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Business is an economic activity carried out by individuals or organizations to produce and sell goods or services with the aim of making a profit. The NSH Group Store is a business that sells carpets, pillows, bolsters, and dolls located in the Sei Dadap I/II Plantation, Sei Dadap District, Asahan Regency, North Sumatra 21225. The NSH Group Store was established in 2016 and is owned by Mrs. Siti Komariah Siregar. Among the challenges faced by the NSH Group Store owner are irregular stock procurement. Sales transaction processes still use conventional methods, reducing efficiency and time effectiveness, and potentially leading to data errors. Supply Chain Management is a series of approaches used to efficiently integrate suppliers so that goods can be distributed in the right quantities, locations, and at the right time, with the aim of minimizing overall system costs. A bolster pillow is a pillow that can function as both a pillow and a bolster. Bolster pillows are oval and long, so they can be hugged while sleeping. The benefits of a bolster pillow include maintaining a proper sleeping position, reducing pressure on joints, helping reduce aches, improving sleep quality, and improving overall health. Therefore, by implementing Supply Chain Management (SCM), data processing will be faster and more accurate.

This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

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.

Selecting the right supplier is a critical aspect of supply chain management, especially in a retail business like Parfume Corner, which relies on product quality, availability, and on-time delivery. This study aims to implement the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method as a multi-criteria decision-making approach to determine the best perfume supplier. The VIKOR method was chosen because of its ability to handle conflicts between criteria and produce optimal compromise solutions. The evaluation criteria used include product quality, price, on-time delivery, after-sales service, and flexibility in negotiations. Data were collected from five potential suppliers through observation, interviews, and historical transaction documents. The analysis results showed that one supplier obtained the lowest VIKOR index score, thus being determined as the best compromise solution. The implementation of the VIKOR method proved effective in providing objective and transparent recommendations, which can support Parfume Corner's strategic decisions in building long-term partnerships with reliable suppliers. This approach can also be adapted by similar businesses to improve procurement efficiency and quality. The test results obtained were that in the expert test a Good value was obtained, namely 80%, while in the system test a Very Good conclusion was obtained, namely 100%.

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.

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.

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.

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.

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. 

This research examines the application of the Elliptic Curve Digital Signature Algorithm (ECDSA) in a blockchain system as a security solution for digital payment systems in Indonesia. Using a descriptive-qualitative approach based on literature review and conceptual simulations using Python, this study discusses the working principles of ECDSA, its advantages over other digital signature algorithms, and the challenges of its adoption in Indonesia. The results show that ECDSA provides high cryptographic efficiency, maintains transaction authenticity and integrity, and supports a transparent decentralized system. The academic simulations include not only KYC processes, top-ups, transactions, validation by validators, and block recording, but also demonstrates the formation of an interconnected multi-level blockchain and tests scenarios for rejecting manipulated or invalid transactions. The contribution of this research lies not only in the theoretical review but also in the implementation illustrations that can be used as a basis for education and the initial development of blockchain-based digital payment systems. The research results show that ECDSA is capable of providing a high level of efficiency in the encryption and transaction verification process, maintaining data integrity and authenticity, and supporting a decentralized and transparent system. The academic simulations included the KYC process, wallet creation using ECDSA keys, balance top-ups through bank integration, transaction creation and validation, and block recording in the blockchain. Specifically, the simulations successfully demonstrated how new blocks are added to the chain by referencing the previous block's hash, as well as how transactions with corrupted signatures, manipulated amounts, or insufficient balances are automatically rejected by the validator consensus mechanism, maintaining system integrity. This research contributes not only theoretically, but also through conceptual representations that can be used as an educational foundation and for the initial development of blockchain-based digital payment systems in Indonesia.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

Social media has become a strategic tool for the government to disseminate public information quickly, interactively, and efficiently in the digital era. The Lampung Provincial Government utilizes various social media platforms such as Facebook, Instagram, and TikTok to support public communication activities. This study aims to analyze the effectiveness of public communication by measuring the level of activity of Regional Apparatus Organization (OPD) social media accounts using the logistic regression method. Data were collected through web scraping techniques on the official OPD social media accounts and then processed using a quantitative approach. The results show that the level of social media activity influences the effectiveness of public communication and the transparency of government information. These findings are expected to serve as a basis for local governments in designing public communication strategies that are more optimal and adaptive to developments in digital technology.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.