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All Journal Jurnal Pendidikan Ekonomi (JUPE) Interest : Jurnal Ilmu Kesehatan Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Pendidikan Nonformal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jiko (Jurnal Informatika dan komputer) International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Kajian Pariwisata PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Ekonomi Kependudukan dan Keluarga
Nooraeni, Rani
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Job Competency Extraction in Information and Technology Sector Using K-Means and Non-Negative Matrix Factorization (NMF) Algorithms Rifa Geandra, Alfitra; Mumtaz Siregar, Amir; Nooraeni, Rani
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.684

Abstract

The advancement of information technology has led to a surge in online job vacancy data, which contains valuable information about the skill demands in the digital labor market. This study aims to extract job competency in the information and technology sector using a combination of KMeans clustering and Non-Negative Matrix Factorization (NMF). A total of 350 job postings were collected from the Kalibrr platform and processed through web scraping, text preprocessing, and feature representation using TF-IDF. The clustering results indicate that the optimal configuration consists of 10 clusters, as evaluated using the Silhouette Score and Davies-Bouldin Index. Each cluster represents a specific job topic, such as backend development, data science, QA automation, cybersecurity, and digital marketing. The results offer a structured overview of digital skill demands and can be utilized by educational institutions, training providers, and labor policy makers. However, the dataset’s limited size, reliance on a single job platform, and the use of traditional machine learning techniques may not capture all semantic variations and complexities present in the broader job market. Consequently, future work should involve larger and more diverse datasets as well as advanced deep learning text representation approaches to enhance the robustness and generalizability of the results. 
Co-Authors A, Yolanda Rizkie Adi Lumaksono Agung Budi Santoso Al Baihaqi, Rakai Anasrul, Anasrul Astriani, Vina Briliyanto, Muhamad Bagus Adji Candra, Arya Candra Dewi, Aprilia Lutviana Firmansyah, Adrian Fitri, Muti Nurjannah Fitri, Nurhidayatul Hakim, Syifa Rahmawati Hamdani, Febri Helmiah, Najia Hirma, Emalia Septiani Khoirun Nisa', Intan Maulida Lestariningsih, Eni Mahendra, Fiqih Tri MS, Muhammad Muawwad Mumtaz Siregar, Amir Munia, Nufaisa Muqiit WS, Andriansyah Nulkarim, Aldi Rochman Prasetya, Ananda Galuh Intan Pratama, Firza Refo Adi Pulungan, Ridson Alfarizal Putra, Amran Pratama Rifa Geandra, Alfitra Rizki, M. Alfa Salsabila, Atikah Saputri, Aprillia Anis Saragih, Meilani Thereza Br. Sari, Esa Anindika Satria, Manca Shariati, Ilhamsyah Ali Siregar, Aisyah Anjani Putri Sofyan, Sabiq Sudianto, Fauzan Bayu Hera Susanti, Amalia Taufiqurrahman, Febi Yusnissa, Hertina Yustiani, Dwi Zekha F, Noval Irgi