Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer BULETIN FISIKA
Huriyatul Firdausi
Universitas Sriwijaya
Astronomy Computer Science & IT Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Machine Learning to Predict Climate Change in Coastal Areas of Indonesia Huriyatul Firdausi; Melly Ariska; Sardianto Marcos Siahaan; Hamdi Akhsan; Yenny Anwar; Iin Seprina; Taufiq Taufiq
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p05

Abstract

Indonesia's coastal regions face significant threats from climate change, including rainfall uncertainty, rising temperatures, and sea level rise. This study aims to explore the potential of machine learning algorithms in predicting climate parameter changes in the coastal areas of Minangkabau, Pesawaran, and Maritim Panjang. Daily climatological data obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) were used as the basis for model training. Three primary algorithms were tested Random Forest, XGBoost, and Long Short-Term Memory (LSTM) selected for their capability to handle complex and temporal data. The research methodology included data preprocessing, model training, cross-validation, and predictive performance evaluation using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Preliminary results show that LSTM excels in time series prediction, while XGBoost offers a good balance between speed and accuracy. These findings indicate that machine learning-based approaches have strong potential as decision-support tools for climate change mitigation and adaptation planning in Indonesia’s coastal regions.
Implementasi Deep Reinforcement Learning untuk Optimasi Energi Sistem Panel Surya pada Penghitung Ikan Otomatis Jupri Suyuti Ramadagandi; Azizah Azahara; Huriyatul Firdausi
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 4 No. 5 (2026): Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v4i5.1842

Abstract

Standalone battery-based photovoltaic systems can experience suboptimal energy utilization when energy-flow decisions rely on fixed control rules that do not adapt to changing operating conditions. This study implemented Deep Reinforcement Learning (DRL) based on a Deep Q-Network (DQN) on an ESP32 as a system-level energy manager and evaluated its performance against conventional Maximum Power Point Tracking (MPPT). The experimental evaluation was conducted for six days, 21–26 June 2026, from 10:00 to 16:00 WIB, using the same prototype and a counterbalanced ABBA switching design. Data were sampled every 10 minutes, producing 444 valid observations consisting of 222 observations for MPPT and 222 for DRL. The evaluated parameters were panel output power, cumulative energy, and power stability. The DRL mode produced an average panel power of 46.75 W, compared with 45.08 W for MPPT, corresponding to a 3.70% increase. The final report summary also records cumulative energy of 1,693.93 Wh for DRL and 1,633.82 Wh for MPPT, while the coefficient of variation was 16.07% and 16.57%, respectively. DQN inference latency on ESP32 was 35–85 ms per decision. Daily-block inferential analysis reported a paired t-test of p < 0.001 and Wilcoxon signed-rank p = 0.031 (n = 6 days). The findings indicate that system-level DRL energy-flow management can improve the utilization of photovoltaic power while maintaining comparable or slightly better relative power stability under the tested conditions.
Co-Authors Azizah Azahara Hamdi Akhsan Iin Seprina Jupri Suyuti Ramadagandi Melly Ariska Sardianto Markos Siahaan Taufiq Taufiq Yenny Anwar