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All Journal Jurnal Responsif : Riset Sains dan Informatika
Tri Prasetio, Rizki
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Computer Science & IT

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Journal : Jurnal Responsif : Riset Sains dan Informatika

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ANALISIS LOWONGAN PEKERJAAN DI BIDANG TEKNOLOGI INFORMASI BERDASARKAN LOKASI MENGGUNAKAN TEKNIK KLASTERING K – MEANS Djaya Siswaja, Hendy; Tri Prasetio, Rizki
Jurnal Responsif : Riset Sains dan Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.2020

Abstract

This study analyzes job vacancy distribution in the Technology Information sector based on location using the K-Means clustering technique. In the current era of Industry 4.0, job seekers and employers face challenges with data overload and time-consuming recruitment processes. Identifying hidden patterns in job vacancy data is crucial to understanding the concentration of job opportunities across regions. The methodology involves data preprocessing, including data cleaning, transformation, and normalization, followed by clustering using K-Means and evaluation with the Davies-Bouldin Index (DBI) to determine optimal clustering. The results reveal that clustering with 6 groups provides the most meaningful separation of job vacancies based on location and category, with a significant dominance of one cluster. The findings suggest that more detailed analysis of smaller clusters could uncover niche opportunities. This approach can assist policymakers and job seekers in making more informed decisions regarding career opportunities.
ANALISIS LOWONGAN PEKERJAAN DI BIDANG TEKNOLOGI INFORMASI BERDASARKAN LOKASI MENGGUNAKAN TEKNIK KLASTERING K – MEANS Djaya Siswaja, Hendy; Tri Prasetio, Rizki
Jurnal RESPONSIF: Riset Sains & Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.2020

Abstract

This study analyzes job vacancy distribution in the Technology Information sector based on location using the K-Means clustering technique. In the current era of Industry 4.0, job seekers and employers face challenges with data overload and time-consuming recruitment processes. Identifying hidden patterns in job vacancy data is crucial to understanding the concentration of job opportunities across regions. The methodology involves data preprocessing, including data cleaning, transformation, and normalization, followed by clustering using K-Means and evaluation with the Davies-Bouldin Index (DBI) to determine optimal clustering. The results reveal that clustering with 6 groups provides the most meaningful separation of job vacancies based on location and category, with a significant dominance of one cluster. The findings suggest that more detailed analysis of smaller clusters could uncover niche opportunities. This approach can assist policymakers and job seekers in making more informed decisions regarding career opportunities.
Co-Authors Djaya Siswaja, Hendy