Enhancing Sentiment Classification Performance on Tentang Anak Application Reviews Using Optimized Support Vector Machine

Riska Aryanti; Eka  Fitriani; Royadi Royadi; Dian  Ardiansyah

doi:10.58602/jaiti.v4i2.271

Riska Aryanti ^{(Corresponding Author)} Universitas Bina Sarana Informatika
Eka Fitriani Universitas Bina Sarana Informatika
Royadi Universitas Bina Sarana Informatika
Dian Ardiansyah Universitas Bina Sarana Informatika

DOI: https://doi.org/10.58602/jaiti.v4i2.271

Keywords: Sentiment Analysis, Support Vector Machine, Hyperparameter Optimization, Tentang Anak Application, Text Mining, Machine Learning

Abstract

The increasing use of parenting and child development applications has generated a large volume of user reviews containing valuable insights regarding application quality, usability, and user satisfaction. One of the widely used applications in Indonesia is Tentang Anak: Kehamilan & Anak. However, manually analyzing these reviews is inefficient due to the large amount of unstructured textual data. Therefore, this study aims to enhance sentiment classification performance on user reviews of the Tentang Anak: Kehamilan & Anak application using an optimized Support Vector Machine (SVM) model. The dataset consisted of user reviews collected from application platforms, which were processed through several text preprocessing stages, including cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using polarity scores to classify reviews into positive and negative sentiments. The proposed model was evaluated using different test size scenarios (0.1, 0.2, 0.3, and 0.4) and random state configurations to identify the optimal parameter setting. Experimental results demonstrate that the best performance was achieved at a test size of 0.1 with random state 0, obtaining an accuracy of 89.8%, precision of 91.7%, recall of 55.0%, and F1-score of 68.8%. The findings indicate that the optimized SVM model is effective in classifying sentiment in reviews of the Tentang Anak: Kehamilan & Anak application, particularly in achieving high precision and classification stability across multiple testing scenarios. Furthermore, the study highlights the importance of parameter optimization in improving sentiment analysis performance for user-generated textual data.

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