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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Bintang Yuliani Manalu, Jernita
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Computer Science & IT

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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 Fadila Utami, Nurul Gilang Hidayat, Muhammad Hulliyyatus Suadaa, Lya Krisela Fabrianne, Elisse Median Ramadhan, Alif Muhammad Gazali, La Ode Mumtaz Siregar, Amir Nisa Rahayu Ananda Suwendra, Made Pramana, Setia Wirata Raja Panjaitan, Eurorea Yuniarto, Budi