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All Journal International Journal of Machine Learning (IJOML)
Hidayatulah Himawan
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia
Computer Science & IT Decision Sciences, Operations Research & Management

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Benchmarking Modern Optimizers for IndoBERT-Based Sentiment Analysis on Indonesian Gojek Reviews Randi Rizal; Hidayatulah Himawan
International Journal of Machine Learning (IJOML) Vol. 1 No. 1 (2026): IJOML Volume 1, Number 1, June 2026
Publisher : APJIKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ijoml.v1i1.5

Abstract

User reviews on platforms like Gojek serve as critical data for business intelligence, necessitating robust automated sentiment analysis models. While IndoBERT is the standard architecture for Indonesian natural language processing, the comparative impact of emerging optimizers on its performance remains underexplored, as most existing studies default to AdamW without investigating modern alternatives. This research comprehensively benchmarks five optimizers—AdamW, Muon, AdaMuon, Lion, and Sophia—by fine-tuning IndoBERT on 29,851 Indonesian Gojek reviews to identify the most effective training strategy. The study evaluates classification metrics alongside computational efficiency indicators, including training duration and peak memory usage. Empirical results demonstrate that AdamW, AdaMuon, and Lion achieve statistically equivalent superior performance, attaining an average accuracy of 91.6% and an F1-macro of 91.5%. Conversely, Muon and Sophia exhibit slightly lower predictive capability with higher resource demands. Regarding computational cost, AdamW and Lion provide the optimal balance of rapid convergence and memory efficiency, whereas Sophia demands significantly higher VRAM and matrix-based optimizers like Muon extend training duration. These findings confirm that AdamW remains the most robust and efficient choice for analyzing informal Indonesian text, indicating that the complex update mechanisms of newer optimizers do not yield necessary marginal gains for this specific classification task.
Co-Authors Randi Rizal