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TASIKMALAYA, Thecuy.com – Repeated hydro‑meteorological threats in Tasikmalaya City and Regency are considered to require a technology‑driven handling approach.
As floods, landslides, and droughts repeatedly occur in succession, an AI‑based mitigation system is proposed as a solution so that response is no longer purely reactive after a disaster occurs.
This idea was presented by Khaerul Hadiswara, a Data Science master’s student in the Informatics Program at Universitas Islam Indonesia (UII) Yogyakarta.
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He proposes using AI and machine learning to enhance the early warning system for disasters in Tasikmalaya.
The proposal arose due to the high frequency of disasters in the Tasikmalaya area.
Throughout 2025, over 400 disaster events were recorded, ranging from landslides, floods, and land movement to clean water shortages during the dry season.
This situation indicates that prediction‑based mitigation is becoming an increasingly urgent need.
According to Khaerul, the current disaster management pattern is still dominated by a reactive approach, i.e. acting after a disaster occurs.
However, technological advances enable the government to utilize large amounts of data to predict potential risks earlier.
“Through machine learning, mitigation can shift from a reactive to a preventive pattern, thereby reducing the potential impact of disasters at an early stage,” he said in a written statement on Wednesday (15/7/2026).
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In his study, Khaerul proposes four key applications of AI technology to support disaster mitigation in Tasikmalaya.
First, a precise rainfall prediction system using a combination of XGBoost and Random Forest algorithms that processes weather data in real time.
The system is expected to provide earlier warnings of potential floods and droughts.
Second, development of a digital landslide vulnerability map using Random Forest and Support Vector Machine (SVM) algorithms.
The system processes various parameters such as rainfall intensity, slope gradient, vegetation condition, and rock characteristics. Based on the study results, this model can achieve an accuracy level of up to 91 percent.
Third, the use of deep learning technology YOLOv3 combined with Sentinel-2A satellite imagery to detect potential landslides, including in forest areas and hard‑to‑reach locations.
Meanwhile, in the post‑disaster response phase, he proposes implementing a Distribution Requirement Planning (DRP) system to optimize the distribution of aid logistics.
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