Rancang Bangun Aplikasi Peringatan Dini Potensi Banjir Berbasis Android

Rafi Azhar Nugraha; Husni Lubis

doi:10.69693/ijmst.v4i3.13314

Authors

Rafi Azhar Nugraha Universitas Harapan Medan
Husni Lubis Universitas Harapan Medan

DOI:

https://doi.org/10.69693/ijmst.v4i3.13314

Keywords:

Flood Potential, Android, Random Forest, Early Warning System, BMKG

Abstract

Flooding is one of the most frequent hydrometeorological disasters in Indonesia, highlighting the need for an early warning system capable of delivering timely and accurate information to support disaster mitigation efforts. This study aims to design and develop an Android-based flood early warning application by implementing the Random Forest algorithm as a classification method based on weather data. The research data were obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) over a six-month period, including air temperature, humidity, rainfall, and wind speed. The dataset was processed through preprocessing, data labeling, and dataset splitting using an 80:20 ratio for training and testing data, respectively. The evaluation results showed that the proposed model achieved an accuracy of 96.97%, a precision of 97.98%, a recall of 96.97%, and an F1-score of 97.06%, indicating excellent classification performance. The trained model was then integrated into an Android application capable of providing weather information, flood potential predictions, and early warning notifications based on the user's location. The results demonstrate that the developed application functions according to the system requirements and has the potential to enhance community preparedness by providing flood potential information that is timely, easily accessible, and informative.

References

1. Alfarizi, M. R., & Al-farish, M. Z. (2023). PENGGUNAAN PYTHON SEBAGAI BAHASA PEMROGRAMAN UNTUK MACHINE LEARNING DAN DEEP LEARNING. 2, 1–6.

2. Chang, K.-H., Chiu, Y.-T., & Su, W.-R. (2023). A Spatial-Temporal Deep Learning-Based Warning System Against Flooding Hazards with an Empirical Study in Taiwan.

3. Danuyasa, A. (2025). Model Prediksi Risiko Banjir Menggunakan Algoritma Machine Learning : Kajian Literatur dan Aplikasi Metode Decision Tree. 2(4), 324–335.

4. Faiza, I. M., & Andriani, W. (2022). Tinjauan Pustaka Sistematis : Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir. 11(September), 59–63.

5. Helmiyah, S., Pramestiawan, R., & Lampung, R. (2025). Analisis Komparatif Algoritma Machine Learning dengan Metrik Akurasi , Presisi , Recall , dan F1-Score pada Dataset Kacang Kering. 6(3), 152–159.

6. Hidayah, N. A., Studi, P., Informasi, S., & Selatan, K. T. (2024). EVALUASI SOFTWARE VISUAL STUDIO CODE MENGGUNAKAN METODE QUETIONNAIRES NELSEN ’ S ATTRIBUTES OF USABILITY ( NAU ). 6, 382–391.

7. Jailani, Z. F., Nurmadewi, D., Informasi, S., & Bakrie, U. (2025). Hybrid machine learning prediksi banjir menggunakan lstm dan random forests pada geodata. 8, 35–41.

8. Lin, S., Liang, Z., Guo, H., Hu, Q., & Cao, X. (2025). Application of machine learning in early warning system of geotechnical disaster : a systematic and comprehensive review.

9. Lonang, S., Yudhana, A., & Biddinika, M. K. (2023). Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting. 7, 2109–2117.

10. Luxshi, K. (2024). AI-Driven Disaster Prediction and Early Warning Systems : A Systematic Literature Review. 44–55.

11. Multidisiplin, J., Sains, D., Effendi, M. F., Lubis, I., Lubis, H., Informatika, T., Teknik, F., & Medan, U. H. (2025). SISTEM INFORMASI PENGADUAN LAYANAN BENCANA BERBASIS WEB. 1(2), 1–12.

12. Mz, M. A., Nurhayati, O. D., & Suseno, J. E. (2026). Performance Comparison of Random Forest , XGBoost , and SVM for Flood Risk Prediction Using BNPB GIS Data. 8(1), 992–1010.

13. Nyoto, R. D., & Safriadi, N. (2024). Rancangan Aplikasi Flood Forecasting and Warning System Kota Pontianak Berbasis Android. 10(2), 196–200.

14. Puspasari, R. L., Yoon, D., Kim, H., & Kim, K. (2023). Machine Learning for Flood Prediction in Indonesia : Providing Online Access for Disaster Management Control. 56(1), 65–73.

15. Rachmawardani, A., Wijaya, S. K., & Shopaheluwakan, A. (2022). SISTEM PERINGATAN DINI BANJIR BERBASIS MACHINE LEARNING : 6(2), 188–198.

16. Rizal, H. M., & Warni, E. (2025). Enhancing Flood Prediction in Urban Areas : A Machine Learning Approach for Makassar City. 15(2), 21678–21684.

17. Setiyani, L. (2021). Desain Sistem : Use Case Diagram Pendahuluan. September, 246–260.

18. Studi, P., Informasi, S., Teknologi, F., & Battuta, U. (2023). Pemodelan Sistem Penerimaan Anggota Baru dengan Unified Modeling Language ( UML ) ( Studi Kasus : Programmer Association of Battuta ). 12, 1514–1521.

19. Sunarsa, T. (2024). Perancangan Unified Modelling Language Sistem Informasi Surat Jalan dan Lembaran Permintaan Perbaikan Berbasis Website. 6(1), 46–59.

20. Teknologi, J., Ningsih, S. R., Suryana, F., Hidayat, R., & Putra, D. M. (2024). Development of Mobile Learning Applications as Learning Media Using Android Studio. 17(2), 412–426.

21. 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 STUDY OF SEMARANG AREA. 11(3)