DOI : https://doi.org/10.18517/ijaseit.15.3.20602

Automated Label Extraction for Sentiment Analysis in Indonesian Text

Khairul Imtihan (1), Lalu Mutawalli (2), Wire Bagye (3), Ahmad Tantoni (4)
(1) Information Systems Department, STMIK Lombok, Praya, Lombok Tengah, Indonesia
(2) Information Systems Department, STMIK Lombok, Praya, Lombok Tengah, Indonesia
(3) Informatics Engineering Department, STMIK Lombok, Praya, Lombok Tengah, Indonesia
(4) Informatics Engineering Department, STMIK Lombok, Praya, Lombok Tengah, Indonesia
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[1]
K. Imtihan, Lalu Mutawalli, Wire Bagye, and Ahmad Tantoni, “Automated Label Extraction for Sentiment Analysis in Indonesian Text”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 718–728, Jun. 2025.
Citation Format :
Sentiment analysis plays a crucial role in helping businesses understand consumer perceptions, improve decision-making, and enhance customer satisfaction. However, large-scale sentiment classification in Indonesian-language texts remains a challenge due to the scarcity of labeled datasets and limited computational resources. This study introduces an automated sentiment labeling approach that integrates chunking and Rule-Based Machine Translation (RBMT) to optimize efficiency and accuracy. Unlike Self-Supervised Learning (SSL), Active Learning (AL), and Transformer-based models (e.g., BERT), which demand extensive labeled data and high-performance computing, the proposed method offers a scalable and resource-efficient solution. A dataset comprising 225,000 entries was preprocessed and segmented into smaller chunks to enhance processing efficiency. Seven classification algorithms, Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, and Multilayer Perceptron (MLP), were employed for performance evaluation. Results show that MLP and Random Forest achieve the highest accuracy, ranging from 0.886 to 0.900, confirming their effectiveness for sentiment classification. Furthermore, the proposed Chunking + RBMT method achieves 89.9% accuracy, outperforming SSL (87.3%) and AL (86.5%), while maintaining significantly lower computational requirements compared to Transformer-based models (90.5%). This study demonstrates the effectiveness of Chunking in reducing computational overhead while maintaining high classification accuracy. Overall, the findings validate the proposed approach as a practical alternative for large-scale sentiment classification in low-resource settings, with strong potential to improve automated sentiment analysis in the Indonesian language.

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