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All Journal Jurnal Geocelebes Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat
Refrizon Refrizon
Geophysics Study Program, Faculty of Mathematics and Natural Sciences, University of Bengkulu
Religion Agriculture, Biological Sciences & Forestry Arts Earth & Planetary Sciences Education Social Sciences

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Training of Computer-Based Qomariah Month Initial Determination (a Case Study of Determination the Months of Ramadhan, Syawwal, and Dzulhijjah) in Nurul Ikhlas Mosque, Bengkulu City, Bengkulu Province, Indonesia Arif Ismul Hadi; Refrizon Refrizon; Budi Harlianto; Darmawan Ikhlas Fadli; Isfardian Agustin Awaliyah; Nur Husniatin Kamila
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2023): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/abdimas.v6i2.3297

Abstract

The determination of the initial of the Qomariah month is still a difference, especially in determining the initial of Ramadhan, Syawwal, and Dzulhijjah. This can happen because there are differences in the method of determination. The method is in the form of Hisab method and Rukyat method. The purpose of community service activities is to determine the initial of the month of Qomariah based on MABIMS criteria (agreement of the Minister of Religion of Brunei Darussalam, Indonesia, Malaysia, and Singapore on 27-29 June 2012 in Bali) and determine the initial of the month using computer software in the form of Accurate Hijri Calculator (AHC) ver. 2.2.1. The method used in this activity is lecture, practice, and discussion. Based on AHC software application ver. 2.2.1 shows that according to calculation 1 Ramadhan falls on Saturday April 2, 2022, but according to MABIMS criteria it falls on Sunday April 3, 2022. This is a possible difference in the start time of fasting in 2022 in Indonesia. However, for 1 Syawwal, both the calculation and the MABIMS criteria fall on the same day, Monday May 2, 2022, both in Indonesia and in other countries.
The Earthquake Prediction in the Southern Part of Sumatra Using Deep Learning (Long Short-Term Memory) Models Ainul Lisa; Refrizon Refrizon; Rida Samdara
JURNAL GEOCELEBES Vol. 10 No. 1: April 2026
Publisher : Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/geocelebes.v10i1.42958

Abstract

The Southern part of Sumatra is highly vulnerable to earthquakes due to its location in the subduction zone between the Indo-Australian plate and the Sunda plate. The Southern part of Sumatra’s vulnerability to earthquakes poses significant risks. This research aims at predicting the Earthquakes in the Southern part of Sumatra Using Deep Learning (Long Short-Term Memory) Models, a deep learning method designed to analyze sequential data. The model utilized 20 years of historical earthquake data from 2004 to 2024, with parameters including magnitude, epicenter location, depth, and event time. Data were preprocessed using Min-Max Scaling normalization and split into training data (70%) and testing data (30%). The model was trained over 150 epochs with a batch size of 32. Evaluation results showed a Mean Absolute Error (MAE) of 0.28 and a Root Mean Squared Error (RMSE) of 0.39, indicating high prediction accuracy. The distribution of prediction results confirmed previous studies indicating that earthquakes in Southern part of Sumatra frequently occur in Bengkulu, western South Sumatra, and Southwestern Lampung. These findings underscore the importance of ongoing seismic hazard mitigation efforts and sustainable development planning in earthquake-prone areas.
Co-Authors Ainul Lisa Arif Ismul Hadi Darmawan Ikhlas Fadli Harlianto, Budi Isfardian Agustin Awaliyah Nur Husniatin Kamila Rida Samdara