influence of latent variables, such as effort expectancy, performance expectancy, self-efficacy, and intention, on the acceptance of digital transformation. It also expands on existing theoretical frameworks and provides empirical evidence of their importance. Data was collected from 387 employees using a structured questionnaire, and statistical analysis, including regression and PLS structural equation modelling, was used to assess the relationships between variables. The findings revealed that effort expectancy, performance expectancy, self-efficacy, and intention significantly influenced employee acceptance of digital transformation. The indirect relationship hypotheses are also supported. Several practical implications and strategies such as user-friendly technologies, effective communications, professional development opportunities and involvement in decision-making processes are identified as key strategies to enhance employee acceptance. The study contributes to the existing knowledge of acceptance theories and suggests avenues for future research including exploring additional variables such as culture and contextual factors and conducting longitudinal studies to understand the long-term effects of digital transformation.


Analisis kesadaran hukum terhadap pelanggaran penggunaan sepeda motor oleh anak di bawah umur di SMP Negeri 1 Kerinci

Abstract: This research was presented to determine the level of legal awareness regarding violations of motorbike use by minors at SMP Negeri 1 Kerinci. This research was conducted at SMP Negeri 1 Kerinci located : Jln. Raya Pugu Semurup, Desa Pugu Raya, Kecematan Air Hangat Barat, Kabupaten Kerinci. The samples that will be used as subjects for this research include: Students of SMP Negeri 1 Kerinci, parents of students of SMP Negeri 1 Kerinci who use motorbikes to school, Student Representatives of SMP Negeri 1 Kerinci, and the Kerinci Traffic Police (Polantas). The approach used is a qualitative descriptive approach. The resulting data will be a description in the form of words and does not emphasize data in the form of numbers. The data obtained and collected in this research was then analyzed so that it could be explained further. The research results showed that students have low legal awareness, where this can be seen from students' low knowledge and understanding of law, there are still many children who do not know the legal bases that regulate traffic and road transportation. Meanwhile, legal attitudes and behavior are categorized as quite high, where SMP Negeri 1 Kerinci has established rules that must be obeyed by students of SMP Negeri 1 Kerinci, and Traffic Police have carried out their duties in accordance with procedures that have been regulated or determined by law.


The function and role of big data: focusing on the diplomatic perspective

Abstract: The rise of the fourth industrial revolution has catalyzed the rapid development of information and communications technologies like AI, big data, and IoT, driving changes in international politics and diplomacy. This study examines the significance of big data and its application as a diplomatic tool, scrutinizing domestic and international instances.The analysis found that using big data and AI technologies in risk management and international politics is emerging as crucial elements of globalization and national influence. Nevertheless, the process of incorporating big data into diplomacy necessitates the resolution of current obstacles. Various applications to expedite diplomatic activities will become more viable when related protocols are defined and concerns about the ethics of managing big data are addressed.


Study of Literature: The Role of Traditional Games as a Learning Media to Instill Character Education in Elementary School Students

Abstract: Traditional games are games owned by a group of people, where in these games they adhere to the norms and customs of the community passed down from their ancestors to the next generation. The aim of this research is to describe the role of traditional games as interactive learning media to instill character education in elementary school students. Character education contained in traditional games contains the values of honesty, enthusiasm, good manners, responsibility, cooperation, discipline, hard work, helping each other, never giving up and so on. The methods used in this research are literature study, searching, analyzing and drawing conclusions. And the results obtained in this research show that traditional games can instill character education, for example religion, honesty, enthusiasm, good manners, responsibility, cooperation, discipline, sportsmanship, hard work, mutual help, never giving up and many more. Therefore, it is important to re-instill traditional games for children as a learning medium that can build good character in children


Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression

Abstract: The intersection of artificial intelligence (AI) and creative expression has sparked a revolution in various artistic domains. This review paper delves into the realms of generative music models, voice cloning, and voice transfer, exploring their significance in fostering novel avenues for creative expression. Beginning with an overview of AI's evolution in creative arts, we scrutinize the progression of AI in music generation and voice synthesis. We delve into the mechanics and impact of generative music models, followed by an examination of voice cloning technologies and their ethical implications. Furthermore, we explore the application of voice transfer in music creation and its extension to other forms of storytelling and podcasting. Ethical and legal considerations surrounding AI-generated voices are also addressed, alongside user perspectives on creativity and the technical challenges and innovations in this field. Through case studies and future directions, we illuminate the potential of AI-driven creative expression, delineating its implications for the future of art and self-expression.


Deepfake Face-Swap Animations and Affect

Abstract: The chapter analyses face-swapped deepfakes in moving images, focusing particularly on two different applications: ‘the public speech’ and ‘the porn scene’. Face-swap technologies generate fears about their abuse: for example, in connection with fake news, advertisements, or hate crime strategies. The fear is that epistemic claims (‘He said this’, ‘She did that’) in a medium that is historically associated with authenticity may affect people differently from other media (writing, audio, still images). The chapter critically questions the affect and the temporality of deepfakes, and analyses what it is like to be depicted in a deepfaked face-swapped animation. It draws on the interrelation between affect and animation, and outlines the medial and attentional ecologies of digitized bodies. Deleuze and Guattari’s ‘faciality’, Bernard Stiegler’s ‘tertiary memory’, and Mark B.N. Hansen’s notions of temporality (‘life’ and ‘artificial time’) are used to theorize about the affect of face-swaps in deepfakes.


Access to Digital Justice

Abstract: Chapter 7, Access to Digital Justice, asks the central question: how accessible is online justice? This chapter explores how those who administer justice, those who provide advice and those who use the online justice system experience it. In doing so, we explore how the use of technology in the justice system is shaped by, and may reshape, people’s orientations and sensibilities towards law and technology. We use our data, in this chapter, to explore how consciousness of how people think and feel about the law relates to their capability of acting upon it.


Digital Accounting Information System Implementation at BPKAD SAMOSIR District

Abstract: Problems at BPKAD Samosir Regency evolved over time with the changing landscape of information technology. Initially, BPKAD relied on Microsoft Office Excel software. However, with technological advancements, Microsoft Office Excel was superseded by e-finance applications. The e-finance application played a crucial role in supporting financial activities, particularly accounting, and was in use from 2019 until the conclusion of 2020. In early 2021, a further enhancement occurred as the SIPKD application (Regional Financial Management Information System) took over the role from the previous e-finance application. The aim of this research is to identify and analyze the factors that influence, either partially or simultaneously, the implementation of the Computer-Assisted Accounting System in the management of assets and finances of Samosir Regency. To conduct this research, researchers used quantitative methods with primary data, by distributing questionnaires to Samosir Regency BPKAD employees. Research participants are public service employees and honorary BPKAD Samosir Regency employees who have worked for at least two years and have made a direct contribution to the implementation of the Informal system. The research was conducted between March and April 2023. The results showed that the application of personal technical skills, user involvement, accounting expertise, and superior assistance had a positive and significant impact on the implementation of computerized accounting information systems, while training and education did not have a positive or significant impact on the implementation of information systems. computerized accounting. Human Resources Management plays a crucial role in the successful implementation and operation of Computerized Accounting Information Systems (CAIS) within an organization. This paper examines the key factors that influence the effective utilization of CAIS, including Personal Technical Ability, User Involvement, Training and Education, Accounting Knowledge, Leadership Support, and the Application of CAIS. The recommendation from this research is to add other variables outside the variables that have been researched, such as Good Government Governance, Use of Information Technology, and others. and conducting in-depth interviews regarding the advantages and disadvantages of implementing a computerized accounting information system. As well as adding references and expanding research objects such as all SKPD in Samosir Regency.


Instagram Features as Media for Learning English Listening Comprehension

Abstract: This study aimed to investigate what Instagram features that help students in learning English listening comprehension, and to find out the students’ perceptions towards Instagram as a media to learn the subject. Mixed method was used for this study, specifically in the form of a case study in one secondary school in Cimahi, West Java, Indonesia. Ten secondary school students aged between 15 and 18 years were chosen as participants of this study. The researchers tried to create Instagram content that would help the students learn the subject, combined with the use of questionnaires, classroom observation, and students’ interview to obtain data. Data from questionnaires were analyzed using percentage calculation, data from classroom observation were analyzed based on the activities in the classroom and displayed in the form of table, while students’ interview were analyzed based on deductive thematic data analysis. The result shows that the students are familiar with Instagram Story, Post, Reel, and Live. These Instagram features were used as tools for learning English Listening comprehension. The students perceived that the use of Instagram features for listening English comprehension could help them develop their potencies, make the learning materials more interested, assist them to learn and be more creative.


Analysis of Credit Card Fraud Detection Performance Using Random Forest Classifier & Neural Networks Model

Abstract: Credit card fraud is one example of data manipulation in the e-commerce industry. Because credit card fraud is so common, preventing it can be difficult. Therefore, it is important to identify credit card fraud as soon as it occurs. Determining the validity of a transaction is a fraud detection process. Credit/ Debit cards/ any financial services are small plastic cards given to members of certain financial organizations with proper identity and verification. This research is quantitative research that uses a dataset that has been verified and classified as fraudulent or non-fraudulent transactions. This research will limit itself to credit card fraud detection methods that use Neural Networks and Random Forest Classifier algorithms. Using publicly available credit card transaction data as a starting point, this study will examine how well machine learning algorithms do overall in identifying credit card fraud. First of all, we check for the presence of duplicate values in the data set. The result of this check shows that there are no duplicate values in the set, marked with the value ‘False’. Next, we focus on class in the target variable ‘Class’. The output of this check shows the wide variety of samples for each class in the goal variable ‘Class’. In this example, class 0 has 284.315 samples and class 1 has 284.315 samples. Based totally on the studies performed, it is possible to conclude that the usage of machine learning algorithms, together with Random Forest Classifier and Neural Networks, can be effective in detecting credit card fraud. The results of the algorithm performance analysis show that the model developed can identify fraudulent transactions accurately. In addition, the data preparation stages, correlation analysis, and model performance evaluation also contribute to understanding potentially fraudulent credit card transaction patterns.


A New Hybrid Algorithm Based on the Firefly Algorithm and Cuckoo Search to Estimate Survival Analysis of Truncated Inverse Gompertz Distribution

Abstract: This paper introduces the Right Truncated Inverse Gompertz Distribution (RTIGD) with two parameters δ and ϑ with some of its properties as; (Survival-Function, Hazard-Function, cumulative distribution function, probability density function, r-th moment, mean, variance, Moment Generating Function, Median, and Mode. In addition, we propose a new hybrid algorithm, namely the firefly Algorithm with Cuckoo Search Algorithm (FA_CSA) to estimate the parameters based on the survival functions of RTIGD. Simulation is utilized to compare the proposed algorithm with traditional methods, for example, the Maximum Likelihood Estimator and moment method and the standard algorithms Firefly Algorithm and Cuckoo Search. In most cases, the results demonstrate that the proposed algorithm (FA_CSA) provides an accurate estimation for the survival function since it has less mean squared error than the other estimation methods.


Task Scheduling in a Cloud Environment Based on Meta-Heuristic Approaches: A Survey

Abstract: Cloud computing is one of the emerging technologies that expands the boundaries of the internet by using centralized servers to maintain data and resources. It allows users and consumers to use various applications provided by the cloud provider, but one of the major issues is task scheduling. Task scheduling is employed for the purpose of mapping the requests of users to the appropriate resources available. This paper provides a detailed survey of the available scheduling techniques for cloud environments based on six common metaheuristic algorithms. Those algorithms are the Cuckoo Search Algorithm (CSA), Chicken Swarm Optimization (CSO), Genetic Algorithm (GA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO). The literature is analyzed from three perspectives: task type, objectives to be optimized, simulation environment, and quality of service performance metrics. In addition, the research gaps and future directions for future investigation are presented.


Recension Financial regulation of cryptoassets

Abstract: Money, as it is traditionally known, is undergoing its own digital revolution. Time will show whether we are facing a true paradigm shift in the essence of money or just mere modifications in the supports of representation. If the latter is linked to trust, the essence of money is related to trust in the issuer and the social consensus for its use and extension. The arrival of cryptoassets in the financial system has been a recurring topic of discussion in recent years.


Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Abstract: Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper, present a thre Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper presents a three-stage multi-objective path-planning method. The first stage is to locate the best or near-best solution path and avoid detected obstacles using a hybrid of the red fox–gray wolf optimizer (RFO–GWO), which finds a route from the start position to the target position. In the second step, a mutation operation using an evolutionary algorithm is utilized to enhance the length, integrity, and smoothness of the route generated by the RFO–GWO algorithm. The final step of the suggested method is refined further using a multiphase technique. By integrating the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path-planning method resembles the actual world. The simulation results indicate that this strategy creates the most viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when compared to prior path-planning methods, the simulation’s outcomes indicate that the suggested RFO–GWO method is effective in terms of the route, and the strategy is extremely competitive. The results showed a significant improvement, where the total percentage convergence time (in seconds) for RFO–GWO for the three maps was 15%, 12%, and 10%, respectively, whereas it was 35%, 41%, and 43% seconds in GWO and 34%, 35%, and 37% seconds in RFO. There was also a significant improvement in the number of nodes for RFO-GWO (2%, 3%, and 2%) compared to GWO nodes (64%, 65%, and 62%), and RFO nodes (32%, 30%, and 35%) for the same three maps. Subsequently, the smoothness of the path formed by the recommended approach was enhanced using the evolutionary algorithm (EA), where the total percentage length of the path in the worst scenario for GWO was 28% and for RFO was 26% in units, but after improvement with the RFO-GWO with EA, it became 22% in units. stages multi-objective path planning method: The first stage is to locate the best or near-best solution path and avoid the detected obstacles using a hybrid of the Red Fox-Grey Wolf optimizer (RFO-GWO) method, which finds a rout from the start position to the target position. In the second step, a mutation operation by evolutionary, are utilized to enhance the length, integrity, and smooth of the rout generated by the RFO-GWO method. the final step the suggested method is refined further by using the multiphase technique. By integrating both the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path planning method resembles the actual world. The simulation results indicate that this strategy creates the best viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when contrasted with prior path-planning methods, simulation outcomes indicate that the suggested RFO-GWO in terms of route effectivity, the strategy is extremely competitive.