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All Journal The Indonesian Journal of Computer Science Research JURNAL MULTIDISIPLIN BHATARA
Sudarmanto Sudarmanto
Universitas Teknologi Digital Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing

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Pengujian Implementasi Disaster Recovery Menggunakan Ansible Miftahul Ulum; Muhammad Agung Nugroho; Edi Iskandar; Sudarmanto Sudarmanto
The Indonesian Journal of Computer Science Research Vol. 2 No. 1 (2023): January
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i1.18

Abstract

Cloud-based technology is a technology that constructs the internet as a central server for managing data and also user applications. Because the smooth process of processing data in a company/organization depends on IT infrastructure, data protection and recovery become essential, especially in damage or natural disasters. Files or data stored and processed by a company/organization will increase in size so that centralized and organized data storage is needed, namely by managing data in a data centre. The data backed up will be stored in a replica of the main data centre; the replica can act as an alternative if the primary data centre is undergoing maintenance or maintenance (down). Consequently, we need a system that can overcome obstacles when the data centre is down and requires a new data centre that is fast and reliable to handle the backup with ansible
Pengujian Implementasi Disaster Recovery Menggunakan Ansible Miftahul Ulum; Muhammad Agung Nugroho; Edi Iskandar; Sudarmanto Sudarmanto
The Indonesian Journal of Computer Science Research Vol. 2 No. 1 (2023): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i1.18

Abstract

Cloud-based technology is a technology that constructs the internet as a central server for managing data and also user applications. Because the smooth process of processing data in a company/organization depends on IT infrastructure, data protection and recovery become essential, especially in damage or natural disasters. Files or data stored and processed by a company/organization will increase in size so that centralized and organized data storage is needed, namely by managing data in a data centre. The data backed up will be stored in a replica of the main data centre; the replica can act as an alternative if the primary data centre is undergoing maintenance or maintenance (down). Consequently, we need a system that can overcome obstacles when the data centre is down and requires a new data centre that is fast and reliable to handle the backup with ansible
Integrasi Augmentasi Data dan Machine Learning dalam Prediksi Magnitudo Gempa Bumi: Analisis dengan Random Forest Regressor dan Visualisasi Geospasial Hastari Utama; Ahlihi Masruro; Toto Indriyatmoko; Sudarmanto Sudarmanto
BHATARA: Jurnal Multidisiplin Ilmu Vol 2 No 3 (2025): October
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/jmb.v2i3.233

Abstract

This research aims to enhance the accuracy of earthquake magnitude prediction through an integration of data augmentation techniques and machine learning based on the Random Forest Regressor, supported by geospatial visualization for in-depth analysis. The dataset used originates from the USGS (United States Geological Survey) in CSV format, encompassing over a thousand global earthquake events within one month, with seismic parameters such as location (latitude, longitude), depth, magnitude, and recording quality. In the context of imbalanced data—dominated by small earthquakes and rare large ones—a data augmentation technique based on noise injection into spatial features (latitude, longitude) and depth was applied, resulting in a dataset five times larger than the original. Evaluation results demonstrate significant improvement in model performance: MAE decreased from 0.2467 to 0.1046 (a 57.6% reduction), RMSE dropped from 0.3499 to 0.1868 (a 46.6% decrease), MSE reduced from 0.1225 to 0.0349 (a 71.5% reduction), and R² increased from 0.9493 to 0.9817. These improvements confirm that data augmentation not only reduces overfitting but also strengthens the model’s ability to predict large-magnitude earthquakes—classes most critical for disaster mitigation. Geospatial visualization displays the spatiotemporal distribution of earthquakes, identifying active seismic hotspots in regions such as the Pacific Ring of Fire, California, Alaska, and Indonesia. This research proves that data augmentation is not merely a supplementary technique but a crucial strategy to enhance model generalization and predictive performance, particularly for rare yet high-impact seismic events. The findings offer significant scientific and practical contributions to seismic hazard mitigation and risk mapping, with potential applications in early warning systems and real-time disaster response.
Co-Authors Ahlihi Masruro Edi Iskandar Miftahul ulum Nugroho, Muhammad Agung Toto Indriyatmoko Utama, Hastari