Prediksi Curah Hujan Jawa Barat Menggunakan Algoritma Machine Learning: Analisis Komparatif Berbasis Data Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) 2024
-
Alif Fahmi
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Amali Amali Universitas Pelita Bangsa, Bekasi, Indonesia
- Aceng Badruzzaman Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
West Java Province exhibits high vulnerability to hydrometeorological disasters due to dynamic rainfall variability, necessitating an accurate weather prediction system for effective disaster mitigation.1 This study aims to conduct a comparative performance analysis of Machine Learning algorithms, specifically Support Vector Machine (SVM), Naïve Bayes, Random Forest, and XGBoost, in predicting rainfall events based on 2024 daily meteorological data sourced from BMKG. Through computational experiments utilizing three data splitting scenarios 80:20, 75:25, and 70:30, and Recursive Feature Elimination (RFE), the results demonstrate that Naïve Bayes, Random Forest, and XGBoost consistently achieved a perfect accuracy of 100% across all scenarios, whereas SVM exhibited stable but more conservative performance with an average accuracy of 95.4%. In-depth analysis indicates that the absolute accuracy achieved under specific data conditions was significantly influenced by the dominance of the daily rainfall feature (RR), leading to indications of data leakage where ensemble and probabilistic models exploited deterministic relationships much more effectively than SVM. Consequently, this study recommends a rigorous re-evaluation of input features, prioritizing atmospheric leading indicators, to develop a more realistic and adaptive early warning system in the future.
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References
Alfien Yoesra, C. Susilo, and F. Yudarmawan, “Bencana Hidrometeorologi: Strategi dan Tantangan Badan Penanggulangan Bencana Daerah (BPBD) Membentuk Kesiapsiagaan Masyarakat,” J. Penelit. Ilmu Sos. dan Eksakta, vol. 4, no. 2, pp. 173–183, 2025, doi: 10.47134/trilogi.v4i2.1603.
M. N. Tsaani et al., “Analisis Komparatif Metode Clustering dan Regresi untuk Prediksi Pola Curah Hujan Menggunakan Pendekatan Data Mining,” J. Tek. Inform. dan Teknol. Inf., vol. 5, no. 2, pp. 71–86, 2025, doi: https://doi.org/10.55606/jutiti.v5i2.5467.
F. H. Nicolaus Advendea Prakoso Indaryono, Rd. Rohmat Saedudin, “ANALISA PERBANDINGAN ALGORITMA RANDOM FOREST DAN NAÏVE BAYES UNTUK KLASIFIKASI CURAH HUJAN BERDASARKAN IKLIM DI INDONESIA Nicolaus,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 1, pp. 158–167, 2024, [Online]. Available: https://doi.org/10.29100/jipi.v9i1.4421
I. Hapsari and S. Pandya Wisesa, “Evaluasi Model Prediksi Curah Hujan Berbasis Machine Learning di Kota Bandung,” J. Nas. Teknol. dan Sist. Inf., vol. 11, no. 2, pp. 136–143, 2025, doi: 10.25077/teknosi.v11i2.2025.136-143.
A. Syahreza, N. K. Ningrum, and M. A. Syahrazy, “Perbandingan Kinerja Model Prediksi Cuaca: Random Forest, Support Vector Regression, dan XGBoost,” Edumatic J. Pendidik. Inform., vol. 8, no. 2, pp. 526–534, 2024, doi: 10.29408/edumatic.v8i2.27640.
M. A. Bouke and A. Abdullah, “An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability,” Expert Syst. Appl., 2023, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417423012174
D. B. Klimatologi, B. Meteorologi, and D. A. N. Geofisika, CATATAN IKLIM DAN KUALITAS UDARA INDONESIA 2024. 2024.
S. R. Rahmadania, “BMKG Ungkap 2024 Jadi Tahun Terpanas di RI, Inikah Pemicunya?” Jan. 02, 2025. [Online]. Available: https://health.detik.com/berita-detikhealth/d-7722475/bmkg-ungkap-2024-jadi-tahun-terpanas-di-ri-inikah-pemicunya
D. Munandar, B. N. Ruchjana, A. S. Abdullah, and H. F. Pardede, “Integration GSTARIMA with deep neural network to enhance prediction accuracy on rainfall data,” Syst. Sci. Control Eng., vol. 12, no. 1, p. 2409106, Dec. 2024, doi: 10.1080/21642583.2024.2409106.
A. V Kumar et al., “Rainfall Prediction Using Machine Learning,” in IGI Global Scientific Publishing, 2024, pp. 100–113. doi: 10.4018/979-8-3693-3807-0.ch009.
S.-H. Moon, Y.-H. Kim, Y. H. Lee, and B.-R. Moon, “Application of machine learning to an early warning system for very short-term heavy rainfall,” J. Hydrol., vol. 568, pp. 1042–1054, 2019, doi: https://doi.org/10.1016/j.jhydrol.2018.11.060.
A. Sampathirao, M. Divya, and P. Sahu, “Feature-based child mortality prediction using ensemble and traditional machine learning models,” J. Appl. Sci. Technol. Trends, vol. 6, no. 2, pp. 169–182, 2025, doi: 10.38094/jastt62264.
S. del Río, V. López, J. M. Benítez, and F. Herrera, “On the use of MapReduce for imbalanced big data using Random Forest,” Inf. Sci. (Ny)., vol. 285, pp. 112–137, 2014, doi: https://doi.org/10.1016/j.ins.2014.03.043.
S. R. Vinta and R. Peeriga, “Rainfall Prediction using XGB Model with the Australian Dataset,” EAI Endorsed Trans. Energy Web, vol. 11, 2024, doi: 10.4108/ew.5386.
Y. Mohia, R. Absi, M. Lazri, K. Labadi, F. Ouallouche, and S. Ameur, “Quantitative estimation of rainfall from remote sensing data using machine learning regression models,” Hydrology, vol. 10, no. 2, p. 52, 2023, doi: 10.3390/hydrology10020052.
M. M. Rahman, M. M. Islam, M. M. H. Manik, M. R. Islam, and M. S. Al-Rakhami, “Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic,” SN Comput. Sci., vol. 2, no. 5, p. 384, 2021, doi: 10.1007/s42979-021-00774-7.
S. Wadhwa and R. G. Tiwari, “Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms,” in International Conference in Advances in Power, Signal, and Information Technology (APSIT), 2023, pp. 487–492. doi: 10.1109/APSIT58554.2023.10201679.
M. R. Allen-Dumas, H. Xu, K. R. Kurte, and D. Rastogi, “Toward urban water security: Broadening the use of machine learning methods for mitigating urban water hazards,” Front. Water, vol. 2, 2021, doi: 10.3389/frwa.2020.562304.
A. R. Hamad, A. N. Abdulateef, B. M. Sabbar, M. J. Mnati, A. H. Ali, and A. Van Den Bossche, “Integrating machine learning in IoT solutions for real-time weather forecasting systems,” Instrum. mes. métrol., vol. 24, no. 2, pp. 119–129, 2025, doi: 10.18280/i2m.240203.
M. Ilić, Z. Srdjević, and B. Srdjević, “Water quality prediction based on Naïve Bayes algorithm,” Water Sci. Technol., vol. 85, no. 4, pp. 1027–1039, Jan. 2022, doi: 10.2166/wst.2022.006.
M. L. T. Alfianti and R. Supriyanto, “Perbandingan Kinerja Algoritma Random Forest, AdaBoost, dan XGBoost Dalam Memprediksi Resiko Penyakit Osteoporosis,” J. Ilmu Komput. dan Agri …, 2024, [Online]. Available: https://journal.ipb.ac.id/index.php/jika/article/view/59154
B. K. Cahyono et al., “Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting,” Commun. Sci. Technol., vol. 10, no. 1, pp. 135–147, 2025, doi: 10.21924/cst.10.1.2025.1638.
M. El Hafyani, K. El Himdi, and S.-E. El Adlouni, “Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique,” Front. Water, vol. Volume 6-2024, 2024, doi: 10.3389/frwa.2024.1378598.
S. K. Singh, S. Kevin, S. Pal, and P. Yadav, “Rainfall Prediction Using Machine Learning,” Int. J. Sci. Dev. Res., vol. 10, no. 3, pp. 484–493, 2025, [Online]. Available: https://ijsdr.org/papers/IJSDR2503160
I Dewa Gede Loka Maheswara and A. H. Al’aziz, “PERBANDINGAN MODEL MACHINE LEARNING PADA KLASIFIKASI CURAH HUJAN DI BOGOR,” j. inti nm, vol. 19, no. 2, pp. 202–210, 2025, doi: 10.33480/inti.v19i2.6296.
E. T. Suharmanto and A. Supriyanto, “Assessment of IDW and ANN on daily rainfall data imputation in Semarang central java,” SinkrOn, vol. 9, no. 1, pp. 382–394, 2025, doi: 10.33395/sinkron.v9i1.14452.
C. Gde and L. Pringandana, “A Comparative Analysis of Hyperparameter-Tuned XGBoost and LightGBM for Multiclass Rainfall Classification in Jakarta,” J. Tek. Inform., vol. 6, no. 4, pp. 2467–2483, 2025, doi: https://doi.org/10.52436/1.jutif.2025.6.4.4965 A.
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