This study aimed to design and develop a website-based *Desa Bersinar* Information System and to evaluate its functionality and usability. The research employed the Rapid Application Development (RAD) methodology, comprising requirements planning, user design, construction, and cutover phases. Data were gathered through observation, interviews, and literature reviews. The system was developed using the Laravel framework and a MySQL database. Functionality was tested using Black Box Testing, while usability was assessed via a Likert-scale questionnaire administered to 10 respondents. Test results indicated that all tested features—including login, reporting, village data, articles, and the gallery—functioned according to the established scenarios. Usability testing yielded a score of 341 out of a maximum of 500 (68.2%), placing it in the "good" category. These results demonstrate that the system is user-friendly and capable of supporting the dissemination of P4GN (Drug Abuse Prevention, Eradication, and Handling) information, as well as the centralized management of data, documentation, and community reports. Consequently, the developed information system can serve as a tool to support the digitalization of *Desa Bersinar* Program management at BNN Trenggalek Regency
Dion Maulana Fahrezi, Taufiq Agung Cahyono, Agung Prasetya· Horizon· 0 citations
User reviews on digital platforms are an important source of information for understanding perceptions of game products, but the large amount of data makes manual analysis less effective. This study aims to analyze user sentiment towards the Minecraft game based on Google Play Store reviews and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) methods. The study used a quantitative approach with 1,600 scraped reviews, which after preprocessing became 1,477 data. The research stages included text cleaning, sentiment labeling, TF-IDF feature extraction, data division using a stratified split, classification model development, and evaluation using accuracy, precision, recall, F1-score, and the McNemar test. The results showed that 70.3% of reviews had positive sentiment, 23.3% were negative, and 6.4% were neutral. The SVM model produced better performance with an accuracy of 82.09% and an F1-score of 0.79, compared to Naïve Bayes with an accuracy of 75.62% and an F1-score of 0.6788. However, the McNemar test results showed no significant difference in performance between the two models (p-value = 0.3074). This finding suggests that the SVM is superior in predictive performance, but both methods have relatively equivalent classification capabilities statistically in analyzing Minecraft user review sentiment.
Faiza Syafi', Mohamad Khoirul Ansor, Agung Prasetya· Horizon· 0 citations
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