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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Engineering, Mathematics and Computer Science Journal (EMACS)
Purwandari, Kartika
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Comparison of IndoBERT and SVM Algorithm to Perform Aspect Based Sentiment Analysis using Hierarchical Dirichlet Process Octarini, Sheila Prima; Zakiyyah, Alfi Yusrotis; Purwandari, Kartika
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13493

Abstract

Analyzing the performance of SVM and IndoBERT models for aspect-based sentiment analysis on fashion reviews in Tokopedia E-Commerce. This study employs the SMOTE technique due to the imbalance in the original data. Aspect determination using the Hierarchical Dirichlet Process (HDP) model yields satisfactory results with an adequate coherence score. The comparison between SVM and IndoBERT methods for aspect-based sentiment analysis shows that SVM is superior. IndoBERT achieved an accuracy of 87%, precision of 91%, recall of 93%, and F1-Score of 92%, while SVM attained an accuracy of 96%, precision of 100%, recall of 92%, and F1- Score of 96%. Therefore, the SVM model was chosen for implementation on a website that allows users to view aspect-based sentiment analysis on products in E-Commerce. The HDP model effectively grouped related terms into aspects such as “Material,” “Shipping,” and “Colour,” enhancing interpretability in sentiment classification. The resulting website enables users to analyze product sentiments interactively, providing actionable insights for both sellers and customers to assess product quality and service satisfaction more efficiently.
Web-Based Quality Control Dashboard Design for Data Validation and Monitoring: A Case Study of BMKG Instruments Purwandari, Kartika; Aufauzan, Brian Tirafi; Sigalingging, Join Wan Chanlyn
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.13532

Abstract

Accurate meteorological data are vital for the operational activities of the Agency for Meteorology, Climatology, and Geophysics (BMKG), specifically for weather forecasting and disaster mitigation. However, Automatic Weather Station (AWS) instruments frequently encounter sensor degradation and technical malfunctions, which compromise data validity. Traditional manual validation is inefficient and prone to human error. This study addresses these gaps by designing a web-based Quality Control (QC) dashboard for real-time AWS data monitoring. Developed using the Laravel framework and PostgreSQL, the system integrates Leaflet.js and Chart.js for interactive spatial and analytical visualization. Using the Agile Scrum methodology, the development process was iteratively refined across eight sprints. Implementation results show a significant improvement in data validation accuracy and a reduction in potential human error. User Acceptance Testing (UAT) with fifteen BMKG specialists confirms high usability, with the system receiving "Strongly Agree" ratings for its efficiency in real-time monitoring and reporting. The practical implications include enhanced data credibility for national climate modeling. This paper concludes that while the dashboard streamlines workflows, future iterations should incorporate automated anomaly detection algorithms. Limitations include a current reliance on static validation thresholds, suggesting a need for machine learning integration in future research.
Co-Authors Ajeng Wulandari Aufauzan, Brian Tirafi Join wan chanlyn Sigalingging Kurniadi, Felix Indra Octarini, Sheila Prima Syarifah Diana Permai Zakiyyah, Alfi Yusrotis