Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER

Authors

  • Ahmad Khotibul Umam Universitas Dian Nuswantoro
  • Farrikh Alzami Universitas Dian Nuswantoro
  • Ramadhan Rakhmat Sani Universitas Dian Nuswantoro
  • Asih Rohmani Universitas Dian Nuswantoro
  • Dwi Puji Prabowo Universitas Dian Nuswantoro
  • Dewi Pergiwati Universitas Dian Nuswantoro
  • Rama Aria Megantara Universitas Dian Nuswantoro
  • Iswahyudi Iswahyudi Dinas Komunikasi dan Informatika Provinsi Jawa Tengah

DOI:

10.33395/sinkron.v9i4.15292

Keywords:

Named Entity Recognition, Latent Dirichlet Allocation, Text Mining, Public Services, E-Government

Abstract

With the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications.

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How to Cite

Umam, A. K. ., Alzami, F., Sani, R. R. ., Rohmani, A. ., Prabowo, D. P. ., Pergiwati, D. ., Megantara, R. A. ., & Iswahyudi, I. (2025). Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(4), 1878-1888. https://doi.org/10.33395/sinkron.v9i4.15292

Issue

Vol. 9 No. 4 (2025): Articles Research October 2025

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Articles

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Copyright (c) 2025 Ahmad Khotibul Umam, Farrikh Alzami, Ramadhan Rakhmat Sani, Asih Rohmani, Dwi Puji Prabowo, Dewi Pergiwati, Rama Aria Megantara, Iswahyudi Iswahyudi

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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