Formosa Journal of Computer and Information Science

Comparative Study of Machine Learning Models for Sentiment Analysis of Amazon Product Reviews

Tri Noviantoro — 2026-03-30

This research presents a comparative analysis of four popular sentiment classification models: Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Bidirectional Encoder Representations from Transformers (BERT). The models are evaluated using the Amazon Product Reviews dataset based on their ability to classify sentiments into positive or negative categories. The results show that BERT outperforms the other models in accuracy, precision, recall, and F1-score, demonstrating its superior ability to capture complex contextual relationships in text. LSTM performed well, particularly in recalling positive sentiments, but was outperformed by BERT overall. Conversely, Naive Bayes and SVM exhibited lower accuracy and higher false positive rates, highlighting their limitations in handling nuanced, context-dependent text. This study emphasizes the trade-offs between traditional machine learning models and advanced deep learning techniques.

Microservices-Based Open-Source Video Conference Deployment for Optimized Online Learning Infrastructure

Davy Putra Ananda — 2026-03-30

The rapid advancement of information technology has fundamentally shifted the interaction paradigm in education from conventional methods to hybrid learning models. In this context, the availability of stable, real-time communication platforms has become crucial for maintaining the effectiveness of knowledge transfer. This study evaluates the implementation of Apache OpenMeetings v9.0.0 using Docker and WSL2 to provide efficient video conferencing. Using an experimental methodology, system performance was monitored during active sessions. Results show high resource efficiency with a stable CPU utilization of 4.94% and memory usage of 1.339 GiB. The system achieved a rapid startup velocity of 11.1 seconds, proving that containerization offers optimal isolation with minimal overhead. The study concludes that this architecture provides a lightweight, portable, and cost-effective solution for independent communication infrastructure in educational institutions.

The Application of Naive Bayes in Analyzing Public Sentiment Toward the Performance of the North Sumatra Regional Government in Handling Flash Floods

Rivaldo Siburian — 2026-03-30

This study analyzes public sentiment towards the performance of the North Sumatra Regional Government in handling flash floods using the Multinomial Naive Bayes algorithm. A total of 1,132 opinion data points were collected from social media and news portals through web crawling from November 2025 to February 2026. Sentiment labeling was performed using a lexicon-based approach with the InSet dictionary. Classification results showed a dominance of negative sentiment at 88.4%, focusing on slow emergency response. Model evaluation with an 80:20 data split yielded 89.43% accuracy and an F1-Score of 0.844 for Naive Bayes, while SVM achieved the highest F1-Score (0.855). This study concludes that AI-based sentiment analysis can serve as an objective instrument for government performance auditing.

Effects of Scratch Gamification with MDA on Students’ Engagement and Learning Outcomes

Agustinus Sembiring — 2026-03-30

This study aims to examine the effect of Scratch-based gamification using the MDA (Mechanics–Dynamics–Aesthetics) model on students’ engagement and learning outcomes in primary education. A quasi-experimental method with a pretest–posttest design was applied to 115 students. Data were collected through tests and questionnaires and analyzed using paired sample t-tests and descriptive analysis. The results showed a significant improvement in learning outcomes, with a mean pretest score of 56.84 and posttest score of 71.03 (t = -26.57; p < 0.001). In addition, students’ engagement was categorized as high (mean = 3.73). These findings indicate that Scratch-based gamification integrated with the MDA model is effective in improving learning quality.