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All Journal Mandalika Mathematics and Educations Journal Eksponensial
Memi Nor Hayati
Mulawarman University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Fuzzy geographically weighted clustering pada pengelompokan kabupaten/kota di Kalimantan berdasarkan indikator indeks pembangunan manusia Milda Alfitri Diani; Memi Nor Hayati; Rito Goejantoro
Mandalika Mathematics and Educations Journal Vol 7 No 4 (2025): Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i4.9977

Abstract

Fuzzy Geographically Weighted Clustering (FGWC) is a method of development of fuzzy clustering by considering geographical elements in the process of regional clustering. This study aims to identify the number and characteristics of optimal clusters formed using the FGWC algorithm with the validity index of the Partition Coefficient Index (PCI). The data analyzed includes HDI indicators for all regencies/cities on the island of Kalimantan in 2024 consisting of the variables Life Expectancy, Length of Schooling, Average School Length, Expenditure per Capita, Open Unemployment Rate, and Percentage of Poor Population. Based on the results of the study, the optimal number of clusters was obtained as many as 2 clusters with a PCI of 0.516. Cluster 1 consists of 18 regencies/cities covering 9 cities and 9 regencies with higher average values of HDI indicator variables, while cluster 2 consists of 38 regencies which are all dominated by inland areas with lower average values of HDI indicator variables.
Penerapan Metode K-Harmonic Means pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Perumahan dan Kesehatan Lingkungan Fany Novianti; Rito Goejantoro; Memi Nor Hayati
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.v17i1.1586

Abstract

Cluster analysis is grouping data into subsets whose members have a high degree of similarity. One commonly used clustering technique is the K-Harmonic Means, an enhancement of the K-Means method, which assigns data to clusters by calculating the harmonic mean of distances from each point to the cluster centers. This study aims to determine the optimal grouping of Regencies/Cities in Kalimantan Island based on housing and environmental health indicators and to identify the best Dunn index value. Based on the grouping results, 5 groups were obtained with parameters (p) = 2. The optimal Dunn index value is 0.2171. Based on the description of the housing and environmental health indicators, it can be seen that there are still disparities between groups in terms of access to basic services, such as the percentage of clean drinking water source services, the percentage of lighting sources from PLN electricity, the percentage of proper sanitation services, and the percentage of home ownership status. Group 4 and group 2 are groups that need special attention because they have the lowest achievements in several important indicators
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 Fany Novianti Milda Alfitri Diani Nariza Wanti Wulan Sari Nihayatul Khoiriyah Rito Goejantoro