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All Journal IJOG : Indonesian Journal on Geoscience Civil Engineering Journal AMPLITUDO: Journal of Science & Technology Innovation
Musta, Baba
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Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences

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Journal : amplitudo journal of science

 1 
A Hybrid Ensemble Framework for Probabilistic Earthquake Forecasting in Northern California in Support of SDG 11: Sustainable and Resilient Cities Madlazim, Madlazim; Musta, Baba; Doyan, Aris; Susilo, Adi; Rehman, Khaista
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.496

Abstract

Forecasting earthquakes is still one of the most difficult problems in geophysics, mainly because seismic activity is irregular and often influenced by many factors that interact in complex ways. In this study, we develop a leakage-controlled hybrid ensemble model that combines CatBoost, LightGBM, XGBoost, and Gradient Boosting to predict five earthquake parameters: magnitude, depth, latitude, longitude, and a scaled inter-event interval in Northern California. These models were trained using USGS earthquake data ranging from 1900 to 2025 (M ≥ 4.0), with a process designed to prevent time leakage through strict time separation, a moving window feature, and prospective validation. Overall, the hybrid models produced consistently low MAE and RMSE values ​​and very high R² values ​​(above 0.99) for all target variables. While the estimates performed impressively, the results should be interpreted in a probabilistic context, with recognition of the inherent uncertainty of seismic processes. The framework proposed here provides a clear and replicable approach that can support the development of systems for more reliable short-term earthquake forecasting
Outlier Identification Techniques in Daily Rainfall Data Sudirman, Sudirman; Irfan, Muhammad; Supari, Supari; Musta, Baba; Dzakiya, Nurul
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.554

Abstract

A quality test was conducted on daily rainfall data in the Sumatra region to select good data. The data used came from 19 observation stations belonging to the Meteorology, Climatology, and Geophysics Agency (BMKG) spread across the Aceh-Lampung provinces from early 1985 to late 2023. The quality test aims to ensure data reliability, consistency, and validity. Daily rainfall data often face issues such as missing data, unrealistic extreme values, and recording discrepancies, which can reduce the accuracy of climate analysis. The quality test examined data completeness and outliers using the interquartile range. The quality test results showed a data completeness level of 93%, thus declaring the data valid. Outliers were identified in small amounts (<1%) for very high rainfall intensity at the Minangkabau meteorological station in West Sumatra (470 mm/day), the Bengkulu climatological station (400 mm/day), the FL Tobing meteorological station in North Sumatra (430 mm/day), the Fatmawati Soekarno meteorological station in Bengkulu (390 mm/day), the West Sumatra climatological station (320 mm/day), the South Sumatra climatological station (230 mm/day), and the Radin Intan II meteorological station in Lampung (265 mm/day). These values ​​were not removed from the analysis because they passed the data quality test and represented meteorologically realistic extreme rainfall events. The results of the evaluation of daily rainfall data in Sumatra during the study were representative and reliable enough to be used in further climatological analysis.
Co-Authors Adi Susilo Agung, P. A. Maha Aris Doyan Dzakiya, Nurul Eko Andi Suryo Idmi, Mohammad Habibiy Madlazim Madlazim Marjon, Carolin Nicole Muhammad Fathur Rouf Hasan Muhammad Irfan Rehman, Khaista Sudirman Sudirman Sundarrajan, Vijay Anand Supari Supari, Supari Sutan Haji, Alexander T.