Kiranadhewi, Afi Iffa Praba (2025) Penerapan Metode K-Means dan Fuzzy C-Means pada Pengelompokkan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Indikator Kemiskinan. Diploma thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kemiskinan adalah masalah sentral yang dihadapi negara-negara berkembang, termasuk Indonesia. Meski beberapa negara telah menunjukkan kemajuan ekonomi, kemiskinan tetap menjadi tantangan signifikan. Di Indonesia, tingkat kemiskinan yang tinggi memerlukan perhatian khusus dari pemerintah pusat dan daerah. Provinsi Jawa Barat, yang memiliki potensi besar dalam berbagai sektor, masih berjuang melawan masalah kemiskinan. Untuk mengatasi ini, diperlukan sistem yang dapat mengelompokkan kabupaten/kota berdasarkan indikator kemiskinan sehingga program pembangunan dapat dirancang dan dilaksanakan lebih efektif. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Jawa Barat berdasarkan faktor-faktor yang mempengaruhi kemiskinan tahun 2023 menggunakan metode K-Means dan Fuzzy C-Means (FCM). Langkah awal penelitian ini adalah melakukan standardisasi data. Selanjutnya, dilakukan analisis deskriptif untuk memahami karakteristik data sebelum melakukan pengelompokan. Hasil pengelompokan kedua metode tersebut dibandingkan untuk menentukan metode yang lebih efektif. Sebagai output akhir, dibuat dashboard untuk memetakan kabupaten/kota di Jawa Barat berdasarkan indikator kemiskinan. Hasil penelitian ini diharapkan dapat memberikan gambaran kondisi kemiskinan di Jawa Barat dan berfungsi sebagai alat monitoring bagi pemerintah dan lembaga terkait dalam upaya mencapai target Sustainable Development Goals (SDGs) 2030 dalam mengurangi kemiskinan.
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Poverty is a central issue facing developing countries, including Indonesia. Although some countries have shown economic progress, poverty remains a significant challenge. In Indonesia, the high poverty rate requires special attention from the central and local governments. West Java Province, which has great potential in various sectors, is still struggling against the problem of poverty. To address this, a system is needed that can cluster districts / municipalities based on poverty indicators so that development programs can be designed and implemented more effectively. This study aims to cluster districts/cities in West Java based on factors affecting poverty in 2023 using the K-Means and Fuzzy C-Means (FCM) methods. The first step of this research is testing the multivariate normal assumption and Bartlett's test to assess the suitability of the data. Next, descriptive analysis was conducted to understand the characteristics of the data before clustering. The clustering results of the two methods were compared to determine the more effective method. As a final output, a dashboard was created to map districts/cities in West Java based on poverty indicators. The results of this research are expected to provide an overview of poverty conditions in West Java and serve as a monitoring tool for the government and related institutions in an effort to achieve the 2030 Sustainable Development Goals (SDGs) target in reducing poverty.
| Item Type: | Thesis (Diploma) |
|---|---|
| Uncontrolled Keywords: | K-Means Clustering, Fuzzy C-Means Clustering, Kemiskinan, Provinsi Jawa Barat, West Java Province, Poverty. |
| Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T58.6 Management information systems |
| Divisions: | Faculty of Vocational > 49501-Business Statistics |
| Depositing User: | Afi-Iffa Praba Kiranadhewi |
| Date Deposited: | 24 Apr 2025 01:44 |
| Last Modified: | 24 Apr 2025 01:44 |
| URI: | http://repository.its.ac.id/id/eprint/119039 |
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