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All Journal Jurnal Ilmu Komputer dan Desain Komunikasi Visual Bulletin of Computer Science Research
Prasetio, Mugi
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Arts Computer Science & IT Education Languange, Linguistic, Communication & Media

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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.
Integrasi Konteks Semantik dan Waktu Akses dalam Algoritma Caching Adaptif untuk Optimalisasi Kinerja Sistem Dewantoro, Mahendra Dewantoro; Dewantoro, Mahendra; Santosa, Budi; Prasetio, Mugi; Amarudin
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 10 No 2 (2025): Jurnal Ilmu Komputer dan Desain Komunikasi Visual
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/a66z4p43

Abstract

 In modern computing systems, caching is an essential technique for improving data access efficiency. However, traditional caching algorithms such as LRU and LFU have limitations in handling complex data dynamics. This research proposes an adaptive caching algorithm that integrates semantic context and access time profiles to enhance system performance. The approach utilizes a semantic embedding model based on Sentence-BERT and temporal analysis of user access patterns. Testing was conducted through simulations using real and synthetic datasets, and compared with conventional caching methods such as KVShare and LRU. Evaluation results show that the proposed algorithm is capable of increasing the cache hit rate by more than 83%, reducing average latency to around 61 ms, and maintaining resource usage efficiency. In addition, the algorithm demonstrates strong adaptability to dynamic access pattern changes and responsiveness to semantic parameter adjustments. Thus, the integration of semantic context and temporal information provides significant contributions to optimizing cache management. This algorithm has potential applications in edge computing systems, LLM services, and cloud-based platforms. Suggestions for future research include implementation in real-world environments, application of predictive machine learning models, and dynamic exploration of adaptive parameters.  
Co-Authors Amarudin Budi Santosa Budi Santosa Dewantoro, Mahendra Dewantoro, Mahendra Dewantoro Heni Sulistiani Inonu, Onassis Yusuf Magda, Kardita