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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application Asri, Yessy; Kuswardani, Dwina; Suliyanti, Widya Nita; Manullang, Yosef Owen; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp677-688

Abstract

PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.
Word embedding for contextual similarity using cosine similarity Asri, Yessy; Kuswardani, Dwina; Sari, Amanda Atika; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1170-1180

Abstract

Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated data becomes 828. Data augmentation aims to evaluate the performance of models by using synonyms and rearranging text while maintaining meaning and structure. The approach used is to label the opinion text based on the cosine similarity calculation of the embedding token from the IndoBERT model. Then, the IndoBERT model is applied to classify the reviews. The experimental results show that the approach of using IndoBERT to classify SI and IT topics based on contextual similarity achieves 90% accuracy based on the confusion matrix. These positive results show the great potential of using transformer-based language models, such as IndoBERT, to support the analysis of comments and related topics in Indonesian.