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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Parameter: Journal of Statistics Journal Of Human And Education (JAHE) Journal of Learning Improvement and Lesson Study Journal of Data Insights Prosiding Seminar Nasional Unimus
Alwan Fadlurohman
Universitas Muhammadiyah Semarang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Parameter: Journal of Statistics

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SOCIAL VULNERABILITY ANALYSIS IN CENTRAL JAVA WITH K-MEDOIDS ALGORITHM Fadlurohman, Alwan; Ayu Nur Roosyidah, Nila; Amalia Annisa, Nafida
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17131

Abstract

To address the limitations of the Social Vulnerability Index (SoVI) in only providing a general overview without pinpointing areas of social vulnerability, a correlational approach paired with a clustering method can be applied. This approach helps in identifying dominant factors and pinpointing socially vulnerable districts or cities in Central Java. The study employs the K-Medoids algorithm, which is advantageous when dealing with outliers in the dataset. Three different distance measures are considered: Euclidean, Manhattan, and Minkowski distances, to identify the optimal clustering of social vulnerability. The evaluation of the best cluster is conducted using the Davies-Bouldin Index, a metric for validating clustering models by averaging the similarity of each cluster to its most similar counterpart. Findings indicate that using the K-Medoids algorithm with Manhattan distance yields the most effective clustering, resulting in two distinct clusters. Cluster 1, comprising 25 districts/cities, is identified as the most vulnerable to natural disasters and challenges in education, demography, economy, and health. Meanwhile, Cluster 2, encompassing 10 districts/cities, includes urban areas with the highest social vulnerability, notably in the proportion of rental housing.
Co-Authors Albertus Dion Sarah Amalia Annisa, Nafida Amri, Saeful Amrullah, Setiawan Ariska Fitriyana Ningrum Ayu Nur Roosyidah, Nila Dannu Purwanto Dhani, Oktaviana Rahma Endang Tri Wahyuni Maharani Eny Winaryati Fatkhurokhman Fauzi Febi Anggun Lestari Febi Anggun Lestari Fitri Diana Musa Gautama, Rahmad Putra Ihsan Fathoni Amri Iksan, Zanaton Haji Indah Manfaati Nur M. Al Haris Oktaviana Rahma Dhani Prasetyawan Aji Sugiharto Prizka Rismawati Arum Rauf, Rose Amnah Abd Rochdi Wasono Rohim, Febrian Hikmah Nur Roosyidah, Nila Ayu Nur Tiani Wahyu Utami Wardani, Amelia Kusuma Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani