Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Abstract: Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.


Polarization-Selective Enhancement of Telecom Wavelength Quantum Dot Transitions in an Elliptical Bullseye Resonator

Abstract: Semiconductor quantum dots are promising candidates for the generation of nonclassical light. Coupling a quantum dot to a device capable of providing polarization-selective enhancement of optical transitions is highly beneficial for advanced functionalities, such as efficient resonant driving schemes or applications based on optical cyclicity. Here, we demonstrate broadband polarization-selective enhancement by coupling a quantum dot emitting in the telecom O-band to an elliptical bullseye resonator. We report bright single-photon emission with a degree of linear polarization of 96%, Purcell factor of 3.9 ± 0.6, and count rates up to 3 MHz. Furthermore, we present a measurement of two-photon interference without any external polarization filtering. Finally, we demonstrate compatibility with compact Stirling cryocoolers by operating the device at temperatures up to 40 K. These results represent an important step toward practical integration of optimal quantum dot photon sources in deployment-ready setups.


Correction: Guo et al. A Siamese Transformer Network for Zero-Shot Ancient Coin Classification. J. Imaging 2023, 9, 107

Abstract: Jochen Büttner was not included as an author in the original publication [...].


Mapping words to the world: Adults, but not children, understand how mismatching descriptions refer.

Abstract: How do children learn to connect expressions (e.g., "that red apple") to the real-world objects they refer to? The dominant view in developmental psychology is that children rely on descriptive information, "red" and "apple." In contrast, linguistic theories of the adult language attribute primacy to grammatical elements: words such as "that" or "another" first establish the status of potential referents within the discourse context (old or new) before descriptions can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explored this novel prediction in adults and children. Over four experiments, we found that (a) adults relied on the articles to identify the referent, even when the description did not fit, consistent with grammar-first accounts; (b) consistent with description-first accounts, and unlike adults, 3- to 5-year-old children prioritized the descriptions provided by nouns and adjectives, despite being sensitive to grammatical information. This suggests that children connect expressions to referents differently from adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment

Abstract: Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.


In situ calibration of shear ratio forquadriwave lateral shearing interferometryusing double-slit interference

Abstract: A new method, to the best of our knowledge, based on double-slit (DS) interference is proposed to accurately estimate the shear ratio of the system, with plane wave or spherical wave incidence. Existing shear ratio calibration methods, designed primarily for lateral shearing interferometry (LSI) with plane wave incidence, are not applicable to LSIs directly testing divergent or convergent spherical waves. Equations for calculating the shear ratio using the fringe spacing of the DS interferogram and the NA of the incident spherical wave are derived in this paper. The simulation result shows that the relative error of the shear ratio value is about 0.3%, when the shear ratio is 0.1. In the experiment, the quadriwave LSI is designed with a plug-in feature. The shear ratio at integer multiples of 1/6 Talbot distance from the modified Hartmann mask was calibrated using a DS, and the results were in good agreement with theoretical values, confirming the accuracy of the method. Subsequently, with the assistance of an inductance micrometer, the shear ratio was calibrated at intervals of 0.5 mm, and the results closely matched the theoretical variation of the shear ratio caused by displacement, confirming the high precision of the method.


Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image

Abstract: Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.


Multispectral image defogging based onwavelength-dependent extinction coefficientmodel in fog

Abstract: Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has more outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing more targets hidden in uniform ground fog than state-of-the-art technologies. In addition, this work provides a method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.


Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning

Abstract: Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect.


Response to “Antisocial personality traits relationship with behaviours and beliefs on COVID‐19 containment measures: Investigation in a large Brazilian sample”

Abstract: Personality and Mental HealthEarly View RESPONSE Response to “Antisocial personality traits relationship with behaviours and beliefs on COVID-19 containment measures: Investigation in a large Brazilian sample” Lien-Chung Wei, Corresponding Author Lien-Chung Wei [email protected] orcid.org/0000-0002-7383-7309 Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), Taoyuan, Taiwan Correspondence Lien-Chung Wei, Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), 71 Longshou Street, Taoyuan District, Taoyuan 330, Taiwan. Email: [email protected]Search for more papers by this author Lien-Chung Wei, Corresponding Author Lien-Chung Wei [email protected] orcid.org/0000-0002-7383-7309 Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), Taoyuan, Taiwan Correspondence Lien-Chung Wei, Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), 71 Longshou Street, Taoyuan District, Taoyuan 330, Taiwan. Email: [email protected]Search for more papers by this author First published: 27 February 2024 https://doi.org/10.1002/pmh.1608Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. REFERENCES De Coninck, D., Duque, M., & Schwartz, S. J. (2023). Dark triad personality traits and realistic and symbolic COVID-19 threat: The role of conspiracy mentality. Scandinavian Journal of Psychology. https://doi.org/10.1111/sjop.12980 10.1111/sjop.12980 PubMedGoogle Scholar de Francisco Carvalho, L., Koich Miguel, F., Machado, G. M., de Oliveira, C., & Pianowski, G. (2023). Antisocial personality traits relationship with behaviours and beliefs on COVID-19 containment measures: Investigation in a large Brazilian sample. Personality and Mental Health. https://doi.org/10.1002/pmh.1601 10.1002/pmh.1601 PubMedGoogle Scholar Lisa, E., & Valachova, M. (2023). Dispositional employability and self-regulation in antisocial and prosocial personalities: Different contributions to employability. BMC Psychology, 11(1), 7. https://doi.org/10.1186/s40359-023-01037-1 10.1186/s40359-023-01037-1 PubMedGoogle Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation


NANOSCALE SURFACE METROLOGY WITH LIQUID CRYSTAL BASED PHASE SHIFTING ANGULAR SHEARING INTERFEROMETER

Abstract: In this Letter, a phase-shifting angular shearing interferometer has been proposed for the application in optical surface metrology (SM) by using a combination of a wedge-shaped liquid crystal (LC) cell and a polarization phase shifter. The demonstration of this angular shearing interferometer for step height measurement is accomplished with the help of a phase-shifting technique. Four phase-shifted interferograms produced by a geometrical phase shifter are subjected to a simplified Wiener deconvolution method, which resembles a simple analysis technique for shearing interferograms in comparison to alternative approaches. A simulation study has been conducted to validate the proposed technique. The experimental results show an accuracy of 5.56% for determining the step height, which also agrees with the results obtained by atomic force microscopy. Owing to the tunability of birefringence, the proposed LC-based angular shearing interferometry technique will be useful to control spatial resolution in optical metrology.


Blockchain-secure patient Digital Twin in healthcare using smart contracts

Abstract: Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.


Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation

Abstract: Objective:: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods:: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Results:: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. Conclusion:: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. Key Messages:: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.


Toward Improving the Generation Quality of Autoregressive Slot VAEs

Abstract: Abstract Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.


Noise Removal from the Image Using Convolutional Neural Networks-Based Denoising Auto Encoder

Abstract: With the exponential growth in the volume of digital images captured daily, there is an escalating demand for elevating image quality to achieve both accuracy and visual appeal. Addressing this need, the development of techniques for reducing image noise while preserving crucial features, such as edges, corners, and sharp structures, has become imperative. This paper delves into the significance of image denoising and introduces a novel approach utilizing a denoising autoencoder based on convolutional neural networks (CNNs). The proposed method adopts a meticulous two-step process to effectively eliminate noise. Initially, input images are segregated into training and testing sets. Subsequently, a denoising autoencoder model is trained using the designated training data. This model is then further refined through training on a CNN, enhancing its noise reduction capabilities. The evaluation of the system's performance is conducted using testing data to gauge its effectiveness. The study employs the MATLAB programming language for implementation and evaluation. Results, measured through RMSE (Root Mean Square Error) and PSNR (Peak Signal-to-Noise Ratio) criteria on two distinct datasets—the Covid19-radiography-database and SIIM-medical-images—reveal that our proposed method outperforms existing approaches significantly. This approach is particularly promising for applications demanding enhanced image quality, such as the resolution enhancement of medical images. The study contributes to the ongoing efforts in noise reduction research, offering a robust solution for improving visual perception in diverse image processing applications.


VIRTUAL REALITY IN THE PHILIPPINES: FACILITATORS AND PERCEIVED BARRIERS OF VIRTUAL REALITY AS A THERAPEUTIC TOOL IN THE CLINICAL SETTING

Abstract: Introduction: Virtual Reality (VR) begins to emerge in the Physical Therapy Practice here in the Philippines. However, it goes through a process of acceptance before it can be used as a part of the mainstream treatment modality in the current PT practice. Study aims to explore the perceived barriers and limitations of a handheld virtual reality as a therapeutic tool. Methods: The study employed a qualitative phenomenological study a semi-structured interview conducted via Zoom platform. A snowball sampling was utilized to gather informants’ opinions. Coding was conducted through a meticulous review of respondents by the researchers. The codes were analyzed using Collaizi Method through QDA Miner Lite. Theoretical thematic analysis was utilized to support the themes from the answers of the informants. Results: Twelve informants were initially invited to reach the number of subjects required. However, only ten agreed and consented to participate. Data saturation was met during the sixth interview. The following themes emerged: Barriers (Expensiveness, Atypical Practice), Limitations (Short-Term Functional Effect, Level of Comprehension Required to Understand VR), and Advantages (Entertaining and Usefulness to Neurological and Musculoskeletal cases). Discussion: This paper gathered information from ten registered physical therapists (PTRP) in the country who experienced the use of handheld VR (Nintendo Wii, Xbox, etc.) and their perception of their exposure to the emerging therapeutic tool. Nine out of ten subjects concluded that the cost and expense of the console and the setup of the whole handheld VR room is the main barrier as to why it is still not being used in majority of the rehabilitative clinics nowadays. As an atypical practice, it also plays a role in the perceived barriers of the informants. Most of the subjects believed that current practice in the country needs to work with technology more – in order to keep up with the ever-growing technology in healthcare.


Science Learning Game (SLG) Based on Augmented Reality Enhances Science Literacy and Critical Thinking Students Skills

Abstract: Science literacy is the ability of individuals to understand, evaluate, and apply scientific knowledge in everyday problem-solving, while critical thinking is the ability to analyze, evaluate, and make decisions based on evidence and logic. This research is based on several issues in SDN Sentul2 Kota Blitar in grade 6 post-Covid 19. This research aims to produce a product used as a learning tool in Grade 6, namely Science Learning Game (SLG) Media based on Augmented Reality to Improve Science Literacy and Critical Thinking Skills in Grade 6 Students in Kota Blitar. This research and development (R&D) study uses the ADDIE (analysis, design, development, implementation, and evaluation) model. The instrument used is a questionnaire. The research data analysis results show that the developed media can improve critical thinking skills, as evidenced by comparing the Pre-test results at 72.21% with the Post-test results at 93.57%. This media can also enhance science literacy, as indicated by the increase between the Pre-test results at 74.28% and the Post-test results at 92.14%. The implication is that the science literacy and critical thinking abilities of students in Indonesia still need improvement, which can be achieved by considering the use of instructional media. With positive results in pre-test and post-test scores, this media has the potential to enhance science literacy and critical thinking skills in students.


Hybrid Android Interface Practicum Mode to Develop Students' Science Process Skills

Abstract: Science Process Skills are abilities that students must possess because they are applicable not only to science learning but also to daily life. To be familiar with scientific method activities, students must have direct experience in order to be trained in Science Process Skills. This study aims to enhance students' Science Process Skills through hybrid android interface mode practicum learning. The research data was collected using description questions previously validated by subject matter experts. This study conducted quasi-experiments using the Nonquivalent (pretest-posttest) Control Group Design method. The research participants included control and experimental groups. The research results show an increase in Science Process Skills in the experiment class as evidenced by the achievement of aspects of observation 71.25%, interpretation 78.25%, classification 86.12%, prediction 84.45%, conducting experiments or investigations 83.34%, communicating 63.33%, hypothesizing 79.63%, applying concepts 88.34%, and asking questions 82.43%. It shows that the practicum mode of the hybrid Android interface can develop Science Process Skills


Remote Rescue Vehicle

Abstract: Search and rescue operations in collapsed buildings provide crucial challenges due to the complexity of the environment and the need for information about trapped individuals. This document presents a versatile system equipped with various sensors, including LiDAR, temperature, GPS, GSM, and Wi-Fi modules, designed to assist rescue teams. The system data collection capabilities include high-quality video, audio, and image formats that supplement situational awareness. The LiDAR sensor is used to create detailed 3D models and identify potential hazards and obstacles. The thermal sensor helps in the detection of temperature signatures and enables the identification of surviving or overheated machines. Additionally, the integration of the YOLO algorithm enables real-time object classification, providing critical insights for faster decision-making. The GSM module enables communication with the system. The Wi-Fi module enables high-speed data transmission to ensure that the collected information is quickly transmitted to the rescue team. High-quality video, audio, and image formats offer comprehensive situational awareness, support decision-making, and improve overall operational efficiency.


CONTENT CLASSIFICATION OF LETTERS

Abstract: This article talks about the content classification of the letters found in the artistic work. The previous classifications were discussed and the reaction to them was expressed. Types of letters are proved by examples.


REFORMS ARE ONGOING IN THE HIGHER EDUCATION SYSTEM

Abstract: This article discusses the reforms carried out in the higher education system, practical work in improving the quality of education, and the knowledge necessary for training future specialists. Information is provided about the social necessity of teaching the science of educational management, the integration of the preparation of students for professional activity with educational management. The opinions of the representatives of management are presented.


Examining the Developing Influence of Emerging Technologies in the Media Sector of Afghanistan

Abstract: The study sets the stage by outlining the growing significance of digital platforms in Afghan media and the transformative potential of emerging technologies in this landscape. The purpose of this research is to comprehensively analyze the current state of affairs, challenges, and opportunities associated with integrating emerging technologies into Afghanistan's media sector. Employing a mixed-methods approach, the study utilizes questionnaires and interviews to gather data from a sample size of 80 participants representing diverse media outlets in Afghanistan. Quantitative analysis involves descriptive statistics and chi-square tests, while qualitative data from interviews are thematically analyzed to provide deeper insights. The results shed light on notable disparities in technology adoption rates, with cloud computing and artificial intelligence emerging as frontrunners. Anticipated integration levels vary across technologies, with cloud computing and AI garnering significant interest. Perceived challenges in integration range from moderately to extremely challenging, while opportunities for media development are viewed as promising. In conclusion, the study underscores the transformative potential of emerging technologies in Afghanistan's media landscape, despite existing challenges. It offers recommendations such as enhancing media literacy and addressing infrastructure gaps to fully harness the benefits of technological advancements.


Sistem Pakar Diagnosa Penyakit Angsa Menggunakan Metode Forward Chaining Berbasis Web

Abstract: Geese have many benefits for human needs. Starting from meat, eggs, feathers, fat, and other uniqueness such as guarding other livestock if there are animals or strangers approaching their territory. However, in Indonesia, goose utilization is less desirable and only a few do it. This poses a probӏem for goose breeders due to the absence of knowӏedge about the diseases experienced and the handӏing soӏutions. An expert system is an artificiaӏ inteӏӏigence that supports expert decision-making. The forward chaining method provides conclusions through rules derived from existing facts. This Expert System for Diagnosing Swan Diseases Using the Web-Based Forward Chaining Method is designed to make it easier for farmers or users to consult about the diseases experienced and treatment solutions with 90% diagnostic results. This expert system is made based on a website that can be accessed easily and at any time. Based on black box testing, the results obtained show 100% system functionality, so this expert system application can be used.


Sistem Pendukung Keputusan Penentuan Lokasi Pabrik Baru Menggunakan Metode ROC dan MAUT

Abstract: Factories are places where goods and services are mass produced. Factories collect and integrate various resources, such as labor, capital, and machines. Factories have an important role in a country's economy, because they produce goods and services that society needs. Apart from that, factories can also create jobs and improve community welfare. However, factories can also have a negative impact on companies and the environment. Companies that produce goods with certain raw materials must consider the availability of raw materials at the factory location. If raw materials are not available at that location, the company must bring them in from other locations, which can increase production costs. Apart from that, companies must also consider the availability of adequate infrastructure, such as roads, electricity, air and telecommunications. If infrastructure is inadequate, companies have to invest to build it, which can also increase production costs. In this study, the ROC method and the MAUT method were used, the ROC method was used to determine the level of importance of the criteria used in selecting the factory location. The MAUT method is used to rank factory locations. The five criteria used in selecting factory locations are: availability of raw materials, availability of infrastructure, labor costs, transportation costs, and environmental impact. A total of 10 factory locations were selected based on predetermined criteria. The final results show that the best factory location is location E with a final score of 0.682, followed by location H and location F with a final score of 0.619 and 0.453 respectively.


Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Abstract: Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.


Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web

Abstract: Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.


Penerapan Metode Support Vector Machine Dalam Memprediksi Prediksi Cuaca

Abstract: Weather is a natural that has a big impact on humans. Information about weather predictions is really needed by humans, especially in the city of Medan. This information is very useful for knowing weather events around us. Data mining is a process of collecting important information from large data, so that it can help make decisions and make accurate predictions. So researchers are interested in conducting research in predicting the weather in the city of Medan using the Support Vector Machine (SVM) method as a solution for predicting the weather in the city of Medan. The application of data mining using the SVM method helps produce precise accuracy for weather based on predetermined criteria. This method is suitable for weather predictions because it is able to provide clear and accurate assessments with weather predictions of 54.55%.


Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat

Abstract: Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.


An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

Abstract: The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.


Design a Desktop-Based Load and Customer Calculation Application Information System (SIAPEL)

Abstract: In today's technological developments, many people are using technology to make work easier, as is PT. PLN Persero Customer Service Implementation Unit (UP3) Tasikmalaya. Several parts of this company, especially the section for recording expenses and customers by the PDKB Team, still use manual methods, namely by calculating using a calculator. This method is very risky, especially as it has the potential for errors in calculations or writing of the recorded numbers. Given these problems, a desktop-based load and customer calculation application information system (SIAPEL) was built. Information system solutions for related load and customer data calculation applications (SIAPEL) so that the results obtained are faster and more accurate. By making direct observations or observations, actively communicating with related fields through the interview process, and looking for research materials that support building an application as a solution to the problems faced. The load and customer calculation application information system (SIAPEL) is an application that can calculate load and customer data and can store the data as a form of company archive. The system development used is waterfall with stages or processes carried out sequentially from the system. The software used to build the load and customer calculation application information system (SIAPEL) is NetBeans 8.2, Java Development Kit 1.8, and MySql. Users can process load calculation data and process customer calculation data. And users can print reports from data that has been entered into the system database. With SIAPEL, it is hoped that it can reduce the risk of information errors and make it easier for users to calculate, store and process data. And it can be used as a more effective way to process data compared to using manual methods


Prediksi Tingkat Angkatan Kerja Terhadap Pengangguran Terbuka Di Semarang Menggunakan Regresi Linier

Abstract: Unemployment is a situation where a person who does not have a job is caused by many factors, not only because they are lazy to look for work but mostly in this region of Indonesia unemployment is caused by limited employment opportunities, a lot of competition in the world of work, the large number of the labor force, lack of experience in the world of work, and also too choosy in working. The unemployment that occurs in Semarang is caused by the high number of labor force that makes the unemployment rate more and more. In this research, the author predicts the level of unemployment in Semarang. This research is a quantitative research whose data is taken from BPS Semarang. In this research, the author uses linear regression algorithm. The algorithm is widely used in cases to predict a problem, this research produces an RMSE (Root Mean Square Error) value of 0.07 with an R Square value of 91%. The results obtained can be used as a reference for the government to see the high unemployment rate in Semarang.


UTAPIS Indonesian Word Error Detection Application: Design and Development

Abstract: People misspell and incorrectly write Indonesian words without even recognizing it. Poor and incorrect language proficiency is a common cause of word spelling mistakes. Since PT Tribun Digital Online publishes between 600 and 700 articles a day, the U-Tapis website might be of great assistance to journalists in identifying grammatical problems. Three separate cities—Jakarta, Makassar, and Solo—have participated in the testing of the U-tapis website. @ Tribun Digital Online, PT. This department is in charge of using the Flask framework to create the U-tapis website design. This study develops software or applications that are comparatively brief, succinct, and quick using the RAD (Rapid Application Development) technique. In addition to system development methods. The implementation of this method uses the U-tapis Sentence Syntax Error Detection algorithm which functions to detect an algorithm comparing two pieces of data, calculated using a formula called the Jaccard Index to check spelling errors in words. UAT test results produce all the features according to the wishes of the company owner based on the problems faced.


Modeling of Wind Power Generation in Tegal Region using Matlab Simulink

Abstract: The increasing energy demands require larger energy production. However, fossil energy sources are depleting, necessitating the use of renewable energy to replace fossil fuels and meet energy needs in Indonesia. Indonesia has a significant wind energy potential. Wind power generation modeling is carried out in one of the cities in Indonesia, namely Tegal. By using Matlab Simulink, it is expected to assist in predicting renewable energy production. Tegal is one of the regions with high wind speeds in Indonesia, specifically at 3 m/s. From the modeling, the optimal blade radius and rotor angular velocity that can generate maximum power at a wind speed of 3 m/s can be determined. Wind power generation in Tegal with a wind speed of 3 m/s has the potential to produce electrical power of 768.55 W.


SISTEM PENYIRAMAN DAN PEMUPUKAN OTOMATIS BERBASIS INTERNET OF THINGS (IOT) DENGAN PEMANTAUAN ANTARMUKA APLIKASI ANDROID

Abstract: Technological developments in the field of agriculture are now expanding widely, various modern tools must be owned to assist farmers in cultivating plants. The owners of the chili plants do watering and fertilizing by collecting water or fertilizer and then giving it to the plants one by one. This is not a problem if the plant owner has free time. However, if the plant owner is busy with other things or is having business that makes the plant owner unable to care for the plant, then this becomes a problem because when the plant does not receive sufficient care, the plant will easily wither and die. Seeing the existing problems, this research will design a system that can automatically water and fertilize plants based on IoT with monitoring from an Android application. The design of this system uses an Arduino microcontroller as a controller, the nodeMCU esp2688 wifi module to send data from the sensor to the web server, a soil moisture sensor to determine soil moisture, a real time clock (RTC) for fertilization scheduling and a relay as an automatic switch on the water pump. This system aims to help the activities of farmers who still use physical force to water and fertilize plants. The IoT-based automatic watering and fertilizing system uses the prototype development method, namely by collecting system requirements, building prototyping, evaluating prototyping, coding the system, testing the system, evaluating the system and using the system


WEBSITE SISTEM PAKAR DIAGNOSA PENYAKIT KULIT DENGAN FORWARD CHAINING (STUDI KASUS : PUSKESMAS TANGGULANGIN)

Abstract: A website-based expert system for diagnosing skin diseases using the Forward Chaining method and the Laravel Framework at the Tanggulangin Health Center. The main focus of research is to improve accessibility, accuracy of diagnosis, and public awareness of skin health. Quantitative approaches and exploratory design are applied through literature studies, interviews, surveys, system design, trials, evaluations, and improvements. The system development process involves collecting data from observations, interviews, and medical data to devise relational rules, tables of disease types, symptoms, and decision trees. The use of the Laravel Framework is the main foundation of system design, including user interface (UI) design, database structure, Forward Chaining inference logic, and diagnostic needs. System trials are conducted with test scenarios to evaluate the system's ability to diagnose various skin disease conditions. Data analysis is carried out quantitatively, especially on test results, to assess system reliability. The results of this study provide an empirical basis for recommendations for further development. Hopefully, this system can make a positive contribution in the process of diagnosing skin diseases at the Tanggulangin Health Center and increase overall public skin health awareness. This system is able to provide a diagnosis of skin diseases based on the symptoms inputted by the user. The evaluation carried out provides a positive picture regarding the effectiveness and satisfaction of users.


Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Abstract: Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).


Automatic Text Labeling Method Based on Large Language Models

Abstract: With the increasing demand for large amounts of training data for model development, this paper proposes LLM4Label, an automatic text labeling method based on large language models, to assist human labelers in annotating text data. LLM4Label first selects the most representative seed data using a clustering algorithm based on text similarity. It then constructs prompt dialogues with few-shot prompts to stimulate the language model’s performance on entity labeling tasks, enabling it to automatically and efficiently label more data. Finally, LLM4Label introduces human feedback to correct un- certain labeling results and retrains the model with the corrected annotations. Experiments show that LLM4Label achieves high- quality labeled data at low human labeling cost. The proposed method provides an effective way to obtain sizable and high- quality annotated datasets with minimal manual effort, which can strongly support downstream natural language processing tasks.


Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification

Abstract: Colon cancer is a type of cancer that attacks the last part of the human digestive tract. Factors such as an unhealthy diet, low fiber consumption, and high animal protein and fat intake can increase the risk of developing this disease. Diagnosis of colon cancer requires sophisticated diagnostic procedures such as CT scan, MRI, PET scan, ultrasound, or biopsy, which are often time-consuming and require particular expertise. This study aims to classify colon cancer based on histopathological images using a dataset of 10,000 images. This data is divided into 7,950 images for training, 2,000 for testing, and 50 for validation, aiming to achieve effective generalization. The Convolutional Neural Network (CNN) method was applied in this research with a relatively shallow architecture consisting of 4 convolution layers, 2 fully connected layers, and 1 output layer. Research results were evaluated by looking at the accuracy value of 99.55%, precision value of 99.49%, recall of 99.59%, prediction experiments on several images, and loss and accuracy graphs to detect signs of overfitting. However, this research has limitations in determining hyperparameters and layer depth, which was only tested from 1 to 5 convolution layers. Therefore, there are still opportunities for further development, such as applying unique feature extraction before the classification process


Joint Extraction of Entities and Relations Based on Multi-feature Fusion

Abstract: Joint extraction of entities and relations is essential for understanding massive text corpora. In recent years, the span-based joint models have achieved excellent results in the entity and relation extraction task. However, the previous literature and experimental results suggest that the usage of span-based method in entity and relation extraction may produce more redundant entities, although it can solve the overlapping problem of entities. In order to solve the problem of entity redundancy, this paper proposes a joint extraction model based on multi-feature fusion. The overall network follows the framework as SpERT, which is the state-of-the-art model for joint entity and relation extraction. In addition to the word embedding features in SpERT, the proposed model also considers the part-of-speech features. We believe that the part-of-speech features in entities are helpful for entity recognition and can effectively alleviate the entity redundancy problem. The proposed model is evaluated on two public data sets, CoNLL04 and ADE. The experimental results show that the proposed joint extraction model based on multi-feature fusion significantly outperforms current state-of-the-art methods.


Pembuatan Game Edukasi Bahasa Inggris Untuk Meningkatkan Kosakata Bagi Peserta Tidar

Abstract: Technology is growing rapidly and has affected many aspects of life today. Technology is being used more and more frequently and the most utilized language is English. Tidar English Course provides tutoring for English, but Tidar 7th and 8th grade participants have difficulty in learning and memorizing English vocabulary. To overcome these problems, an application is made to help Tidar 7th and 8th-grade participants learn vocabulary with educational game media. Learning will be packaged into a crossword puzzle game so that it can train their vocabulary by finding the right answer in the crossword puzzle boxes that have been provided. The development method in this research is using Game Development Life Cycle (GDLC), and the testing method uses the User Acceptance Test (UAT). The system testing in this study uses black box testing. User Acceptance Test (UAT) testing submitted to the teacher gets 100% of Tidar 82.44% for grade 7 Tidar participants and 84.22% for grade 8 Tidar participants. It can be concluded that the results of the three User Acceptance Tests (UAT) get the criteria "Very Feasible"


Aplikasi Pengenalan Tempat Dan Bangunan Bersejarah Bangka Belitung Berbasis Mobile

Abstract: The historical introduction application is an area that has information about historical heritage. However, not many people know about the history of the Bangka Belitung Islands province. In the current era, mobile applications are growing very rapidly, so it would be very good to create a media in the form of an information application for identifying historical places and buildings in the Bangka Belitung Islands province. The aim of this research is to introduce the history of cultural heritage in the Bangka Belitung Islands province by creating an application that can display images, videos, materials and descriptions of the application. Researchers hope that it will be easier for users to find out and study the application for recognizing historical places and buildings in the Bangka Belitung Islands province.


Sistem Informasi Apotek Happy Berbasis Web

Abstract: Happy pharmacy is a pharmacy that sells medicine located at street Kp. Asam, Kec. Rangkui, Pangkalpinang City, Bangka Belitung Island 33684. In running its business, Happy Pharmacy still uses a conventional system where all information regarding drug sales and sales reports is still done manually. This causes problems such as the problems that occr at the Happy Pangkalpinang Pharmacy, the author created a web-based pharmacy information system with the aim of making it easier for users to process and provide information regarding pharmacy data and to support the work effectiveness and accuracy of existing pharmacy data. the method that the author useus in building a web-based pharmacy information system uses the waterfall method which consists of several stages such as analysis, design, coding, testing and implementation. Data collection techniques used in the web-based pharmacy information system are interviews, observation and literature study. For system development, the language used PHP,MySQL.


SISTEM PAKAR DIAGNOSIS PENYAKIT MELALUI GENERAL CHECK UP BERBASIS MOBILE APPLICATION ANDROID

Abstract: Expert System is a system created by imitating the performance or thought patterns of humans, which is input into an application system with the aim of helping to facilitate human work as done by experts. Android-based Expert System can help users analyze what diseases are present in the body. Expert System for Disease Diagnosis Through General Check Up Based on Android Mobile Application at Bojong Medika Clinic is a mobile-based application system that can be used as a platform for users to record, store medical records, automatically process data from general check-ups, and remind them to do further general check-ups in an effort to make everyone using this application more concerned about their health and avoid various diseases. This application is created using Kodular and supported by Air Table as its database, with additional tools using Firebase. The system development method used is the Waterfall Model, starting from the analysis stage, then followed by design, implementation, and finally testing of the application system.


EVALUASI USABILITAS SISTEM MENGGUNAKAN METODE SYSTEM USABILITY SCALE (SUS) PADA APLIKASI AKHLAQU DENGAN PENERAPAN TEKNIK INDEXING MONG

Abstract: The Akhlaqu Application is a web-based Islamic learning platform used by Al-Falah Integrated Islamic Elementary School to facilitate the recording and reporting of students’ activities in developing their memorization. This research aims to assess the level of user satisfaction with the Akhlaqu Application using the System Usability Scale (SUS). The results showed a high level of usability, measured by a System Usability Scale (SUS) score of 89.7, indicating a “very good” quality application that meets high usability standards. In addition, the implementation of text indexes on MongoDB has a positive impact on the speed of transaction data search, especially on complex data searches, improving the efficiency and responsiveness of the application. The implications of this research involve recommendations to continue to improve the usability and performance of the Akhlaqu Application, by maintaining the use of MongoDB index techniques as a reliable solution for optimizing the database. The study concludes that the Akhlaqu Application is an effective tool for managing students’ memorization and recommends further improvements to enhance its usability and performance.


Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation

Abstract: The Quran considered the holy book for Muslims, contains scientific and historical facts affirming Islam's truth, beauty, and influence on human life. Consequently, the Quran text and its translations are valuable sources for text mining research, particularly for studying the interrelationship of its verses. One approach to grouping objects using certain algorithms is clustering, with K-Means Clustering being a prominent example. However, clustering results are often suboptimal due to the random selection of centroids. To address this, the study proposes using the Particle Swarm Optimization (PSO) algorithm, which selects centroids based on PSO results. The hybrid PSO algorithm initiates a single iteration of the K-means algorithm. It concludes either upon reaching the maximum iteration limit or when the average shift in the center of the mass vector falls below 0.0001. Evaluation of the clustering results from the three models indicates that the K-Means algorithm produced the lowest Sum of Squared Error (SSE) value of 1032.19. Additionally, the hybrid PSO algorithm generated the highest Silhouette value of 0.258 and the lowest quantization value of 0.00947. Further evaluation using a confusion matrix showed that K-Means clustering had an accuracy rate of 81.7%, K-Means with PSO had 82.5%, and the combination of K-Means with hybrid PSO yielded the highest accuracy rate of 91.1% among the three grouping model.


The Impact Of Make Every Minute Count Classroom Management On The Activities Of Class IV Students

Abstract: The present study reports the first comprehensive study on the freshwater macroinvertebrates and its habitat preferences in Bilah River, the largest riverin the Northern Sumatra. The riverside is characterized by the presence of anthropogenic and industrial activities which may alter the macroinvertebrate assemblage and biodiversity. Five moon 1, 2, 3 and 4 while group 2 consisted of station 5, 6, 7, 8, 9, and 10. Based on the diversity indices as ecological parameters, the habitat condition in Bilah River is categorized from low to moderately polluted. Spatial patterns in both environmental conditions affecting the macroinvertebrate assemblage was observed using canonical correspondence analysis (CCA) revealed the preferences from each macroinvertebrate species towards environmental conditions This study aims to find out: (1) Population variance of the two groups is the same or homogeneous, (2) Differences in average student learning outcomes, (3) The influence of class management Make Every Minute Count on student learning activity. This research is quantitative research using experimental methods and quasi-experimental design. The research population is the entire student at MI Taufiqurrahman 2 Kukusan which totaled 457 students. The sample technique used is purposive sampling. Research sample of 42 students consisting of class IV A 21 students (experimental) and IV B 21 students (kontrol). The data collection technique uses the lift as the primary instrument. From the test results the instrument obtained a Table R of 0.396 and Cronbach's Alpha of 0.836. The results analysis technique uses the t-test, which compares the two groups' rata-rata. The research findings indicate that: (1) Based on the information available, the significance level (Sig.) is less than 0.198 > 0.05, indicating a similar or homogenous population variation. (2) Based on results showing that the average learning time for the Afterwards Experiment (Make Every Minute Count) was 86,00 and the average deviation was 5,167, as well as the Average Learning Time for the Afterwards Control (Classroom Meeting) was 82,29 and the average deviation was 6,133. It may be observed that there are differences in student learning activity between the "Make Every Minute Count" and "Classroom Meeting" lesson plans. (3) Based on the Uji Wilcoxon results, Asymp.Sig. (2-Tailed) 0,000 < 0,05 or Ha is found. Thus, it is shown that there is a difference in the Make Every Minute Count staff's learning activity among fourth-grade students.


The Effect of Infrastructure Management and Work Environment on the Effectiveness of Teachers' Work in Madarasah Ibtidaiyah

Abstract: The investigation employs a quantitative strategy utilizing a survey research methodology. The research is carried out at Private Islamic Elementary Schools in the KKMI Cluster of Pancatengah Sub-District, Tasikmalaya Regency. A Simple Random Sampling technique is applied, involving a sample size of 92 teachers. Questionnaires are used for data collection, and data analysis is conducted using SPSS version 24 for Windows. The research outcomes are outlined as follows: (1) There is a noteworthy positive impact of facility management on teachers' work effectiveness at Private Islamic Elementary Schools in the KKMI Cluster of Pancatengah Sub-District, Tasikmalaya Regency, substantiated by the computed t-value surpassing the tabulated t-value (6.626 > 1.987) with a coefficient of determination of 0.553, indicating a 55.3% influence. (2) There is a significant positive effect of the work environment on teachers' work effectiveness at Private Islamic Elementary Schools in the KKMI Cluster of Pancatengah Sub-District, Tasikmalaya Regency, validated by the calculated t-value exceeding the tabulated t-value (5.351 > 1.987) with a coefficient of determination of 0.494, signifying a 49.4% impact. (3) There is a substantial positive combined impact of facility and infrastructure management and the work environment on teachers' work effectiveness at Private Islamic Elementary Schools in the KKMI Cluster of Pancatengah Sub-District, Tasikmalaya Regency, confirmed by the computed F-value surpassing the tabulated F-value (86.960 > 3.10) with a coefficient of determination of 0.661, indicating a 66.1% effect. In summary, both facility and infrastructure management and the work environment positively and significantly influence teachers' work effectiveness at Private Islamic Elementary Schools in the KKMI Cluster of Pancatengah Sub-District, Tasikmalaya Regency, both individually and collectively. This study suggests that all stakeholders should actively manage and maintain facilities and infrastructure while preserving the school's work environment to enhance teacher effectiveness consistently.


Examining Technology Use and Competence of Higher Education Academics During the COVID-19 Pandemic

Abstract: Objectives: The present study describes the utilization frequency and competencies of educational technologies among academics at a university in Turkey during the COVID-19 pandemic. Methods: Participants were 391 faculty members and lecturers working in the faculties and vocational schools of a Turkish university during the 2020–2021 academic year. A survey included questions regarding the use of educational technologies and perceived competency in the use of those technologies. Results: Academics are more familiar with distance education than hybrid or blended learning. Academics reported that blended learning, hybrid learning, and distance education provide more effective education on integrating technology but report that they mostly prefer face-to-face teaching after the COVID-19 pandemic. The top three self-reported competencies are MS Office, the university academic information system, and meeting and course management tools. More information is needed about educational technology approaches and various applications such as augmented reality, simulations, assessments, and video tools. Conclusion: Faculty use of digital tools is limited, they experience significant deficiencies in using various digital tools and systems, and they are less competent in applying these tools. Academics still consider traditional face-to-face teaching as the primary choice if they are free to make decisions in the context of education and training. Thus, there is a need for professional development focused on pedagogical educational technology approaches, models, and methodologies. Implications: Various factors such as the course type, subject matter, education level, technical infrastructure, and technological and methodological support should be evaluated within the context of digitizing universities.


Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas

Abstract: Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.


Pemanfaatan Panel Surya untuk Usaha Indekos guna Menambah Nilai Ekonomi yang Ramah Lingkungan

Abstract:
Energy needs are increasing every year, on the other hand energy stocks from fossil materials will be depleted if they continue to be used, because energy from fossil sources cannot be renewed. Therefore, today we must utilize energy from new renewable energy sources because it can be renewed and environmentally friendly. Solar energy cannot be directly used and must be converted first into electrical energy. To convert in the form of electrical energy the components needed to convert solar energy into electrical energy are called solar cells. With the need for sufficiently large electrical energy, private homes should have switched and must take advantage of the existing potential of solar energy to be converted into electrical energy in meeting the electricity needs needed daily. This research focuses on descriptive research. By using a quantitative descriptive method, it is expected to produce research results that are truly in accordance with field conditions. The results of the calculations that can be concluded are that the investment costs are only around 4.3 years with an average use of panels that can reach a period of 25 years, and the power generated in 1 year is 18,885 kWh. Then the revenue generated from PLTS is IDR 38,525,400.
 Keywords: Cost, Economy, Energy, Solar Panels.


Tinjauan berbasis gender: Kemampuan literasi sains peserta didik dalam mitigasi bencana banjir

Abstract: This study aims to analyze the effect of gender differences on students' scientific literacy abilities towards the flood disaster mitigation process through four components of the science process including the science process component, the physics concept component, the impact component, and the response attitude component. The method used is descriptive analysis method with a quantitative approach with a sample of 60 students majoring in Mathematics and Natural Sciences consisting of 30 male students and 30 female students. The results showed that there was a gender influence on students' scientific literacy skills. Female students have a higher value of science skills with an overall average score of 60.6 and fall into the moderate category. Whereas for male students, the average overall score of scientific literacy skills was 40.4 and included in the low category.


A Study on the Principles of Development of IBT Korean Language Proficiency Test: Focusing on Connectivity with the Archives for Self-Directed Learning

Abstract: This paper aims to examine the development principles of the Korean language proficiency test developed by the Korean Language Institute of Y University as a private qualification test, by focusing on combining it with self-directed learning archives. Chapter 2 introduced the IBT proficiency test and looked into the concept of archives and the need for archives for Korean learners' self-directed learning. In Chapter 3, the learner-centered assessment principles of this test tool were examined, and the development principles related to the Korean language proficiency level index were examined in terms of the hierarchy of grammatical knowledge, systematicity of vocabulary arrangement, and complementarity of discourse function constructs. In addition, it was discussed how this test tool could be linked with the self-directed learning archive in order to properly perform the diagnosis and feedback, the original roles of assessment.


A Study on Teacher’s Speech Act in Korean Speaking Test: Focusing on the Teacher Talk at Each Stage

Abstract: In this study, the speech act used by five Korean teachers in the Korean speaking test were examined according to the structure of conversation sequences. In the preparation and explanation stage, teachers were using speech acts such as 'elicitation, comment, prompt, and marker' to introduce the speaking test. At the beginning of the test, there was no teacher immediately presented the test questions without the speech act for introduction. In the evaluation progress stage, Korean language teachers used speech acts such as 'starter, informative, elicitation, prompt, accept, and loop' to induce more utterances and learners' responses. In the finishing stage, the Korean language teacher was using speech acts such as 'accept, evaluate, check, and reply' to arrange the entire speaking test situation.


A Study on the Development Direction of Korean Language Introductory Textbooks for Overseas Korean Learners through Textbook Analysis

Abstract: This paper focuses on Korean as a heritage language (KHL) education, particularly highlighting Korean alphabet instruction as the foundation of Korean language learning. Korean as a heritage language distinguishes itself from the local community's language, serving as a bridge for specific individuals, groups, families, and communities. KHL learners differ significantly from the traditionally targeted learners, necessitating tailored educational approaches and customized learning materials. Despite preschool and elementary students comprising over 63% of global Korean language school enrollment in 2019, research on education for this demographic lags behind that for adults. Additionally, existing textbooks often overlook the unique characteristics of young learners. This paper pursues the following objectives: 1) Identifying KHL education characteristics and methods to integrate them into Korean alphabet instructional materials. 2) Examining domestic and international Korean language textbooks for crucial insights in introductory-level textbook development. 3) Establishing a foundation for ongoing research on Korean alphabet education for KHL learners. Against this backdrop, Chapter 2 explores heritage language Korean education, Korean alphabet instruction for child learners, and features specific to Korean language schools. Chapter 3 analyzes 10 selected textbooks, drawing on established criteria to identify and discuss their unique features. This analysis provides implications for future research and development in KHL education.


A Study on Designing a Syllabus for Business Korean

Abstract: More and More foreign students studying at domestic universities want to get a job at Korean companies or at companies related to Korea after graduation. In line with this, business Koreanlanguage courses are being operated to help foreign students prepare for employment and facilitate their company life after employment. The purpose of this study is to design a syllabus of business Koreanlanguage classes to cultivate the employment skills of foreign learners majoring in Korean language and culture at domestic universities. To this end, the concept of business Korean and previous studies are reviewed. In addition, a demand survey is conducted for learners and business Korean textbooks are analyzed. Based on this, a syllabus for business Korean is proposed to ensure that foreign students successfully prepare for employment and to accumulate knowledge necessary for company life.


Polynomial Solutions of Electric Field Equations in Anisotropic Media

Abstract:
The time-dependent system of partial differential equations of the
 second order describing the electric wave propagation in
 electrically and magnetically anisotropic homogeneous media is
 considered in the paper. A method for the computation of the polynomial solutions of the initial value problem for the considered system is proposed.
 Symbolic computations are used and these symbolic computations are implemented in Maple. It is proved also that these polynomial solutions are approximate solutions of the considered initial value problem with smooth initial data and the inhomogeneous term. The computational experiments confirm the robustness of the suggested method for the computation of electric fields in general electrically and magnetically anisotropic media.


Analisis Kombinasi Algoritma K-Means Clustering dan TOPSIS Untuk Menentukan Pendekatan Strategi Marketing Berdasarkan Background Target Audiens

Abstract: The promotion is an annual agenda for STIMIK Tunas Bangsa Banjarnegara. The aim of this promotional activity is to attract more new students every year. On the other hand, campus promotion encounters obstacles in mapping applicant data from previous years so that considerations for new promotion policies are based on data from the school of origin of alumni or students. By using the K-Means Clustering algorithm, applicant data can be grouped according to the background represented through the school origin attribute. , parents' occupation and place of origin. Then the data is processed using DSS with the TOPSIS method to obtain priority references for marketing types for each cluster. The results of calculating the silhouette coefficient value for the five clusters obtained a score of 0.426. Meanwhile, in the ranking process using the TOPSIS method, the first rank was found in cluster 0 with a score of 0.994110. Further stages use the Decision Tree method to obtain output in the form of recommendations for promotion types for each cluster. For example, cluster 0 is recommended to use promotion types with codes P1, P2, P3, P8 and P9.


PROSPECTS AND RISKS FOR THE DEVELOPMENT OF INDIVIDUAL SUBJECTIVITY IN THE CONTEXT OF DIGITALIZATION OF EDUCATION

Abstract: The purpose of the study is to analyze the prospects and risks of developing individual subjectivity in the context of digitalization of the education system. Methodological basis of the study is the activity approach to understanding the subjectivity of the individual, as well as the provisions of the concept of post-industrial society, theories of digital transformation and digital culture. The results of the study. Currently, the development of subjectivity in the educational sphere is due to its digital platformization, which allows the use of innovative forms and methods in the learning process. The main goal of digitalization of the educational sphere is to increase the efficiency of the educational process, prepare students for life and activity in a digital society. At the same time, the authors note that in order to develop significant general cultural and professional competencies, it is necessary to preserve traditional forms of education, which involve «live» communication between teacher and student. The prospects of the research. The problem of subjectivity in the context of digitalization of education is of scientific and practical interest in connection with the need to understand the further transformation of the educational environment and its impact on the subjectivity of the individual.


TRANSFORMATION OF INSTITUTIONAL TEACHER CODES OF THE COGNITIVE MODEL “QUALITY” IN THE CONTEXT OF THE SEMANTIC AND LINGUISTIC CRISIS OF MODERN TIME

Abstract: Objective of the study is – the construction of the ideological linguocognitive matrix «Institute of a Teacher», in which the central place is given to the correlative «teacher codes» as implicators of the cognitive model «Quality» of the semantic and linguistic crisis of our time. The methodological basis of the research. An attempt is made to formulate a methodological basis for studying the transformation of institutional teacher codes of the cognitive model «Quality» in the context of the semantic and linguistic crisis of modernity within the framework of an integrated methodology of transdisciplinary knowledge of linguistic and philosophical research, dialectical-trialectical and cultural-temporal approaches being developed. Within the framework of these studies one of the dominant roles is intended to be played by a contaminated analysis of the linguistic and logical-philosophical aspects of the category «Quality», which is one of the central categories of the structuring of the value-oriented system of modern society. Research results. Based on the conducted critical analysis of the current state of the integral vectors of development of sociopragmalinguistics and the linguo-philosophical direction of practical philosophy of communication management, it is necessary to postulate the interdisciplinarity of the concept of semantic-linguistic crisis. It can be stated that the core of the concept of MEANING-LANGUAGE CRISIS in its transdisciplinary understanding will be cultural-temporal, i.e. forming dialectical-trialectical unity, analysis-crisis structure of communicative-semiotic fields. It is this fact that forces us to consider it in a multi-paradigmatic manner based on the categorical apparatus of philosophy, social psychology, cultural studies, as well as special sociolinguistics, structural linguistics and cognitive science. Prospects of the study. The planned fundamental research of the above data allows us to state the objective need for further study of the changing language policy. The prospect of studying the effective preservation of correlative "teacher codes" as implicators of the cognitive model "Quality" of the semantic-linguistic crisis of modernity is being built.


Source-based Waste Management (Research Study in Bresela Village, Payangan District, Gianyar Regency)

Abstract:
Waste is a problem that is often faced by various countries in the world. The main problem is how to manage waste properly and correctly. Improper waste management can cause pollution, environmental damage, and have a negative impact on human life. Hence a solution is needed to minimize the waste problem. So Bresela Village Regulation No. 3 of 2021 concerning Source-Based Waste Management was issued (Research Study in Bresela Village, Payangan District, Gianyar Regency). The aims of this research are: (1) To find out the implementation of Bresela Village Regulation No. 3 of 2021 concerning Source-Based Waste Management in Bresela Village, Payangan District, Gianyar Regency. (2) And to find out the obstacles faced in the implementation of Bresela Village Regulation No. 3 of 2021 Concerning Source-Based Waste Management in Bresela Village, Payangan District, Gianyar Regency. The results of this research show that: (1) Implementation of Bresela Village Regulation No. 3 of 2021 concerning Source-Based Waste Management with 4 variables, namely communication, disposition, resources, and bureaucratic structure, has not run optimally. (2) Obstacles faced in Implementing Village Regulations Bresela No. 3 of 2021 concerning Source- Based Waste Management in Bresela Village, Payangan District, Gianyar Regency, including the lack of facilities and infrastructure for waste management.
 Keywords: implementation, village regulations, waste management


Intellectual Property Protection in the Transformation of Medical Scientific and Technological Achievements

Abstract: In the transformation of medical scientific and technological achievements, intellectual property disputes caused by improper protection of intellectual property rights are common, which have seriously affected the efficiency of the transformation of medical scientific and technological achievements. Therefore, this article takes the importance of intellectual property protection in the process of transformation of medical scientific and technological achievements as an entry point, analyzes the current intellectual property infringement behaviors and manifestations, puts forward specific solutions, and improves the efficiency of the transformation of medical scientific and technological achievements.


Analisis Kesalahan Siswa Dalam Menyelesaikan Soal Cerita Pada Pembelajaran Matematika Di Kelas Vii Smpn 1 Lareh Sago Halaban Berdasarkan Tahapan Newman

Abstract: This study aims to analyze student errors in solving word problems in mathematics learning. One way that can be used to find out why this happened is by analyzing students' mistakes in solving word problems in mathematics learning based on Newman's stages in class VII SMPN 1 Lareh Sago Halaban. This research is a qualitative descriptive study. Data collection was carried out by collecting students' daily test results for analysis based on Newman's stages and interviews. The research subjects in this study were taken from students in class VII SMPN 1 Lareh Sago Halaban. The research subjects were taken based on the number of subjects making mistakes from the results of students' daily tests. Each research subject was interviewed regarding the results of his work in solving math word problems based on Newman's stages. Data validity test was carried out by technical triangulation.The results showed that the subject made mistakes at the stages of understanding, transforming, processing ability and writing the final answer due to carelessness and lack of understanding of the subject's mathematical concepts.


A Study on the Historical Records-Based Instructional Plan about Property Inheritance in the Goryeo Dynasty

Abstract: This study presented a teaching method that can induce experience of the process of deriving historical knowledge based on historical records related to property inheritance during the Goryeo Dynasty.First, by analysing the historical data related to the Son Byeon (孫抃) judgement on the brother-sister lawsuit, which is one of the cases of equal inheritance of property between children in the Goryeo Dynasty, a teaching plan was proposed to infer that the record reflects the "practice" of the time beyond the "case". Given the situation of historical research, this can be an experience of the stage of establishing a related hypothesis through case analysis.Then, a class plan was constructed to raise additional questions about the equal inheritance of property in the Goryeo Dynasty in the process of reviewing other related cases with the historical knowledge or hypothesis identified above in mind, and to explore them. Specifically, it is a class that raises questions about the aspect of "equality" through the case of Yoon Seon-jwa(尹宣佐), and deduces the specific aspect of "equality" through the interpretation of the 1122 ruling on inheritance.If the above is a method of learning historical materials that explores the process of deriving historical knowledge of textbooks and their specific content through historical materials, the last step of the in-depth class is a class that can explore the relationship between the interpretation of historical materials and the researcher's perspective, which is the core of historical research. Here, an interpretation from the 1950s is presented, which differs significantly from the 'current' interpretation of the 1122 decision used above, and the state of research and research perspectives on the family and kinship system at the time are considered as factors influencing this interpretation.It is believed that this lesson plan is more in line with the original aim of experiencing historical thinking by allowing students to experience the process of deriving historical knowledge more practically through the analysis of historical materials. Furthermore, it is hoped that it will help students to accept the historical knowledge of textbooks as provisional by understanding that the 'present' perspective - problem awareness and research situation - is an important background for exploring the creation of such historical knowledge.


“Rus', You are but a Kiss in the Cold!” (1921) by V. Khlebnikov: Text as a Combinatory Model / Dynamic Construction

Abstract: The paper is devoted to analysis of the poem by Velimir Khlebnikov “Rus', you are but a kiss in the frost!” (1921) as part of a study of transformation of the sonnet genre in the poet’s verses. Velimir Khlebnikov elaborates his version of a lyrical story of ‘a kiss in the frost’, originating from the poem by Alexander Pushkin “Winter. What do we do in the village?…” (1829). The love scene is represented as a natural phenomenon, embedded not only in the winter landscape but also in the Universe. There are no persons in the Nature’s integrity, and the couple of lovers are designated only by demonstrative pronouns. The tension of feelings (“the lightning kiss”) is but one of the manifestations of the Elements. The phenomenon of a lightning as a “potentially energy-embracing symbol” has a conceptual character in Khlebnikov’s perception of the world, is both the “primordial image” of the world and the principle of universal unity, which has already been noted by the eminent researchers’ of Khlebnikov’s works. From the formal viewpoint, the text is a special variant of structural deformity and overcoming it, which is directly related to the idea of accumulation of energy, explosion, and return to tranquility. The poem is characterized by increased expression of the arrangement of its sounds; alliteration of the occlusive, fricative, and sonorant sounds, their combinations, and paronymic attraction create the effect of a special energy of sound-making. The rhythmic arrangement of the text “deceives” the reader’s expectations: fast shifts and transitions from one meter to another (polymetry in the first stanza), occasional caesuras in the first, fourth, and ninth verses, an enjambement in the seventh and eighth verses: breakage of the seventh verse, when the verb “would [swiftly] outline” starts a line, visualizing the movement of a lightning. Structural deformities are settled in the final — the last verse is written in a classical iambic tetrameter. Graphically, the lyrical text is a combinatory dynamic model which may vary in terms of strophic division, the “headless” sonnet, preserving the characteristics of the Italian sonnet, dominates. The mechanism of cohesion is ensured by all the levels of the artistic whole, and the genre of the sonnet is a harmonizing form, balancing the structural instability of the text.


A Comparative Study of Random Forest and Double Random Forest Models from View Points of Their Interpretability

Abstract: Purpose: This study aims to compare the performance of ensemble trees such as Random Forest (RF) and Double Random Forest (DRF) from view points of interpretability of the models. Both models have strong predictive performance but the inner working of the models is not human understandable. Model interpretability is required to explain the relationship between the predictors and the response. We apply association rules to simplify the essence of the models.Methods: This study compares interpretability of RF and DRF using association rules. Each decision tree formed from each model is converted into if-then rules by following the path from root node to leaf nodes. The data was selected in such a way that they were underfit data. This is due to the fact that DRF has been shown by other researchers to overcome the underfitting problem faced by RF. A Simulation study has been conducted to evaluate the extracted rules from RF and DRF. The rules extracted from both models are compared in terms of model interpretability based on support and confidence values. Association rules may also be applied to identify the characteristics of poor people who are working in Yogyakarta.Result: The simulation results revealed that the interpretability of DRF outperformed RF especially in the case of modelling underfit data. On the other hand, using empirical data we have been able to characterize the profile of poor people who are working in Yogyakarta based on the most frequent rules.Novelty: Research on interpretable DRF is still rare, especially the interpretation model using association rules. Previous studies focused only on interpreting the random forest model using association rules. In this study, the rules extracted from the random forest and double random forest models are compared based on the quality of the rules extracted.


Knowledge Discovery from Confusion Matrix of Pruned CART in Imbalanced Microarray Data Ovarian Cancer Classification

Abstract: Purpose: The results of microarray data analysis is important in cancer diagnosis, especially in early stages asymptomatic cancers like ovarian cancer. One of the challenges in analyzing microarray data is the problem of imbalanced data. Unfortunately, research that carries out cancer classification from microarray data often ignores this challenge, so that it doesn’t use appropriate evaluation metrics. It makes the results biased towards the majority class. This study uses a popular evaluation metric “accuracy” and an evaluation metric that is suitable for imbalanced data “balanced accuracy (BA)” to gain information from the confusion matrix regarding accuracy and BA values in case of ovarian cancer classification.Methods: This study use Classification and Regression Tree (CART) as the classifier. CART optimized by pruning. CART optimal is determined from the results of CART complexity analysis and confusion matrix.Results: The confusion matrix and CART interpretations in this research show that CART with low complexity is still able to predict majority class respondents well. However, when none of the data in the minority class was classified correctly, the accuracy value was still quite high, namely 86.97% and 88.03% respectively at the training and testing stages, while the BA value at both stages was only 50%.Novelty: It is very important to ensure that the evaluation metrics used match the characteristics of the data being processed. This research illustrate the difference between accuracy and BA. It concluded that that classification of an imbalanced dataset without doing resampling can use BA as evaluation metric, because based on the results, BA is more fairly to both classes.


YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Abstract: Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.