Benchmarking IndoBERT and Transformer Models for Sentiment Classification on Indonesian E-Government Service Reviews 1751 2427
DOI:
https://doi.org/10.26623/transformatika.v23i1.12095Kata Kunci:
Analisis Sentimen, Pemrosesan Bahasa Alami, Indobert, Model Transformator, Penambangan DataAbstrak
The rapid adoption of e-government services in Indonesia has increased the importance of understanding public sentiment toward digital platforms. This study presents a comparative analysis of five models—IndoBERT, mBERT, XLM-R, CNN, and BiLSTM—for sentiment classification on user reviews of NEWSAKPOLE, a public service application for vehicle tax and licensing. A custom dataset of 11,000+ reviews was scraped from the Google Play Store and labeled using a hybrid rating-based and manual validation approach. Each model was evaluated using accuracy, precision, recall, and F1-score. IndoBERT achieved the highest performance with an F1-score of 0.882, outperforming multilingual and classical deep learning models. Confusion matrix analysis showed that transformer-based models were more effective in detecting neutral and mixed sentiments, while CNN and BiLSTM struggled with misclassification. The results highlight IndoBERT's robustness in low-resource sentiment analysis and its potential to enhance public service monitoring and policy feedback mechanisms in Indonesian digital governance.
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[1] D. Marutho, Muljono, S. Rustad, and Purwanto, Optimizing aspect-based sentiment analysis using sentence embedding transformer, bayesian search clustering, and sparse attention mechanism, J. Open Innov. Technol. Mark. Complex., vol. 10, no. 1, p. 100211, 2024, doi: 10.1016/j.joitmc.2024.100211.
[2] C. Shaw, P. LaCasse, and L. Champagne, Exploring emotion classification of indonesian tweets using large scale transfer learning via IndoBERT, Soc. Netw. Anal. Min., vol. 15, no. 1, Mar. 2025, doi: 10.1007/s13278-025-01439-6.
[3] H. Imaduddin, F. Y. A’la, and Y. S. Nugroho, Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach, Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, 2023, doi: 10.14569/ijacsa.2023.0140813.
[4] B. V. Kartika, M. J. Alfredo, and G. P. Kusuma, Fine-Tuned IndoBERT Based Model and Data Augmentation for Indonesian Language Paraphrase Identification, Rev. Intell. Artif., vol. 37, no. 3, pp. 733–743, Jun. 2023, doi: 10.18280/ria.370322.
[5] N. K. Nissa and E. Yulianti, Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model, Int. J. Electr. Comput. Eng. IJECE, vol. 13, no. 5, p. 5641, Oct. 2023, doi: 10.11591/ijece.v13i5.pp5641-5652.
[6] S. Aras, M. Yusuf, R. Y. Ruimassa, E. A. B. Wambrauw, and E. B. Pala’langan, Sentiment Analysis on Shopee Product Reviews Using IndoBERT, J. Inf. Syst. Inform., vol. 6, no. 3, pp. 1616–1627, Sep. 2024, doi: 10.51519/journalisi.v6i3.814.
[7] F. Iscus and A. S. Girsang, Sentiment Analysis of COVID-19 Public Activity Restriction (PPKM) Impact using BERT Method, Int. J. Eng. Trends Technol., vol. 70, no. 12, pp. 281–288, Dec. 2022, doi: 10.14445/22315381/ijett-v70i12p226.
[8] Ibadurrohman Irfan Fatani, Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches, J. Electr. Syst., vol. 20, no. 4s, pp. 1028–1035, Apr. 2024, doi: 10.52783/jes.2147.
[9] S. Pecar, M. Simko, and M. Bielikova, Sentiment Analysis of Customer Reviews : Impact of Text Pre-processing, 2018 World Symp. Digit. Intell. Syst. Mach. DISA, pp. 251–256, 2018, doi: 10.1109/DISA.2018.8490619.
[10] R. K. Bania, COVID-19 Public Tweets Sentiment Analysis using TF-IDF and Inductive Learning Models Handwritten Assamese Character Recognition using Texture and Diagonal Orientation features with Artificial Neural Network View project COVID-19 Public Tweets Sentiment An, no. December, 2020, [Online]. Available: https://www.researchgate.net/publication/346572350
[11] D. R. Firmansyah and E. Lestariningsih, Analisis Sentimen Ulasan Aplikasi Smart Campus Unisbank di Google Playstore Menggunakan Algoritma Naive Bayes, J. JTIK J. Teknol. Inf. Dan Komun., vol. 8, no. 2, pp. 498–507, Apr. 2024, doi: 10.35870/jtik.v8i2.1882.
[12] D. Marutho, M. Muljono, R. Supriadi, and P. Purwanto, Optimizing Aspect Term Extraction and Sentiment Classification through Attention Mechanism and Sparse Attention Techniques, Int. J. Intell. Eng. Syst., vol. 17, no. 5, pp. 1004–1015, Oct. 2024, doi: 10.22266/ijies2024.1031.75.
[13] Primanda Sayarizki, Hasmawati, and H. Nurrahmi, Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates, Indones. J. Comput. Indo-JC, vol. Vol. 9 No. 2, pp. 61-72 Pages, Aug. 2024, doi: 10.34818/INDOJC.2024.9.2.934.
[14] E. Arif, S. Suherman, and A. P. Widodo, Predicting Stock Prices of Digital Banks: A Machine Learning Approach Combining Historical Data and Social Media Sentiment from X, Ingénierie Systèmes Inf., vol. 30, no. 3, Mar. 2025, doi: 10.18280/isi.300313.
[15] M. Rosidin, M. F. Gustafi, and S. A. Pratiwi, Optimizing nazief adriani’s stemmer algorithm in detecting indonesian word errors using sastrawi, no. 3.
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Hak Cipta (c) 2025 Dhendra, Victor Gayuh Utomo
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