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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia
Iskoko, Angga
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Computer Science & IT Education Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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Hyperparameter Optimization Of IndoBERT Using Grid Search, Random Search, And Bayesian Optimization In Sentiment Analysis Of E-Government Application Reviews Iskoko, Angga; Tahyudin, Imam; Purwadi, Purwadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4897

Abstract

User reviews on Google Play Store reflect satisfaction and expectations regarding digital services, including E-Government applications. This study aims to optimize IndoBERT performance in sentiment classification through fine-tuning and hyperparameter exploration using three methods: Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on Sinaga Mobile app reviews, evaluated using accuracy, precision, recall, F1-score, learning curve, and confusion matrix. The results show that Grid Search with a learning rate of 5e-5 and a batch size of 16 provides the best results, with an accuracy of 90.55%, precision of 91.16%, recall of 90.55%, and F1-score of 89.75%. The learning curve indicates stable training without overfitting. This study provides practical contributions as a guide for improving IndoBERT in Indonesian sentiment analysis and as a foundation for developing NLP-based review monitoring systems to enhance public digital services.
Co-Authors Imam Tahyudin Purwadi Purwadi Purwadi Purwadi