Pengelompokan Provinsi di Indonesia Tahun 2022 Berdasarkan PDRB Sektor Usaha Dengan Metode K-Means dan Self-Organizing Map

Inayah, Hanifah (2024) Pengelompokan Provinsi di Indonesia Tahun 2022 Berdasarkan PDRB Sektor Usaha Dengan Metode K-Means dan Self-Organizing Map. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perekonomian tiap provinsi di Indonesia yang digambarkan melalui PDRB masih belum merata. Padahal, kesenjangan ekonomi antar wilayah ini merupakan isu strategis yang ingin diatasi oleh pemerintah Republik Indonesia dalam tenggat waktu 2020-2024. Untuk itu, pemerintah membuat strategi pembangunan berbasis kewilayahan yang terdiri atas pertumbuhan ekonomi dan pemerataan. Dalam melaksanakan strategi tersebut, pemerintah perlu mengetahui karakteristik wilayah, dimana provinsi dengan karakteristik ekonomi yang serupa dapat diberikan sarana prasarana yang saling mendukung, efisien, dan efektif. Pengelompokan objek ke dalam beberapa kelompok berdasarkan persamaan karakteristik dapat dilakukan dengan analisis cluster. Penelitian ini akan melakukan pengelompokan provinsi di Indonesia berdasarkan PDRB sektor usaha pada tahun 2022 dengan metode clustering K-Means dan Self-Organizing Map, kemudian mendeskripsikan karakteristik dominan masing-masing kelompok menggunakan Principal Component Analysis. Hasilnya, metode yang terbaik yaitu SOM sebanyak 4 cluster. Karakteristik tiap cluster antara lain cluster 1 unggul di bidang industri dan jasa, cluster 2 unggul di sektor usaha pertambangan dan penggalian, cluster 3 cukup baik di bidang hasil alam namun kurang di sektor usaha jasa perusahaan, serta cluster 4 masih di bawah rata-rata untuk semua sektor usaha.
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It is known that the economy of each province in Indonesia as described through The economic state of Indonesia's province measured by GRDP is still not evenly distributed. This economic disparity between regions is a strategic issue that the government of the Republic of Indonesia wants to solve within the 2020-2024 deadline. To that end, the government created a regional-based development strategy consisting of economic growth and equity. In implementing this strategy, the government needs to know each region's characteristics, where economically similar regions can be provided with mutually supportive, efficient, and effective infrastructure. Grouping objects into several groups based on characteristic similarities can be done by cluster analysis. This research will classify provinces in Indonesia based on GRDP in the business sector in 2022 using the K-Means clustering method and Self-Organizing Map, then describe the dominant characteristics of each group using Principal Component Analysis. The conclusion resulting in best clustering method which is SOM with 4 clusters. Characteristics of each cluster are cluster 1 is superior in industry and services, cluster 2 is superior in the mining and quarrying business sector, cluster 3 is quite good in the natural products sector but is lacking in the corporate services business sector, and cluster 4 is still below the average for all business sectors.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Clustering, K-Means, PDRB, Principal Component Analysis, Self-Organizing Map, GDRP
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HC Economic History and Conditions > HC441 Macroeconomics.
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Hanifah Inayah
Date Deposited:	16 Apr 2024 06:51
Last Modified:	16 Apr 2024 06:51
URI:	http://repository.its.ac.id/id/eprint/107858

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