Setiyanto, Kanessa Jasmine Prisheila Az Zahra (2026) Analisis Klaster Status Pembangunan Kabupaten/Kota di Jawa-Bali Menggunakan Variational Autoencoder dan Improved K-Means Clustering. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Regional development is an important issue in improving public welfare, particularly in the Java–Bali region where disparities still persist. The complexity of development indicators such as the Human Development Index, Gross Regional Domestic Product, and other socio-economic variables often makes it difficult to identify the development status across districts/cities. This study employs a Deep Learning approach using Variational Autoencoder (VAE) and Improved K-Means for clustering. The urgency of this research lies in the need for more adaptive and accurate analytical methods compared to traditional approaches. The research gap arises from the limitations of previous studies that mainly used conventional methods and did not specifically focus on the Java–Bali region. This study aims to generate a development clustering map as a basis for more equitable and balanced policy-making. The results show the formation of three clusters, namely developing (C0), underdeveloped (C1), and developed (C2), indicating persistent disparities, with fairly good clustering quality reflected by an average Silhouette Score of 0.437, Davies-Bouldin Index of 0.730, and Calinski-Harabasz Index of 213.169.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76.87 Neural computers T Technology > T Technology (General) |
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| Divisions: | Faculty of Computer Science > Departemen of Data Science | ||||||||||||
| Depositing User: | Kanessa Jasmine Prisheila Az Zahra Setiyanto | ||||||||||||
| Date Deposited: | 21 May 2026 06:23 | ||||||||||||
| Last Modified: | 21 May 2026 06:23 | ||||||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/51601 |
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