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All Journal EIGEN MATHEMATICS JOURNAL Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Social Sciences and Humanities ARRUS Journal of Mathematics and Applied Science Ininnawa: Jurnal Pengabdian Masyarakat ARRUS Jurnal Pengabdian Kepada Masyarakat Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Jurnal MediaTIK Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sitti Masyitah Meliyana R.
Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Journal : JINAV: Journal of Information and Visualization

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Fuzzy Geographically Weighted Clustering Analysis of Poverty Indicators in South Sulawesi, Indonesia Annisa, Nurawalia; Aidid, Muhammad Kasim; Meliyana, Sitti Masyitah
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3949

Abstract

Cluster analysis is a method used to group data into several clusters, where the data within a single cluster exhibit high similarity, while the data between clusters show low similarity. This study aims to classify the regencies and cities in South Sulawesi based on poverty indicators using the Fuzzy Geographically Weighted Clustering (FGWC) method. FGWC is an integration of the classical fuzzy clustering approach with geo-demographic components, incorporating geographical aspects into the analysis. As a result, the clusters formed are sensitive to environmental effects, which influence the values of cluster centers. In this study, the optimal number of clusters was determined using the IFV (Index of Fuzzy Validity) validity index, which indicated an optimal solution of three clusters. Cluster 1 consists of 9 regencies/cities characterized by a high level of poverty. Cluster 2 comprises 7 regencies/cities with a moderate level of poverty. Cluster 3 includes 8 regencies/cities with a low level of poverty.
Performance Evaluation of the K-Means Clustering Method in Grouping Indonesian Provinces Based on Potential Disaster Impact Meliyana, Sitti Masyitah; Dunggio, Anugra S. S.; Ahmar, Ansari Saleh
JINAV: Journal of Information and Visualization Vol. 6 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to cluster the provinces in Indonesia based on their level of potential disaster impact, which consists of hazard area, exposed population, physical losses, economic losses, and environmental damage, using the K-Means clustering algorithm and to evaluate the performance of the resulting model. The optimal number of clusters was determined using the Silhouette Coefficient and the Elbow Method with the Within-Cluster Sum of Squares (WSS) approach. The performance evaluation of the K-Means clustering was conducted using the Davies–Bouldin Index (DBI). Based on the selection of the optimal number of clusters, the Silhouette Coefficient produced the highest value at K=3, with a score of 0.699. Similarly, the Elbow Method showed a significant decrease in the mean WSS at K=3, indicating that three clusters were optimal. The performance evaluation using DBI for K=3 resulted in a score of 0.30. According to the principle of DBI evaluation, the closer the DBI value is to zero (without being negative), the better the clustering quality. Therefore, it can be concluded that the K-Means clustering algorithm successfully produced a very good clustering structure in grouping Indonesian provinces based on their potential disaster impact.
Co-Authors Abdul Rahman Agusalim Juhari Ahmad Yasir Aras Ahmar, Ansari Saleh Aidid, Muhammad Kasim Alfairus, Muh. Qodri Alok Kumar Panday Angri Lismayani Anisa Nurfadilah Nurdin Annisa, Nurawalia Baso Intang Sappaile Dunggio, Anugra S. S. Elisa, Nur Erman Syarif Hardianti Hafid Hasbullah, Dinda Warfani Hastuty Musa Islamiah, Farida Isma Muthahharah Isma Muthahharah Jamaluddin Jamaluddin Lince, Ranak Mar'ah, Zakiyah Mariani Mar’ah, Zakiyah Miguel Botto-Tobar Muhammad Ammar Naufal Muhammad Ansarullah S. Tabbu Muhammad Isbar Pratama Muhammad, Subhan Muthahharah, Isma Nachnoer Arss Parkhimenko Vladimir Anatolievich R. Rusli Rahmadhani, Amaliyah Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rais, Zulkifli Rezky Amalia Hamka Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusli Rusli Rusli Rusli Rustam, Sitti Nail Rustam, Sitti Nailah S. Sudarmin, S. S.A. Dunggio, Anugra Siti Nurazizah Auliah Sitti Nailah Rustam Sitti Nailah Rustam Sudarmin Sudarmin Sudarmin Wahyuni, Maya Sari Yunus, Asmar Zakiyah Mar'ah Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais