Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Eksponensial
Nihayatul Khoiriyah
Mulawarman University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasterisasi Rumah Tangga Miskin di Kota Samarinda Menggunakan Algoritma K-Modes dengan Validasi Klaster Davies Bouldin Index Memi Nor Hayati; Nihayatul Khoiriyah; Nariza Wanti Wulan Sari; Aji Syarif Hidayatullah
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v17i1.1627

Abstract

Poverty alleviation is one of goals at Sustainable Development Goals (SDGs) that can be supported by grouping poor households based on similar characteristics, enabling social assistance programs to be delivered more effectively and accurately. This study aims to cluster poor households in Samarinda City using the K-Modes algorithm with validation through the Davies-Bouldin Index (DBI). The data were obtained from the 2023 Samarinda Poverty Survey and consist of 16 poor households indicators. The analysis was conducted by testing the number of clusters (K) from 2 to 10. The results indicate that the optimal number of clusters is 3, with a DBI value of 1.6997. Cluster 1 consists of 11,237 households, Cluster 2 consists of 2,327 households, and Cluster 3 consists of 1,451 households. The distinct characteristics of each cluster suggest that the clustering results can serve as a basis for designing more targeted social assistance programs. Future research is recommended to consider alternative clustering methods for categorical data, such as the ROCK algorithm, which utilizes link-based similarity by considering the number of common neighbors between objects, allowing it to better capture the inherent structure of categorical data compared to distance-based methods.
Co-Authors Aji Syarif Hidayatullah Memi Nor Hayati Nariza Wanti Wulan Sari