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As a vocational education institution, SMK Swadhipa 1 Natar is required to provide adequate facilities to support the development of its students' technical and practical skills. Although some facilities are already available, student complaints remain regarding the condition, availability, and utilization of these services, particularly those related to information technology.This study aims to analyze the level of student satisfaction with information technology services at SMK Swadhipa 1 Natar using a combination of Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The study was conducted through a quantitative approach by distributing questionnaires to 100 respondents selected using stratified random sampling techniques. The data collected were analyzed to determine the overall satisfaction score and identify factors of information technology services that were a priority for improvement. The results of the CSI analysis showed that the level of student satisfaction with school information technology services was in the good category, with an average score of 82%. Furthermore, the results of the IPA analysis revealed that information technology services such as computer services in the school lab, wifi networks, and school websites consisting of school exam applications, student registration applications and information about the school on the website were in the top priority quadrant because they had a high level of importance but their performance was still low. Based on these results, it can be concluded that although in general students stated that they were quite satisfied with the information technology services available, there were several important aspects, especially technology-based information technology services, that needed more attention from the school. Thus, recommendations for improving technological infrastructure and periodic evaluation of educational information technology services can help SMK Swadhipa 1 Natar in improving the quality of educational services and student satisfaction. 

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

Current advances in information technology have encouraged universities to utilize student academic data as a basis for decision-making, one of which is predicting academic achievement. This study aims to apply the C4.5 algorithm to develop a system for predicting student academic success in the Islamic Religious Education Study Program. This method was chosen because it produces a decision tree model that is easy to understand and has a high level of accuracy. The data used comes from student achievement indexes from semesters 1 to 5. The research results showed that the prediction system achieved 99.62% accuracy and achieved high recall precision across each class category. This demonstrates the effectiveness of the C4.5 algorithm in predicting student academic achievement and has the potential to serve as a valuable tool for decision-makers in higher education.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

The rapid development of information technology in today's digital era has significantly impacted organizational performance, particularly in data management and resource planning. One organization that heavily relies on accurate data availability is the Indonesian Red Cross (PMI), especially its Blood Donor Unit (UDD). UDD PMI of Asahan Regency faces challenges in determining monthly blood donor targets to maintain stable blood stock. A shortage of blood supply can be fatal for patients requiring transfusions. Therefore, a system is needed to forecast the number of blood donors, allowing for more accurate decision-making. This study utilizes the Weighted Moving Average (WMA) method to predict the number of blood donors for the following month based on historical data from March 2024 to March 2025. The WMA method is chosen for its ability to assign greater weight to recent data, making the forecast more relevant and accurate. The results of this research are expected to assist UDD PMI Asahan Regency in anticipating blood needs and maintaining optimal stock availability.

This study aims to investigate the factors influencing the continuance intention of Al-Kautsar Senior High School students in using metaverse-based learning media. The background of this research lies in the rapid adoption of immersive technologies in education, while students’ levels of acceptance have not yet been fully understood. The objective is to identify the antecedents of satisfaction, which subsequently influence continuous intention. The research model examines the effects of perceived interactivity, perceived sociability, perceived enjoyment, perceived ease of use, perceived security, and social influence on satisfaction. A quantitative approach was employed by distributing questionnaires to students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that satisfaction is a very strong and statistically significant predictor of continuous intention to use metaverse applications (β = 0.716, p < 0.001). The six hypothesized antecedent variables were not found to have a significant individual effect on satisfaction. In conclusion, for digital native students at Al-Kautsar Senior High School, factors such as ease of use, interactivity, and enjoyment have shifted from being drivers of satisfaction to becoming basic expectations (hygiene factors). Satisfaction itself emerges as the primary determinant, likely influenced by more substantive elements such as content quality or pedagogical design rather than merely the technical features of the platform.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Penelitian ini mengevaluasi performa tiga algoritma deteksi komunitas Louvain, Infomap, dan Walktrap dalam konteks social network analysis pada jaringan undang-undang Republik Indonesia periode 2014–2024. Jaringan dibangun dari hubungan kutipan antar undang-undang Republik Indonesia pada rentang waktu antara tahun 2014 sampai dengan tahun 2024. Kutipan antar undang-undang diperoleh pada bagian “Mengingat” pada setiap undang-undang, menghasilkan sebuah konstruksi struktur graf berarah dan tak berbobot. Setiap algoritma diuji berdasarkan empat metrik evaluasi: modularity, coverage, conductance, dan inter-cluster density. Evaluasi terhadap tiga algoritma deteksi komunitas Infomap, Louvain, dan Walktrap pada jaringan undang-undang menunjukkan perbedaan karakteristik dalam membentuk struktur komunitas. Louvain unggul dalam hal modularity (0.522387) dan conductance (0.287157), yang mencerminkan kemampuan optimal dalam memisahkan komunitas besar yang kohesif dan minim koneksi keluar. Infomap menempati posisi menengah dengan modularity dan inter-cluster density yang cukup baik, menawarkan keseimbangan antara segmentasi dan kepadatan komunitas. Walktrap memiliki keunggulan pada coverage (0.809586) dan inter-cluster density (0.50640), menandakan kemampuannya membentuk komunitas kecil yang sangat padat secara internal, meskipun cenderung kurang terstruktur secara global karena modularity-nya paling rendah (0.464787). Dengan demikian, Louvain direkomendasikan sebagai algoritma paling sesuai untuk analisis jaringan undang-undang, terutama jika tujuan utama adalah memperoleh segmentasi komunitas yang terstruktur kuat dan representatif secara makro terhadap arsitektur hukum nasional.

Persaingan industri distribusi yang semakin ketat menuntut perusahaan untuk memiliki sistem manajemen persediaan yang efisien dan terintegrasi. PT. Nindy Glow Beauty Aesthetic, sebuah klinik kecantikan yang bergerak di bidang penjualan produk skincare di Sei Piring, saat ini masih menggunakan nota pembelian manual sebagai acuan informasi persediaan barang. Hal ini mengakibatkan data stok tidak akurat dan menghambat pengambilan keputusan. Penelitian ini bertujuan untuk mengembangkan sistem informasi persediaan barang berbasis metode Supply Chain Management (SCM) yang dapat membantu perusahaan dalam merencanakan kebutuhan stok berdasarkan data penjualan, permintaan, dan ketersediaan barang. Hasil dari pengembangan sistem ini diharapkan dapat meningkatkan efisiensi pengelolaan persediaan, mengurangi kerugian akibat kelebihan atau kekurangan stok, serta mendukung proses distribusi produk skincare secara optimal. Studi ini juga mengacu pada penelitian sebelumnya yang menunjukkan keberhasilan penerapan metode SCM di berbagai sektor industri

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

This study conducts a Systematic Literature Review (SLR) to explore the impact of users’ mental models on the implementation of Multi-Factor Authentication (MFA) as a strategy for mitigating password guessing risks in organizational environments. Amid the growing landscape of cyber threats, single-factor authentication has proven to be vulnerable, making MFA an essential layered security solution. However, the adoption of MFA remains slow. Existing studies indicate that expert users perceive MFA as a useful additional layer of verification, whereas non-expert users often view it as a burdensome task (a chore) and may even struggle to distinguish between different types of MFA. Dependence on mobile devices emerges as a common source of frustration for both groups. These findings emphasize that understanding users’ mental models is crucial for improving the implementation and usability of MFA. Innovations such as adaptive MFA or Single Input Multi-Factor Authentication (SIMFA) show potential as solutions to balance security requirements and user experience.

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Penelitian ini bertujuan untuk mengimplementasikan sistem Supply Chain Management (SCM) dalam pengelolaan persediaan sembako di Toko Grosir Rohman guna meningkatkan efisiensi operasional dan mengatasi permasalahan kehabisan atau kelebihan stok. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan studi kasus, melalui observasi, wawancara, dan dokumentasi. Sistem SCM yang dirancang berbasis website menggunakan PHP dan MySQL, dengan pemodelan UML untuk menganalisis kebutuhan dan perancangan sistem. Hasil dari implementasi sistem menunjukkan peningkatan akurasi data stok, efisiensi dalam proses pemesanan, serta tersedianya informasi real-time yang membantu pengambilan keputusan. Dengan sistem ini, Toko Grosir Rohman mampu mengelola persediaan sembako secara lebih optimal, meningkatkan kepuasan pelanggan, dan memperkuat daya saing usaha.

Acne vulgaris (AV) is a common dermatological condition affecting adolescents and young adults, with multifactorial causes including hormonal influences, increased follicular keratinization, excessive sebum production, and colonization by Cutibacterium acnes. External factors, particularly skincare behavior, are also considered to play an important role. The use of skincare products that are unsuitable for skin type or contain comedogenic ingredients such as mineral oil, lanolin, and certain silicones may clog pores and exacerbate acne. This study aimed to analyze the relationship between skincare product selection and usage behavior, types of skincare products used, and frequency of use with the occurrence of acne vulgaris among female medical students of the Faculty of Medicine, Universitas Sumatera Utara, cohorts 2022–2024. An analytical observational study with a cross-sectional design was conducted. Primary data were collected through an online questionnaire, while secondary data were obtained from the student directory. A total of 90 respondents were selected using stratified random sampling. Data analysis was performed using the Chi-square test. The results showed a significant association between skincare product selection and usage behavior and the occurrence of acne vulgaris (p = 0.043), as well as between the type of skincare products used and acne incidence (p < 0.001). However, no significant relationship was found between the frequency of skincare product use and acne vulgaris (p = 0.116). These findings indicate that appropriate product selection and type are more influential in acne development than usage frequency.

Musculoskeletal Disorders (MSDs) complaints in muscles, joints, and soft tissues can be caused by poor posture, repetitive movements, and excessive physical load. In the wet cake-making industry, workers often sit or stand for long periods, bend, and perform monotonous hand movements, which increases the risk of MSDs. The impact includes decreased work comfort, productivity, and quality of life. This study aims to assess the ergonomic risk level in wet cake-making workers and identify the relationship between work activities and MSD complaints. The method used is descriptive quantitative research with a cross-sectional design, involving 45 workers at Kue X Silaberanti. Ergonomic risk was assessed using the REBA method, while MSD complaints were measured using the Nordic Body Map questionnaire. The results show that in the mixing process, 58.8% of workers experienced a very high risk of MSDs. In the molding and baking processes, 52.9% and 68.8% of workers, respectively, were at high risk. The most frequent complaints were pain in the back, neck, shoulders, arms, and wrists due to poor posture and repetitive movements. To reduce the risk of MSDs, workers need to improve their posture, use ergonomic aids, rotate tasks, and undergo training in safe working techniques.

Radiographic examination plays a crucial role in visualizing internal body structures for diagnostic purposes. One of the radiographic assessments frequently performed is the Acromioclavicular (AC) joint projection, which is used to evaluate abnormalities such as joint widening, subluxation, and dislocation. This study aimed to compare the image quality of the AC joint using the Anteroposterior (AP) projection with a 3-kg load and without load. The study was conducted in the Radiology Laboratory of STIKES Borneo Nusantara using a conventional X-ray system with a quantitative descriptive case-study approach. Data were collected through observation and questionnaires administered to 10 research subjects, including radiographers and patient participants. The findings demonstrated that the AP projection with a 3-kg load produced clearer visualization of the AC joint, particularly in widening of the joint space and separation between the humeral head and glenoid cavity. The average image quality score using load was 3.5 (good), compared with 2.9 (poor) for the projection without load. The study concludes that applying a 3-kg load improves anatomical visualization of the AC joint and is recommended for cooperative patients to enhance diagnostic accuracy.

This community service was carried out at SMAS PGRI 1 Mojokerto City with the main goal of improving student discipline through the application of Japanese-style discipline culture. This activity is designed in a structured manner to have a real and sustainable impact on student behavior and the integrity of educational institutions. The method used is a participatory and applicative approach, so that students not only passively receive information, but also actively engage in the process of learning, reflection, and discipline practice. This approach emphasizes the direct involvement of students in activities that foster awareness, responsibility, and positive habits that support the creation of a conducive learning environment. The results of the implementation of activities are divided into three main categories. First, the short-term results seen immediately after the activity are increased students' motivation and understanding of the importance of discipline. Second, the medium-term results that appear within 1-3 months are in the form of real behavioral changes, such as punctuality, neatness, and compliance with school rules. Third, long-term results that contribute to strengthening the integrity of the institution as a school with a high discipline image. Thus, this community service is expected to be able to become a model for implementing a discipline culture that is effective, sustainable, and relevant to the needs of education in Indonesia.

This community engagement program aims to strengthen the halal value chain of micro, small, and medium enterprises (MSMEs) in Hagu Selatan Village, Banda Sakti District, Lhokseumawe City through knowledge transfer based on the Indonesian halal value chain model. MSMEs play a vital role in regional economic development, yet many still face challenges in understanding halal–thayyiban principles, fulfilling certification requirements, managing production processes, and accessing Islamic financing. Limited adoption of digitalization also reduces their competitiveness in the rapidly growing halal industry. The program applies a participatory approach by involving universities, village authorities, and MSMEs. Key activities include needs assessment, training on halal value chain concepts, technical assistance for halal certification documents, workshops on digital business practices, and business clinics on Islamic financing. A Halal Value Chain Learning Circle is also established to support continuous collaboration and knowledge sharing. The program is expected to enhance MSME capacity, improve product quality, expand market access, and contribute to a sustainable halal ecosystem in Aceh.

Environmental cleanliness is a critical factor in the effective management of nature-based tourist destinations, as it strongly shapes tourists’ perceptions of destination quality, comfort, and overall attractiveness, as well as their intention to visit and revisit. This study aims to examine the extent to which environmental cleanliness influences tourists’ visit intention in the Harau Valley tourism area (Lembah Harau), Kanagarian Tarantang, Harau District, Lima Puluh Kota Regency. The research employed a quantitative survey method by distributing structured questionnaires to 50 tourists during their visit to Lembah Harau. The variables measured include tourists’ perceptions of environmental cleanliness, such as waste management practices, sanitation conditions, and the availability of waste disposal facilities, as well as visit intention indicators, including intention to return and willingness to recommend the destination to others. The findings highlight the importance of maintaining a clean environment to enhance tourists’ positive behavioral intentions. The implications of these findings suggest that destination managers and local government authorities should consistently enforce cleanliness policies, strengthen integrated waste management systems, and provide adequate sanitation facilities to enhance the attractiveness of Lembah Harau and support sustainable tourism development, without neglecting the need for continuous monitoring, evaluation, and improvement in these areas.