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Eka Fitriani
Universitas Bina Sarana Informatika
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Journal : journal of artificial intelligence and technology information

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Enhancing Sentiment Classification Performance on Tentang Anak Application Reviews Using Optimized Support Vector Machine Riska Aryanti; Eka Fitriani; Royadi Royadi; Dian Ardiansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.271

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.
Co-Authors Abdillah Abdillah Abdussomad, Abdussomad Aditia, M. Agus Herianto Alta, Ulik Aminah Aminah Apridonal M, Yori Aprilia, Widya Aprilianicahyati, Aprilianicahyati Ardiana Priharwanti Arina, Yudi Asharsyira, Almeyda Atang Saepudin Azwir Nasir Baiti, Nurul Dahlia Dahlia Dede Firmansyah Saefudin Dede Saefudin Dewi, Lucia Vita Inandha Dian Ardiansyah Dila Delia Fadilah Duwi Cahya Putri Buani Dzulfikar, Dzulfikar Emrinaldi Nur, Emrinaldi Erna Listyaningsih Galih Pratiwi, Galih Hafidz, Putra Hakim, Ahmad Ibrahim Ibrahim Ilham Kurniawan Lestari, Nadya Citra Mahsup, Mahsup Mariaty Ibrahim Mastur Mastur Mizan habibi, Moh Muhamad Tabrani Muhammad Faittullah Akbar Muhammad Tabrani, Muhammad Muharramsyah, Rambang Nadirah, Yahdinil Firda Negsi, Ratika Nindi Vaulia Puspita, Nindi Vaulia Onny Indriani Orowala, Anggriani R, Herowati Ramadhiani, Aninditha Rachmah Richardus Eko Indrajit Rifky Permana Riska Aryanti Risky Cahyani, Fhadilla Rizal Amegia Saputra Royadi - Royadi, Royadi Rr. Vita Nurlatif SALMAN ALFARIZI Sativa, Risya Oriza Sintayana Muhardini Siregar, Iqbal Kamil Sopian sopian Suhardi Suhardi Swahip, Swahip Syafril Syafril Syura, Asvia Tari, Mayang Utami, Anita Syafira Vera Mandailina, Vera Widya Apriliah Widya Apriliah, Widya Wijaya, Mario Wuryanti, Lestari Yuliari, Kartika Yuniarti Yuniarti Zaenudin Zaenudin