Indonesian Sentiment Analysis towards MyPertamina Application Reviews by Utilizing Machine Learning Algorithms

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Fiddin Yusfida A'la


This paper is a report of experiment analysis on sentiment analysis in application review that explored the methods and the data. Application review contains a large amount of raw data that has been published by users in the form of text, image, audio, and video. The data can be converted into valuable information by using sentiment analysis. In this work, around 5000 Indonesian review in MyPertamina google play application are analyzed. The goal of this study was to investigate the effectiveness of using sentiment analysis to extract valuable insights from application reviews. Some techniques were applied during this work, such as data collection, pre-processing, feature extraction, TF-IDF text representation, machine learning modelling, and evaluation phase. The machine learning algorithms that we used are Linear Support Vector Classification (Linear SVC) and Multinomial Naïve Bayes (Multinomial NB). The result shows both machine learning models present good performance in this data. The accuracy of Multinomial NB reaches 95%, while Linear SVC obtains 96% of accuracy. The results of the experiment suggest that both Linear SVC and Multinomial NB are well-suited for sentiment analysis tasks on Indonesian language data. Future work could include expanding the dataset to include reviews from a broader range of applications, or evaluating the performance of additional machine learning algorithms. In addition, word cloud analysis also performed in this experiment. The word cloud shows that positive and negative sentiment present some popular words which appear inside the review. It would also be interesting to conduct a deeper analysis of the word cloud results to identify common themes and trends in the positive and negative sentiments expressed in the reviews

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How to Cite
A’la, F. (2022). Indonesian Sentiment Analysis towards MyPertamina Application Reviews by Utilizing Machine Learning Algorithms. Journal of Informatics Information System Software Engineering and Applications (INISTA), 5(1), 80-91.


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