What is it about?
Disaster-prone area mapping is critical for effective mitigation planning in vulnerable regions. We have demonstrated for the first time that the K-Prototypes algorithm can successfully cluster mixed-type disaster data (numerical and categorical) to identify high-risk zones across Java Island. This approach was previously challenging as traditional methods like K-Means handle only numerical data while K-Modes works solely with categorical data. We have used clustering validation through Davies-Bouldin Index calculations to confirm the robustness of our disaster vulnerability classifications, revealing that West Java faces the highest multi-hazard risk among all provinces studied.
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Why is it important?
We establish a framework for mapping disaster-prone areas using mixed-type data clustering to prioritize mitigation resources in high-risk regions. This is important in disaster management planning, to anticipate vulnerability patterns and allocate resources to minimize casualties and economic losses. Two significant findings are that: a) West Java exhibits the highest multi-hazard vulnerability across all four disaster types studied, requiring urgent comprehensive mitigation strategies, and b) we identify and quantify that tornado disasters, despite moderate frequency, cause disproportionately high average economic losses (IDR 200,000 per event), while earthquakes generate the highest individual event losses (IDR 425,000 per event), enabling more accurate risk-based insurance calculations and disaster preparedness budgeting.
Perspectives
I hope this article makes what people might think is a purely technical, data-driven area like disaster clustering and geographic risk mapping, kind of interesting and maybe even vital to everyday life. Because the way we understand and prepare for natural disasters is not just a problem for scientists, policymakers and emergency responders to worry about - it is an issue that touches every single person living in disaster-prone areas like Java Island, affecting where they live, how they build their homes, and ultimately their safety and livelihood. With 50% of Indonesia's natural disasters occurring on Java Island alone, and millions of people affected annually, understanding these vulnerability patterns could be the difference between life and death. More than anything else, and if nothing else, I hope this article inspires readers to recognize that disaster preparedness is not just reactive - it's a data-driven, scientific process that can save lives and protect communities.
Ahmad Fuad Zainuddin
Universitas Prasetiya Mulya
Read the Original
This page is a summary of: MAPPING DISASTER-PRONE AREAS ON JAVA ISLAND USING THE K-PROTOTYPES ALGORITHM, BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN, November 2025, Universitas Pattimura,
DOI: 10.30598/barekengvol20iss1pp0179-0196.
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