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All Journal Jurnal Sains Matematika dan Statistika Pattimura International Journal of Mathematics (PIJMath)
Laamena, N. S.
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Mathematics

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PENERAPAN ALGORITMA K-MEANS UNTUK KLUSTERISASI KABUPATEN/KOTA BERDASARKAN TINGKAT KEMISKINAN DI KEPULAUAN MALUKU DAN PAPUA Matdoan, M. Y.; Igo, La; Rumeon, Ramli; Fadhilah, Rahmi; Laamena, N. S.
Jurnal Sains Matematika dan Statistika Vol 10, No 1 (2024): JSMS Januari 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v10i1.21260

Abstract

Berdasarkan data Badan Pusat Statistik Tahun 2022 menunjukan bahwa persentase penduduk miskin terbesar berasal dari Kepulauan Maluku dan Papua. Penelitian ini menggunakan meotde K-Means untuk klusterisasi kabupaten/kota berdasarkan tingkat kemiskinan. Data yang diperoleh pada penelitian ini berasal dari BPS Provinsi Papua, Papua Barat, Maluku dan Maluku Utara yang terdiri dari 63 kabupaten/kota dengan 8 menggunakan variabel. Penelitian ini disimpulkan bahwa terdapat 3 kluster dalam tingkat kemiskinan di Kepulauan Papua dan Maluku. Kluster 0 terdiri atas Kabupaten Maluku Tengah, Kota Ambon, Kota Merauke, Jayawijaya, Kota Jayapura, Lanni Jaya dan Kota Sorong. Cluster 1 yang terdiri atas Kabupaten Seram Bagian Barat, Buru, Halmahera Tengah, Halmahera Selatan, Halmahera Timur, Halmahera Utara, Kepulauan Sula, Pulau Morotai, Jayapura, Biak Numfor, Puncak Jaya, Nabire, Paniai, Mimika, Tolikara, Yahukimo, Puncak, Manokwari dan Nduga. Selanjutnya cluster 2 yang terdiri atas Kepulaun Tanimbar, Maluku Tenggara, Kepulaun Aru, Seram Bagian Timur, Maluku Barat Daya, Buru Selatan, Tual, Halmahera Barat, Pulau Taliabu, Ternate, Tidore Kepulauan, Kepulauan Yapen,  Mappi, Boven Digoel, Asmat, Sarmi, Pegunungan Bintang, Keerom, Supiori, Waropen, Mamberamo Raya, Yalimo, Dogiyai, Mamberamo Tengah, Intan Jaya, Deiyai, Kaimana, Teluk Wondama, Fakfak, Teluk Bintuni, Sorong Selatan, Sorong, Tambrauw, Raja Ampat Maybrat, Pegunungan Arfak  dan Manokwari Selatan. Kata Kunci:  Kemiskinan, Klustering, K-Means.
Implementation of Centroid Clustering Method for Industrial Clusterization in Regencies and Cities in Maluku Province Matdoan, M. Y.; Fadhilah, Rahmi; Laamena, N. S.; Safira, Dinda Ayu; Loklomin, S. B.
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 1 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss1pp09-14

Abstract

The industrial sector has a vital role in economic development. In addition to increasing state revenue, the industrial sector can also provide business opportunities that make a positive contribution to efforts to equalize community welfare. The limited employment opportunities available in Maluku Province need to be balanced with the increase in the labor force, which significantly impacts the high unemployment. Basically, the high unemployment rate will significantly impact economic development, which aims to improve the standard of living of the people in Maluku Province. Centroid Linkage is the average of all objects in the cluster, and the distance. The distance between the cluster centroids is what separates two clusters. Cluster centroid is the center value of observations on variables in a set of cluster variables. The purpose of this research is to cluster the distribution of industries in regencies and cities in Maluku Province using data from BPS Maluku Province. This study obtained the results that there are 3 clusters formed in the clusterization of industry in regencies and cities in Maluku Province, namely cluster 1 consisting of Tanimbar Islands Regency. Cluster 2 consists of Buru, South Buru, West Seram, East Seram, Central Maluku, Tual City, Southeast Maluku, and Aru Islands Regency. Furthermore, Cluster 3 consists of Ambon City and Southwest Maluku.
Co-Authors Fadhilah, Rahmi Igo, La Loklomin, S. B. Matdoan, M. Y. Rumeon, Ramli Safira, Dinda Ayu