PENERAPAN ALGORITMA K-MEANS UNTUK SEGMENTASI DAERAH DI JAWA TIMUR BERDASARKAN INDIKATOR KESEJAHTERAAN MASYARAKAT

Happy Maulana, Muhammad Kandias (2024) PENERAPAN ALGORITMA K-MEANS UNTUK SEGMENTASI DAERAH DI JAWA TIMUR BERDASARKAN INDIKATOR KESEJAHTERAAN MASYARAKAT. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Community welfare is a crucial indicator in assessing the quality of life in a region. This research aims to cluster regions in East Java Province based on welfare indicators using the K-Means algorithm. Principal Component Analysis (PCA) is employed to reduce the dimensions of the welfare variables before clustering. The study follows the stages of the CRISP-DM data mining process, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. In the modeling stage, the K-Means algorithm is applied to identify characteristics or information to cluster regions by their welfare levels in East Java Province from 2020 to 2022. The clustering results are visualized as interactive maps using the Geopandas and Folium libraries in Python. The findings show that in 2020 and 2021, two clusters were formed: Cluster 2 represents prosperous regions, while Cluster 1 represents less prosperous regions. In 2022, six clusters with distinct characteristics were formed. Keywords: Clustering, Welfare, Principal Component Analysis, K-Means, Segmentation

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Wibowo, Nur Cahyo	NIDN0717037901	nurcahyo.si@upnjatim.ac.id
Thesis advisor	Efrat Najaf, Abdul Rezha	NIDN0029099403	rezha.efrat.sifo@upnjatim.ac.id

Subjects:

T Technology > T Technology (General) > T58.6-58.62 Management Information Systems

Divisions:

Faculty of Computer Science > Departemen of Information Systems

Depositing User:

M.Kandias Happy Maulana

Date Deposited:

31 Jul 2024 08:19

Last Modified:

31 Jul 2024 08:19

URI:

https://repository.upnjatim.ac.id/id/eprint/28037