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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JSiI (Jurnal Sistem Informasi) Jurnal Telematika Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Jurnal UNITEK Jurnal Algoritma Jurnal Masyarakat Madani Indonesia AL-UMRON: Jurnal Pengabdian kepada Masyarakat The Indonesian Journal of Computer Science
Sanjaya, Ucta Pradema
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Journal : Jurnal Informatika Global

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Algoritma K-Means untuk Mengelompokkan Tingkat Pengangguran Terbuka (TPT) menurut Provinsi di Indonesia Saputra, Agus Bima; Sanjaya, Ucta Pradema; Sa’ida, Ita Aristia
Jurnal Ilmiah Informatika Global Vol. 15 No. 2: Agustus 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i2.4359

Abstract

Unemployment is one of the main problems faced by many countries, including Indonesia. The Open Unemployment Rate (OER) is an important indicator used to measure the amount of labor force that is not absorbed in the labor market. This research aims to Cluster the provinces in Indonesia based on unemployment rate and school enrollment rate, so as to provide a clearer picture of the distribution of unemployment in different regions: The study identified three main Clusters: Cluster 1: Provinces with high unemployment rates. Cluster 2: Provinces with a medium unemployment rate. Cluster 3: Provinces with low unemployment rates. Distribution: The Clustering results show that 13 provinces are included in Cluster 1, 18 provinces in Cluster 2, and 3 provinces in Cluster 3. This study found that the K-Means algorithm is effective in Clustering provinces based on TPT and school enrollment rates. The Clustering results show significant variation between provinces, with some provinces having higher unemployment rates and lower school enrollment than others.This study successfully Clustered Indonesian provinces based on unemployment and school enrollment rates using the K-Means algorithm. The Clustering results provide valuable insights into the distribution of unemployment in Indonesia and can be used as a basis for more effective policy making.
Co-Authors Ade Pratama Ais, Salma Rihadatul Damayanti, Hevi Alvina Diah Anggraini, Diah Dirgantoro, Guruh Putro Fauziyah, Dewi Nur Fetrika Anggraini, Fetrika Heni Setyowati Hesti Yunitiara Rizqi Hevi Alvina Damayanti Ilhamy, Rifqi Syahrul Irsyada, Rahmat Ita Aristia Sa'ida Iwan Setiawan Wibisono Nita Cahyani Oktana, Weka Brilliant Jaya Prastya, Ifnu Wisma Dwi Pribadi , Teguh Saputra, Agus Bima Sri Mujiyono Wibisono, Bagas Zakki Alawi Zayn, Afta Ramadhan