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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Muhammad Yunus Hendrawan
Politeknik Statistika STIS
Computer Science & IT

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Topic Modelling in Knowledge Management Documents BPS Statistics Indonesia Muhammad Yunus Hendrawan; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.52

Abstract

Knowledge management is an important activity in improving the performance an organization. BPS Statistics Indonesia has recently implemented such a system to improve the quality and efficiency of business processes. The purposes of this research are: 1) implementing topic modelling on BPS Knowledge Management System to identify groups of document topics; 2) providing recommendations on which the best topic modelling; 3) building a web service function of topic modelling for BPS that includes data preprocessing function and topic group recommendation function. This study applies the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) topic modelling methods to determine the best grouping techniques for knowledge management systems in BPS Statistics Indonesia. The results show that the LDA model using Mallet is the best model with 25 topic groups and a coherence score of 0.4803. The performance result suggest that the best modelling method is the LDA. The LDA model is then successfully implemented in RESTful web service to provide services in the preprocessing function and topic recommendations on documents entered into the Knowledge Management System BPS.
Co-Authors Nucke Widowati Kusumo Projo