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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Mumtaz Siregar, Amir
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Computer Science & IT

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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. 
Business Description Categorization to the Five-Digit Indonesian Standard Classification of Business Field (KBLI) Using Machine Learning and Transfer Learning Amnur, Muh. Alfian; Muhammad Gazali, La Ode; Mumtaz Siregar, Amir; Ariya Jalaksana, Faruq; Nisa Rahayu Ananda Suwendra, Made; Fadila Utami, Nurul; Median Ramadhan, Alif; Krisela Fabrianne, Elisse; Wirata Raja Panjaitan, Eurorea; Aini Izzati, Fitri; Bintang Yuliani Manalu, Jernita; Gilang Hidayat, Muhammad; Hulliyyatus Suadaa, Lya; Yuniarto, Budi; Pramana, Setia
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.v2025i1.719

Abstract

The Indonesian Standard Classification of Business Fields (KBLI) is essential for economic statistics, yet manual classification of business descriptions to five-digit KBLI codes is time-consuming and prone to inconsistencies. This study aims to develop and compare machine learning (Support Vector Machine and Random Forest) and transfer learning  (IndoBERT) models for automating KBLI classification, supported by the preparation of synthetic and real-world datasets for model training. The synthetic data were generated using large language models, validated through human majority voting and complemented with realworld data from the National Labor Force Survey (Sakernas) and the Micro and Small Industry Survey (IMK). The findings indicate that Fine-tuned IndoBERT achieved superior performance, achieving an F1-score of 92.99% and an accuracy of 93.40% on synthetic data, alongside top-1, top-5, and top-10 accuracies of 32.93%, 54.71%, and 63.24% on real-world data. The deployment of fine-tuned IndoBERT as a RESTful API demonstrates its scalability and efficiency, presenting a reliable solution for large-scale KBLI classification in official statistics. 
Co-Authors Aini Izzati, Fitri Amnur, Muh. Alfian Ariya Jalaksana, Faruq Bintang Yuliani Manalu, Jernita Fadila Utami, Nurul Gilang Hidayat, Muhammad Hulliyyatus Suadaa, Lya Krisela Fabrianne, Elisse Median Ramadhan, Alif Muhammad Gazali, La Ode Nisa Rahayu Ananda Suwendra, Made Nooraeni, Rani Pramana, Setia Rifa Geandra, Alfitra Wirata Raja Panjaitan, Eurorea Yuniarto, Budi