cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sri Ngudi Wahyuni
Contact Email
yuni@amikom.ac.id
Phone
+6282138594141
Journal Mail Official
jurnalbhatara@gmail.com
Editorial Address
Janti Buana Asri 4 No. B7, Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta, Indonesia, Post Code 55198.
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
JURNAL MULTIDISIPLIN BHATARA
Published by Hemispheres Press
ISSN : -     EISSN : 30471192     DOI : https://doi.org/10.59095/jmb.v2i2
Core Subject : Religion, Humanities, Health, Science, Education, Agriculture, Art, Engineering,
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
BHATARA focuses on empirical research and critical analysis of the development, use, management and impact of science globally, such as: Education, Information Technology, Management, Economy, Technique, Culture, Law, Tourism and Other fields of science are possible.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "Vol 2 No 3 (2025): October (In progress issue)" : 2 Documents clear
Integrasi Augmentasi Data dan Machine Learning dalam Prediksi Magnitudo Gempa Bumi: Analisis dengan Random Forest Regressor dan Visualisasi Geospasial Utama, Hastari; Masruro, Ahlihi; Indriyatmoko, Toto; Sudarmanto, Sudarmanto
BHATARA: Jurnal Multidisiplin Ilmu Vol 2 No 3 (2025): October (In progress issue)
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.
Pemodelan Prediktif Konsumsi Energi Listrik di Pabrik Baja Berbasis XGBoost untuk Pengelolaan Sumber Daya Berkelanjutan Utama, Hastari; Santoso, Joko Dwi
BHATARA: Jurnal Multidisiplin Ilmu Vol 2 No 3 (2025): October (In progress issue)
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/jmb.v2i3.234

Abstract

This research aims to develop a predictive model for electrical energy consumption in steel plants based on XGBoost Regressor, with the goal of supporting sustainable resource management. The dataset used was obtained from the UCI Machine Learning Repository with DOI: 10.24432/C52G8C , covering real-time operational data from a steel plant during 2018, including variables such as energy consumption (Usage_kWh), reactive power, power factor, and production load status. The research process included data exploration, preprocessing, time and categorical feature extraction, and training the XGBoost Regressor model with hyperparameter optimization using Grid Search and time-series split validation. Evaluation results showed outstanding performance with an MAE of only 0.41 kWh, RMSE of 0.81 kWh, and an R² value of 0.9993, indicating that the model successfully explained nearly all variations in actual data. Feature importance analysis revealed that Lagging_Current_Reactive.Power_kVarh and CO2(tCO2) were the most influential features in predicting energy consumption. This model is not only technically accurate but also holds significant practical potential for use in industrial energy management systems, helping companies plan production schedules, avoid peak loads, and improve energy efficiency sustainably.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 2 No 3 (2025): October (In progress issue) Vol 2 No 2 (2025): July Vol 2 No 1 (2025): January Vol 1 No 3 (2024): July Vol 1 No 2 (2024): April Vol 1 No 1 (2024): Januari