ANALISIS PERBANDINGAN ALGORITMA K-MEANS DAN KMEDOIDS UNTUK KLASTERISASI DATA KEMISKINAN DI SUMATERA SELATAN MENGGUNAKAN EVALUASI DAVIES BOULDIN INDEX

AKHDA, M. DANDI and Tania, Ken Ditha (2024) ANALISIS PERBANDINGAN ALGORITMA K-MEANS DAN KMEDOIDS UNTUK KLASTERISASI DATA KEMISKINAN DI SUMATERA SELATAN MENGGUNAKAN EVALUASI DAVIES BOULDIN INDEX. Undergraduate thesis, Sriwijaya University.

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Abstract

This research focuses on the implementation and comparison of the KMeans and KMedoids algorithms that function as poverty data clustering in South Sumatra Province, the poverty data is taken from the Central Statistics Agency of Indonesia (BPS Indonesia). This research also aims to analyze the poverty level in South Sumatra Province by including additional variables such as average years of schooling and per capita expenditure in the community in each regency or city in South Sumatra Province. Data clustering is done by both algorithms and then the performance value is Evaluated using Davies Bouldin Index DBI shows that KMeans gives better results, with a lower DBI value (0.204 at K=5) while KMedoids has a DBI value of 0.239 at K=5, which indicates more compact and separated clusters. The superiority of K-Means is due to the homogeneous and minimal outlier characteristics of the dataset, which makes the centroid approach more optimal than medoids in K-Medoids. With these results, K-Means was chosen as the best algorithm for clustering poverty data in the region. The use of the KMeans algorithm produces a pattern in clusters related to education, economic inequality, and poverty distribution in various regions in South Sumatra. This implementation provides insight into how data clustering techniques can be applied to socioeconomic data to provide policy makers in a region with information about the region, especially information about poverty-stricken areas.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	K-Means, K-Medoids, Data Kemiskinan, Clustering, Indeks Davies Bouldin (dbi)
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	M. Dandi Akhda
Date Deposited:	08 Jan 2025 06:28
Last Modified:	08 Jan 2025 06:28
URI:	http://repository.unsri.ac.id/id/eprint/162974

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