Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Infotekmesin
Alfina Latifa Maysara
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Infotekmesin

 1 
Optimalisasi Akurasi dan Stabilitas Analisis Sentimen Ulasan E-Commerce Indonesia melalui Fine-Tuning Transformer IndoBERT Alfina Latifa Maysara; Muljono
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3037

Abstract

The rapid growth of e-commerce in Indonesia increases the need for sentiment analysis to accurately understand customer perceptions. This study evaluates the effectiveness of the Transformer-based IndoBERT model for sentiment classification on Indonesian e-commerce reviews and compares its performance with four RNN architectures (LSTM, GRU, BiLSTM, and BiGRU). The PRDECT-ID dataset containing 5,400 reviews was processed through preprocessing, an 80:20 data split, RNN training using 5-Fold Cross Validation, and IndoBERT fine-tuning under a hold-out scheme. Unlike previous studies that focused solely on RNN models with a maximum accuracy of 90.7%, this work expands the evaluation by integrating a Transformer-based approach. Results show that IndoBERT achieves 98.52% accuracy and F1-weighted score, outperforming the best RNN models by approximately 0.94–0.95. Paired T-Test and Wilcoxon tests yield p < 0,05, confirming that the performance improvements are statistically significant. IndoBERT demonstrates greater stability and effectiveness for Indonesian sentiment analysis.
Co-Authors Muljono