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Iqbal Maulana
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Bulletin of Computer Science Research

 1 
Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Ulasan Kompas.id pada Data Tidak Seimbang Muhammad Ardana; Rini Mayasari; Iqbal Maulana
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.929

Abstract

The rapid advancement of digital technology and the increasing use of mobile devices have driven the widespread adoption of digital news applications, including Kompas.id. User reviews on the Google Play Store represent an important data source for understanding user satisfaction and emerging issues; however, the large volume of reviews makes manual analysis inefficient. Therefore, this study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying Kompas.id user reviews into positive, neutral, and negative sentiments. The research employs the Knowledge Discovery in Databases (KDD) framework, which includes web scraping, text preprocessing, lexicon-based sentiment labeling, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification and evaluation stages. The dataset consists of 1,023 cleaned reviews after data preprocessing. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that Naïve Bayes achieves an accuracy of 72%, while SVM outperforms it with an accuracy of 80%, reflecting its stronger ability to handle high-dimensional and sparse textual feature spaces. Word cloud visualization reveals that positive sentiments are mainly associated with content quality, whereas negative sentiments are dominated by subscription-related issues and technical problems. Based on these findings, SVM is recommended as a more effective algorithm for sentiment analysis of digital news application reviews.
Co-Authors Abdul Aziz Abdul Rochim Aditya Arif Wicaksono Aditya Dwi Raja Kamansastra2 Agung Saputra Agus Gilang Hermawan Ainun Safitri Allkaf Movlexis Adam Amelia Nurrahmadina Andi Rosa Angina Sandy Arga Sutrisna Arya Aji Putra Pangestu Azhari Cahyadi Dahlan, Zaini Daris Fauzaan Deni Exka Saputra Desi Kristina Deviana Eka Putri Dewi Amrih Dewi Tresnawati Didik Aribowo Dino Rimantho Fiki Romadhon Finisica Dwijayati Patrikha Firza Angga Malarangeng Fitriah Garno Garno Gelen Veranda Deanda Gultom, Ronal Gusta Marlinda Hafsah Hijroh Tamamil Gina Ihsan Satya Adi Nugraha Iis Istiqomah Jaman, Jajam Haerul Jayus Jayus Khaila Mardina Fauziah Khoirul Sholeh Kusuma Agdhi Rahwana Ludi Rahmanto Luky Setiyawan Lulu Nurul Khasanah Mohammad Ilhamsyah Akbar Muhammad Ardana Muhammad Khaesar Juniardi Muhammad Syawal Karo-Karo Muhammad Yasin Muhammad Yusuf Habibi Muhammad Zidan Fahreza Mukhlidin Mukhlidin Muslikhun, Alfin Mustika Sari Mustofa Abi Hamidn Nanda Kamila Azzahra Nita Nurmawati Nur Yulianti Hidayah, Nur Yulianti Nuril Khoirunisa Izzati Nurmawati Nur’aini Oman Komarudin Popy Nurfadilah Prasta Mahrifatika PURWANTI PURWANTI Puspita Budiarti Rachmat Mudiyono Ria Amelia Rini Mayasari Roland Vincent Rowiyani Rowiyani Salminawati Setiyawan, Luky Shafa Yuniar Yasmin Sri Mulyo Bondan Respati Sugianti Suwadi Syarifah, Atika Nur Syukri Kurniawan Nasution Theodorus Yoseph Tatabuang Lejap Vector Anggit Pratomo Virgaria Zuliana Wahid Hasim Wahyu Eka Candra Wanda Pratiwi Willy Rahim Marpaung Yunita Yunita