SISTEM DETEKSI BANJIR DI KABUPATEN BEKASI BERBASIS DATA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.33480/inti.v21i1.8642Keywords:
Flood Detection, Support Vector Machine, Text Mining, TF-IDF, TwitterAbstract
Floods are the most common hydrometeorological disasters in Indonesia, and Bekasi Regency is among the areas with a high level of vulnerability. This study aims to develop a Twitter-based flood detection system using Support Vector Machines (SVM), with Logistic Regression (LR) and Random Forest (RF) as comparison models. A total of 4,436 tweets from the 2020–2024 period were collected using Tweet Harvest, manually labeled, and then processed through preprocessing, TF-IDF feature extraction, and data splitting using group-based random splitting (80:20). Evaluation was conducted using 10-fold GroupKFold cross-validation with recall as the primary metric, followed by hyperparameter tuning using GridSearchCV. The evaluation results showed that SVM performed best compared to LR and RF. The tuned SVM model achieved a test recall of 0.8673, exceeding the minimum threshold of 0.80. The model was then integrated into a web-based monitoring dashboard that displays interactive maps, statistics, and temporal trends. Black-box testing across nine scenarios achieved a 100% success rate, while a user experience evaluation using the UEQ-S on 18 respondents yielded an overall score of 1.97, categorized as “Very Good.” The research results indicate that the developed system is capable of effectively supporting social media-based flood monitoring.
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References
Adili, P., & Chen, Y. (2024). Fast disaster event detection from social media: an active learning method. International Journal of Computers Communications & Control, 19(2).
Agustina, I. H. (2021). Study of flood-prone areas in Bekasi Regency. Jurnal Geografi Gea, 21(2), 180–187.
Alharbi, A. A. (2024). Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable. Discrete Dynamics in Nature and Society, 2024(1), 6671395.
Aryanti, N. N. A., & Suria, O. (2025). Analisis Sentimen Terhadap Pemutusan Hubungan Kerja di Indonesia: Komparasi IndoBERT dengan SVM, Random Forest, dan Decision Tree dengan Optimasi TF-IDF. Rabit: Jurnal Teknologi dan Sistem Informasi Univrab, 10(2), 1158–1176.
Azizah, M., Subiyanto, A., Triutomo, S., & Wahyuni, D. (2022). Pengaruh perubahan iklim terhadap bencana hidrometeorologi di kecamatan cisarua-kabupaten bogor. PENDIPA Journal of Science Education, 6(2), 541–546.
BMKG. (2021). Peta Rata-Rata Curah Hujan dan Hari Hujan Periode 1991–2020 Indonesia. Pusat Informasi Perubahan Iklim BMKG.
BNPB. (2025). Data Bencana Indonesia 2024 (Vol. 3). Pusat Data Informasi dan Komunikasi Kebencanaan, BNPB.
Caniago, R., & Habibi, M. (2023). Klasifikasi Situasi Bencana Alam Banjir Menggunakan Support Vector Machine Berdasarkan Data Twitter. INFORMATIKA, 15(1), 16–22.
Esparza, M., Farahmand, H., Liu, X., & Mostafavi, A. (2024). Enhancing inundation monitoring of road networks using crowdsourced flood reports. Urban Informatics, 3(1), 25.
Ghosh, D., & Cabrera, J. (2021). Enriched random forest for high dimensional genomic data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(5), 2817–2828.
Hassan, S. U., Ahamed, J., & Ahmad, K. (2022). Analytics of machine learning-based algorithms for text classification. Sustainable Operations and Computers, 3, 238–248.
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9).
Mahadika, D. A., Aristyagama, Y. H., & Budiyanto, C. W. (2023). Evaluation of Website Based Information System To Monitor Student Learning Progress In Schools Using ISO/IEC 9126 Standards And GTMetrix. IJIE (Indonesian Journal of Informatics Education), 7(1), 42–51.
Mansoor, M., Rehman, Z. U., Afzal, S., Gil, J. M., Jo, J., & Yoon, Y. C. (2025). NLP Framework for Disaster Prediction Using Social Media Analytics and Large Language Models. International Journal on Advanced Science, Engineering & Information Technology, 15(4).
Marzouk, R., Alluhaidan, A. S., & El_Rahman, S. A. (2022). An analytical predictive models and secure web-based personalized diabetes monitoring system. IEEE Access, 10, 105657–105673.
Moghadas, M., Fekete, A., Rajabifard, A., & Kötter, T. (2023). The wisdom of crowds for improved disaster resilience: a near-real-time analysis of crowdsourced social media data on the 2021 flood in Germany. GeoJournal, 88(4), 4215–4241.
Palomino, M. A., & Aider, F. (2022). Evaluating the effectiveness of text pre-processing in sentiment analysis. Applied Sciences, 12(17), 8765.
Prosise, J. (2022). Applied Machine Learning and AI for Engineers: Solve Business Problems that Can’t be Solved Algorithmically. O’Reilly Media, Inc.
Purwandari, K., Perdana, R. B., Sigalingging, J. W., Rahutomo, R., & Pardamean, B. (2023). Automatic smart crawling on twitter for weather information in indonesia. Procedia Computer Science, 227, 795–804.
Reinert, J., Dittmer, C., Lorenz, D. F., & Klopries, E. M. (2025). Design Flaws at the Interface of Flood Forecasting, Early Warning and Disaster Response in the Disaster in Western Germany in July 2021—An Interdisciplinary Analysis. Journal of Flood Risk Management, 18(3), e70099.
Ristiana, I., & Mutmainah. (2026). Analisis Sentimen Bencana Banjir Sumatera Menggunakan TF-IDF Dan Logistic Regression. Jurnal Sistem Informasi dan Teknologi (SINTEK), 6(1), 57–65.
Sari, P. K., & Suryono, R. R. (2024). Komparasi algoritma Support Vector Machine dan Random Forest untuk analisis sentimen metaverse. Jurnal Mnemonic, 7(1), 31–39.
Segovia-Cardozo, D. A., Bernal-Basurco, C., & Rodríguez-Sinobas, L. (2023). Tipping bucket rain gauges in hydrological research: Summary on measurement uncertainties, calibration, and error reduction strategies. Sensors, 23(12), 5385.
Sinaga, A., & Nainggolan, S. P. (2023). Analisis perbandingan akurasi dan waktu proses algoritma stemming Arifin-Setiono dan Nazief-Adriani pada dokumen teks Bahasa Indonesia. Sebatik, 27(1), 63–69.
Sufi, F. K., & Khalil, I. (2022). Automated disaster monitoring from social media posts using AI-based location intelligence and sentiment analysis. IEEE Transactions on Computational Social Systems, 11(4), 4614–4624.
Thieken, A. H., Bubeck, P., Heidenreich, A., Von Keyserlingk, J., Dillenardt, L., & Otto, A. (2023). Performance of the flood warning system in Germany in July 2021–insights from affected residents. Natural Hazards and Earth System Sciences, 23(2), 973–990.
Thölke, P., Mantilla-Ramos, Y. J., Abdelhedi, H., Maschke, C., Dehgan, A., Harel, Y., & Jerbi, K. (2023). Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data. NeuroImage, 277, 120253.
Valkenborg, D., Rousseau, A. J., Geubbelmans, M., & Burzykowski, T. (2023). Support vector machines. American Journal of Orthodontics and Dentofacial Orthopedics, 164(5), 754–757.
Veigel, N., Kreibich, H., De Bruijn, J. A., Aerts, J. C., & Cominola, A. (2025). Content analysis of multi-annual time series of flood-related Twitter (X) data. Natural Hazards and Earth System Sciences, 25(2), 879–891.
Yang, T., Xie, J., Li, G., Zhang, L., Mou, N., Wang, H., & Wang, X. (2022). Extracting disaster-related location information through social media to assist remote sensing for disaster analysis: The case of the flood disaster in the Yangtze River Basin in China in 2020. Remote Sensing, 14(5), 1199.
Yu, C., & Wang, Z. (2024). Multimodal social sensing for the spatio-temporal evolution and assessment of nature disasters. Sensors, 24(18), 5889.
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