A Hybrid VADER–IndoBERT Framework for Robust Sentiment Analysis of Long and Ambiguous Indonesian Texts

Margareta Valencia Suci Handayani (Universitas Dian Nuswantoro,)
Ruri Suko Basuki (Universitas Dian Nuswantoro)
Muljono (Universitas Dian Nuswantoro)
Raden Arief Nugroho (Universitas Dian Nuswantoro)
Dhendra Maruhto (Universitas Muhammadiyah Semarang)
Yo Ceng Giap (Universitas Dian Nuswantoro)
Deshinta Arrova Dewi (INTI International University)

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Vol 10 No 4 (2026): August 2026 (in progress)

The rapid expansion of digital learning platforms has increased the reliance on user-generated reviews for service evaluation and quality monitoring. However, sentiment analysis of Indonesian reviews remains challenging due to the prevalence of long sentences, mixed sentiments, and ambiguous linguistic expressions. This study introduces a Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts. A dataset of 4,904 Ruangguru application reviews was collected through web scraping and processed using a hybrid pipeline consisting of preprocessing, translation-based silver-standard sentiment labeling with VADER, and class balancing via Random Oversampling (ROS). The IndoBERT classifier was evaluated against a Bidirectional Long Short-Term Memory (BiLSTM) baseline. Experimental results show that IndoBERT achieved 90.9% accuracy, outperforming BiLSTM at 86.4%, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues. These findings highlight the effectiveness of integrating lexicon-based and Transformer-based approaches to achieve more robust sentiment analysis on linguistically complex Indonesian texts.