Pendekatan Hybrid Transformer-BiLSTM Berbasis Multi-Task Learning untuk Klasifikasi dan Prioritisasi Pengaduan Pelanggan

Reifal Hafizh Satria (Universitas Dian Nuswantoro, Semarang)
Arry Maulana Syarif (Universitas Dian Nuswantoro, Semarang)

Building of Informatics, Technology and Science
Vol 8 No 2 (2026): September 2026

The rapid growth of e-commerce platforms has generated a massive volume of user reviews that can be utilized to understand customer issues. However, the informal nature of user-generated text, the diversity of complaint types, and the need to identify both complaint categories and their handling priorities make automatic complaint classification a challenging task. Therefore, this study proposes a Hybrid Transformer–BiLSTM model based on Multi-Task Learning (MTL) to simultaneously classify complaint categories and priority levels through a shared feature representation. The dataset was collected by web scraping Google Play Store reviews of the Tokopedia application, resulting in 100,000 raw reviews. After the preprocessing stage, 42,929 reviews were retained for model development. The labeling process employed a rule-based pseudo-labeling approach involving keyword identification, weighted scoring, and ambiguity resolution to generate category and priority labels. The proposed model integrates IndoBERT as the transformer encoder with BiLSTM to capture contextual representations and sequential dependencies in Indonesian text. Experimental results show that the proposed model achieved 96.17% accuracy, with a weighted F1-score of 0.9617 and a macro F1-score of 0.9353 for complaint category classification. For complaint priority classification, the model achieved 80.04% accuracy, with a weighted F1-score of 0.7437 and a macro F1-score of 0.4236. The lower performance on the priority classification task indicates that the model is still affected by class imbalance, resulting in better recognition of majority classes than minority classes. In addition, the quality of the generated labels depends on the predefined rules used in the pseudo-labeling process. Nevertheless, the proposed approach demonstrates strong potential as a foundation for developing an automated complaint classification and prioritization system for e-commerce customer service. The contribution of this study lies in the implementation of a Hybrid IndoBERT–BiLSTM architecture based on Multi-Task Learning for the simultaneous classification of customer complaint categories and priorities, as well as the provision of an Indonesian Tokopedia complaint dataset that has undergone a pseudo-labeling process as a reference for future research.