International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 5, October 2023, pp. 5641~5652 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5641-5652  5641 Journal homepage: http://ijece.iaescore.com Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model Nuzulul Khairu Nissa, Evi Yulianti Faculty of Computer Science, University of Indonesia, Depok, Indonesia Article Info ABSTRACT Article history: Received Dec 8, 2022 Revised Feb 2, 2023 Accepted Feb 10, 2023 Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN- XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively. Keywords: Convolutional neural network Customer review IndoBERT Multi-label text classification Word2Vec This is an open access article under the CC BY-SA license. Corresponding Author: Evi Yulianti Faculty of Computer Science, University of Indonesia Depok, West Java, Indonesia Email: evi.y@cs.ui.ac.id 1. INTRODUCTION Customer review is a critical resource to discover useful information about user experiences on a particular product (or service). Such information is important for a company to help them making a good decision about their products. A review text may contain user’s opinion about several aspects of a product, where each aspect may accept different sentiments from the user. Here is an example of Indonesian customer review of hotel experiences that contains different sentiments for different aspects of hotel: “kamarnya nyaman dan bersih, tetapi TV nya terlalu tinggi jadi kamu tidak bisa nonton” (“The room is comfortable and clean, but the TV is too high, so you can’t watch it”). In that review, the aspect of “cleanliness” has a positive sentiment, but the aspect of “TV” as one of the hotel’s facilities has a negative sentiment. Aspect category detection or aspect classification is one of the subtasks from aspect-based sentiment analysis (ABSA) [1]. For the aspect classification task, the aspects contained in the text review are identified and the polarity of each aspect is then determined by sentiment classification. The results of this system are