Implementasi Deteksi Anomali Menggunakan Isolation Forest pada Data Curah Hujan Harian untuk Evaluasi Ambang Hujan Ekstrem Bmkg di Jawa Timur
DOI:
https://doi.org/10.62710/qg5dng25Keywords:
BMKG,Rainfall,Extreme Rain,Isolation ForestAbstract
Extreme rainfall requires monitoring due to the potential increased risk of hydrometeorological disasters. The BMKG defines extreme rainfall as rainfall exceeding 150 mm per day; however, rainfall characteristics vary by region. This study aims to apply the Isolation Forest algorithm to detect daily rainfall anomalies and compare the results with the BMKG extreme rainfall threshold. Data from Juanda, Kediri, and Malang stations—totaling 1,006 observations—were used. The model used daily rainfall and month of observation as features, with a contamination value of 0.05. Results showed that the model detected 17 anomalies in Juanda, 17 in Kediri, and 18 in Malang. The model also identified one BMKG-defined extreme event in Juanda (150.8 mm) as an anomaly. Comparative evaluation showed a 100% recall rate for BMKG-defined extreme events; however, this figure requires cautious interpretation, as the dataset contains only one BMKG-defined extreme event. These findings suggest that Isolation Forest can serve as a tool to identify local rainfall anomalies without replacing BMKG operational thresholds.
Downloads
References
Astika Eka Kirana, A. S. (2024). Prediksi Kelembaban Dan Curah Hujan Maksimum Di Kabupaten Malang Menggunakan Bivariate Extreme Value Logistic. 12(03), 465–474.
Auliya, E. R. (2021). Pengklasteran Daerah Di Jawa Timur Berdasarkan Curah Hujan. 09(02).
Bimaprawira, A. K. (2021). Keterkaitan Periodisitas Curah Hujan Di Daerah Pesisir Dan Pegunungan Provinsi Jawa Timur Dengan Variabilitas Cuaca Skala Global Dan Regional Relationship Of Rainfall Periodicity In Coastal Dan Mountain Areas Of East Java Province With Global And Regional. 22(2), 51–59.
Fatmi, Y., & Lase, Y. Y. (2025). Jurnal manajemen teknologi informatika. 3(26), 237–245.
Hutabarat, S. H., Putra, F. A. A., Informasi, S., & Malang, U. M. (2025). Perbandingan Metode Exponential Smoothing Dan Arima Pada Peramalan Curah Hujan Di Kota Malang. 9(4), 7294–7300.
Munanjar, M. C., Ariska, J., & Safitri, I. (2025). Validasi Data Curah Hujan Maksimum Bulanan GPM. 706–711. https://doi.org/10.33364/konstruksi/v.23-2.2708
Naufal, A., Brampu, F., & Fitra, J. (2026). Analisis Risiko Longsor Di Wilayah Tropis Berdasarkan Curah Hujan Dan Topografi Berbasis DEM : Studi Literatur Sistematis. 16(01), 310–324.
Nurlatifah. (2023). Analisis Potensi Kejadian Curah Hujan Ekstrem di Masa Mendatang Sebagai Dampak dari Perubahan Iklim di Pulau Jawa Berbasis Model Iklim Regional Conformal Cubic Atmospheric Model. 21(4), 980–986. https://doi.org/10.14710/jil.21.4.980-986
Pramono, A., & Soebroto, A. A. (2017). Analisis Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine ( Studi Kasus : Stasiun Klimatologi Jawa Timur ). 1(1), 1–7.
Risnawati, I. (2023). Klasifikasi Data Mining Untuk Mengestimasi Potensi Curah Hujan. 3(2), 78–84.
Sangaji, D., & Sutabri, T. (2025). E ISSN : 2809-4069 Analisis XGBoost dan Random Forest untuk Prediksi Curah Hujan dalam Mendukung Mitigasi Karhutla. 5(1), 13–18.
Sunarto, M. W., Kurniawan, D., Siswanto, E., & Huda, H. I. (2021). Deteksi Anomali Menggunakan Extended Isolation Forest ( Eif ). 1(2), 96–111.
Taufiqi, R. I., Pramuji, V. M., Arifudin, M., & Hari, A. (2025). Perbandingan Model Decision Tree untuk Prediksi Intensitas Hujan di. 17(1), 15–28.
Wijayanto, A., Sugiharto, A., Santoso, R., Diponegoro, U., & Korespondensi, P. (2024). Identifikasi Dini Curah Hujan Berpotensi Banjir Menggunakan Algoritma Long Short-Term Memory ( Lstm ) Dan Isolation Forest Early Identification Of Rainfall With Flood Potential Using Long Short-Term Memory ( Lstm ) And Isolation Forest Algorithms Case Stu. 11(3). https://doi.org/10.25126/jtiik.938718
Xu, H., Pang, G., Wang, Y., & Wang, Y. (2023). Deep Isolation Forest for Anomaly Detection. 1–14.
Zaehol Fatah, R. A. (2026). Mendeteksi Curah Hujan Ekstrem sebagai Upaya Mitigasi Banjir Menggunakan Algoritma K-Means. 5(April), 243–251.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Amor Maulidiyah Rizamzam Prasetya Putri, Mohammad Hafiyusholeh, Shanas Septy Prayuda, Hani Khaulasari (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
