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Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

Website security is an important aspect of designing a website and managing web systems. However, many developers still pay little attention to security aspects from the early stages of development. In fact, the website that has been built will be the target of attacks by hackers at any time. Therefore, this research aims to analyze the vulnerability of the SMAN 1 Banjar Agung website based on the OWASP Top 10 standard. The research method was conducted through vulnerability assessment using OWASP ZAP tools with the stages of spidering, passive scanning, and active scanning. This test allows identification of vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), and security configuration weaknesses. The scan results showed eight vulnerabilities, consisting of two medium, three low, and three informational vulnerabilities. Although the risk level is low, the website still requires mitigation through the application of security headers, dependency updates, and removal of sensitive information to make the system more secure and stable.

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.

Web security is an important aspect in maintaining data integrity and confidentiality in the digital age, where cyber threats are increasingly complex and difficult to detect. This research was conducted because there are still many web systems that are vulnerable to attacks due to weak early detection of security gaps. For this reason, this study implements a combination of Nmap and Metasploit Framework as the main tools in proactively detecting and testing system vulnerabilities. The research method was carried out in three stages, namely data collection by scanning the network using Nmap to identify open ports and services, selecting the appropriate testing tools, and controlled exploitation using Metasploit on the Metasploitable2 test system. The results of the study show that Nmap is capable of mapping the attack surface in detail, while Metasploit can validate the scan results through exploitation of vulnerable services such as vsftpd 2.3.4, which successfully provided root access to the target system. The combination of these two tools has proven to be effective in conducting systematic, fast, and accurate early detection of attacks, so that it can be used as a preventive measure to improve web security from potential cyber threats.

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.

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 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.

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.

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.

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.

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.

Aritmia merupakan gangguan irama jantung yang umum terjadi pada lansia dan dapat menimbulkan risiko kesehatan serius jika tidak terdeteksi secara dini. Penelitian yang dilakukan bertujuan untuk mengidentifikasi aritmia pada lansia menggunakan algortima K- Nearest Neighbor (KNN) berdasarkan data elektrokardiogram (EKG). Data yang digunakan berjumlah 105 data EKG lansia yang diperoleh dalam format CSV. Proses awal melibatkan pembersihan dan normalisasi data menggunakan metode StandardScaler, serta pelabelan awal menggunakan algoritma K-Means Clustering untuk mengelompokkan data ke dalam dua kelas: Normal dan Sangat Berpotensi Aritmia. Data kemudian dibagi menjadi 70% data latih dan 30% data uji dengan metode stratified split untuk menjaga proporsi label. Model KNN dilatih dengan parameter k = 3, dan dievaluasi menggunakan confusion matrix serta classification report. Hasil pengujian menunjukkan akurasi model sebesar 97% dengan nilai precision dan recall yang tinggi pada kedua kelas. Hasil ini menunjukkan bahwa algoritma KNN efektif dalam mengklasifikasikan kondisi aritmia pada lansia dan memiliki potensi untuk diterapkan dalam sistem pendukung diagnosis berbasis data EKG.

CV KIA is a shop engaged in the field of computer sales and services, coming directly to make transactions and provide computer service information services. So there are obstacles that are quite time consuming and transportation costs for customers to find out information on goods, stock of goods, and the process of purchasing goods because customers have to come directly to the store. Product information on CV Kia cannot be updated in real time so that there is a delay in calculating stock of goods. The method used in this study is the prototype development method and is designed using UML. This system uses two programming languages, namely PHP. Implementation using the Xampp application, and MySQL. The results of this study are the design and creation of a web-based sales information system. The system that is built will also later facilitate the sales transaction process which can later reduce the level of competition with the outside market. This system displays information about product sales, and can carry out the sales transaction process so that customers do not need to come to the store to get information and make product purchases. The results of testing that has been carried out involving 10 Respondents that the conclusion of the quality of the feasibility of the software produced has a percentage of success with an average total of 100%.  Keywords: Sales, Prototype, PHP, Information Systems, Sublime Text

Meningkatnya ketergantungan pada sistem komunikasi digital telah memperkuat kebutuhan akan metode yang andal untuk melindungi data sensitif dari akses tidak sah. Studi ini memperkenalkan mekanisme keamanan terintegrasi yang menggabungkan enkripsi ChaCha20 dengan steganografi citra Least Significant Bit (LSB), yang menargetkan perlindungan data berbasis citra digital. ChaCha20, sebuah cipher aliran modern yang dikenal akan kecepatan dan keamanannya, digunakan untuk mengenkripsi pesan teks biasa (plaintext), menghasilkan ciphertext yang sangat aman. Data terenkripsi kemudian disematkan ke dalam citra sampul — khususnya, logo universitas — menggunakan teknik LSB, yang mengubah bit paling tidak signifikan dari nilai piksel untuk menyembunyikan informasi tanpa memengaruhi kualitas citra secara signifikan. Pendekatan dua lapis ini memastikan kerahasiaan dan penyembunyian informasi sensitif. Sistem ini dievaluasi menggunakan metrik objektif seperti Rasio Sinyal terhadap Derau Puncak (PSNR) dan Indeks Kesamaan Struktural (SSIM) untuk menilai fidelitas citra setelah penyisipan data. Hasil menunjukkan bahwa metode ini mempertahankan integritas visual (PSNR > 50 dB) sekaligus memungkinkan ekstraksi data yang akurat. Integrasi ChaCha20 dan steganografi LSB menawarkan solusi yang ringan, aman, dan efektif untuk perlindungan informasi digital, khususnya cocok untuk komunikasi akademis atau kelembagaan di mana gambar logo berfungsi sebagai pembawa konten terenkripsi yang tersembunyi.

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.

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.

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

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%.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms