Optimasi Algoritma Gaussian Mixture Model Menggunakan PCA – UMAP Untuk Klasterisasi Tingkat Kesejahteraan Rakyat

Gunawan, Rafif Ilafi Wahyu (2025) Optimasi Algoritma Gaussian Mixture Model Menggunakan PCA – UMAP Untuk Klasterisasi Tingkat Kesejahteraan Rakyat. Undergraduate thesis, Universitas Pembangunan Nasional Veteran Jawa Timur.

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Abstract

Socioeconomic inequality among regions remains a major challenge in regional development, particularly in East Java Province. Differences in social, economic, and quality-of-life conditions have led to an uneven distribution of welfare levels. To analyze this issue, this study applies the Gaussian Mixture Model (GMM) optimized through a combination of Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for welfare clustering. The dataset used was obtained from the Central Bureau of Statistics (BPS) for the period 2010–2024. The results show that the PCA–UMAP combination achieved the highest Silhouette Score of 42.36% with an optimal cluster number of theree K = 3, effectively distinguishing regions with high and low welfare levels. This approach proved to be efficient in producing stable, representative, and interpretable clusters, making it a potential tool to support data-driven policymaking aimed at equitable and sustainable regional development.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Al Haromainy, Muhammad Muharrom	NIDN0701069503	muhammad.muharrom.if@upnjatim.ac.id
Thesis advisor	Junaidi, Achmad	NIDN0710117803	achmadjunaidi.if@upnjatim.ac.id

Subjects:

Q Science > Q Science (General)
T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Rafif Ilafi Wahyu Gunawan

Date Deposited:

22 Jan 2026 07:24

Last Modified:

23 Jan 2026 06:17

URI:

https://repository.upnjatim.ac.id/id/eprint/48927