Pengelompokkan Wilayah Banjir di Jawa Tengah untuk Mitigasi Banjir Menggunakan Pendekatan K-Medoids

Background: Flooding is a natural event that can occur at any time, often resulting in fatalities and significant material losses. Mapping flood zones in Central Java based on flood occurrences is crucial for optimizing disaster management. Clustering approaches are highly relevant and potential methods for tackling flood mitigation challenges in Central Java. Objective: To map flood zoning in Central Java using the optimal K-Medoids method based on the Silhouette Coefficient. Methods: This study uses the K-Medoids method for Clustering analysis because it is more resistant to outliers. Unlike K-Means, K-Medoids selects the medoid as the cluster center, making it more stable against extreme values. The data used was obtained from the Dinas PUSDATARU of Central Java Province regarding flood events in the region from October 1, 2022, to March 2023. Results: The K-Medoids method with k=2 produced the highest Silhouette Coefficient of 0.83748, Clustering 34 districts/cities with low flood occurrences and 1 district/city with high flood occurrences. This model evaluation supports the planning of disaster mitigation policies that focus more on flood-prone areas. Conclusion: There are two groups of districts/cities based on flood occurrence levels. The high Silhouette Coefficient value indicates a good cluster structure.

Authors

Institutions

Publication Details

Journal
Jurnal Statistika dan Komputasi
Published
2024-12-31
DOI
https://doi.org/10.32665/statkom.v4i2.3223
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pengelompokkan Wilayah Banjir di Jawa Tengah untuk Mitigasi Banjir Menggunakan Pendekatan K-Medoids

M. Al Haris, Safril Ahmadi Sanmas, Rahma Nurmalita, Dwi Sulistiyani
Jurnal Statistika dan Komputasi
Data Mining and Machine Learning Applications
article

Pengelompokkan Wilayah Banjir di Jawa Tengah untuk Mitigasi Banjir Menggunakan Pendekatan K-Medoids

M. Al Haris, Safril Ahmadi Sanmas, Rahma Nurmalita, Dwi Sulistiyani
article en

Abstract

Background: Flooding is a natural event that can occur at any time, often resulting in fatalities and significant material losses. Mapping flood zones in Central Java based on flood occurrences is crucial for optimizing disaster management. Clustering approaches are highly relevant and potential methods for tackling flood mitigation challenges in Central Java. Objective: To map flood zoning in Central Java using the optimal K-Medoids method based on the Silhouette Coefficient. Methods: This study uses the K-Medoids method for Clustering analysis because it is more resistant to outliers. Unlike K-Means, K-Medoids selects the medoid as the cluster center, making it more stable against extreme values. The data used was obtained from the Dinas PUSDATARU of Central Java Province regarding flood events in the region from October 1, 2022, to March 2023. Results: The K-Medoids method with k=2 produced the highest Silhouette Coefficient of 0.83748, Clustering 34 districts/cities with low flood occurrences and 1 district/city with high flood occurrences. This model evaluation supports the planning of disaster mitigation policies that focus more on flood-prone areas. Conclusion: There are two groups of districts/cities based on flood occurrence levels. The high Silhouette Coefficient value indicates a good cluster structure.

Jurnal Statistika dan KomputasiVol. 4(2)
Universitas Muhammadiyah Semarang (ID)
Openalex Percentile: Top 61%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Pengelompokkan Wilayah Banjir di Jawa Tengah untuk Mitigasi Banjir Menggunakan Pendekatan K-Medoids — M. Al Haris, Safril Ahmadi Sanmas, et al. · Jurnal Statistika dan Komputasi (2024) | TGRS Research Map | TGRS