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All Journal Bulletin of Computer Science Research SENTRI: Jurnal Riset Ilmiah
Magda, Kardita
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Aerospace Engineering Humanities Computer Science & IT Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice

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Klasifikasi Tingkat Risiko Gempa di Indonesia Menggunakan Pola Spasial dan Temporal Berbasis Decision Tree Prasetio, Mugi; Sulistiani, Heni; Inonu, Onassis Yusuf; Magda, Kardita; Santosa, Budi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.624

Abstract

Indonesia is an area that is very vulnerable to earthquakes due to its location in the meeting zone of active tectonic plates. This study aims to classify the level of earthquake risk based on spatial and temporal patterns using the Decision Tree method as a solution in predicting potential earthquake hazards. The data used is earthquake data in Indonesia from 2015 to 2023 obtained from public datasets, including location information (latitude and longitude), event time (year and month), and earthquake magnitude. Earthquakes are categorized into three risk classes: Low (M < 4.0), Medium (4.0 ? M < 6.0), and High (M ? 6.0). The Decision Tree model was successfully built with an average accuracy of 88% on the test data. The results show that earthquakes mostly occur in active subduction zones such as the Sunda Subduction Zone (Sumatra and Java), Banda Arc (Nusa Tenggara, Maluku, Seram), Sulawesi, and Papua. Temporal analysis also shows fluctuations in the number of earthquakes by year and season, with increased activity in certain months. The spatial visualization reinforces the finding that the eastern region of Indonesia is more seismically active than the western region. This research proves that machine learning approaches can be used to support earthquake disaster mitigation through historical data-based risk identification.
Analisis Komparatif Sentimen Publik terhadap Liputan Media Terkait Aksi Menteri Keuangan Menggunakan Algoritma SVM dan RoBERTa Santosa, Budi; Magda, Kardita; Budiman, Ega; Suryono, Ryan Randy
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 2 (2026): SENTRI : Jurnal Riset Ilmiah, Februari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i2.5858

Abstract

Public opinion on social media is a crucial representation of government policy legitimacy, especially in the fiscal sector. This study intends to provide a comparative investigation of the efficacy of sentiment categorization on YouTube comments pertaining to the activities of the Indonesian Finance Minister by juxtaposing the Support Vector Machine (SVM) algorithm with the RoBERTa Transformer model. A total of 3,780 comments were acquired from national digital media channels. The research method involves intensive text preprocessing, including stemming using the Sastrawi algorithm and lexicon-based labeling. The results showed that the SVM algorithm with TF-IDF features achieved an accuracy of 83.33% and an F1-score of 76.05%. In contrast, the RoBERTa model showed a significantly lower performance with an accuracy of 29.76%. This study concludes that for datasets dominated by neutral sentiments and informal language in specific Indonesian contexts, traditional machine learning like SVM with optimal feature engineering remains more reliable and efficient than complex Transformer models that require more extensive fine-tuning.
Co-Authors Budi Santosa Budi Santosa Budiman, Ega Heni Sulistiani Inonu, Onassis Yusuf Prasetio, Mugi Ryan Randy Suryono