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Comparative Analysis of the Performance of K-Nearest Neighbor (K-NN) and Naive Bayes Algorithms on User Satisfaction Levels of the Tokopedia Application Novelan, Muhammad Syahputra; Iqbal, Muhammad
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.37

Abstract

The rapid advancement of digital technology has significantly influenced the development of e-commerce platforms in Indonesia, where Tokopedia stands out as one of the most popular and widely used online marketplaces. As user expectations continue to increase, understanding and measuring user satisfaction has become essential for ensuring service quality and maintaining customer loyalty. This study aims to perform a comparative analysis of the performance of two machine learning classification algorithms—K-Nearest Neighbor (K-NN) and Naive Bayes—in analyzing and predicting user satisfaction levels toward the Tokopedia application. The dataset used in this study was obtained from a combination of online reviews and structured survey responses from active Tokopedia users. The research methodology includes several stages: data collection, text preprocessing (tokenization, stop-word removal, and stemming), feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) technique, and model implementation using the two algorithms. Both models were evaluated using key performance metrics such as accuracy, precision, recall, and F1-score. The experimental results indicate that the K-NN algorithm achieved superior performance compared to Naive Bayes, demonstrating higher accuracy and better consistency in classifying user sentiments into “satisfied” and “dissatisfied” categories. The K-NN model proved to be more effective in handling diverse and nonlinear data patterns derived from user-generated reviews. Meanwhile, Naive Bayes, although computationally efficient, showed limitations in processing complex text dependencies. The findings of this research highlight the importance of selecting appropriate machine learning algorithms for user satisfaction analysis. Furthermore, the study contributes to the broader understanding of sentiment-based evaluation models in e-commerce platforms and provides valuable insights for Tokopedia and similar companies in enhancing customer experience and service improvement strategies.
Co-Authors ', Khairunnisa , Arpan Adli Abdillah Nababan Afif Yasri Amin, Muhammad Aminuddin Indra Permana Andysah Putera Utama Siahaan Antoni, Robin Anugrah, Maisya Fitri Aria Dhanu Tirta Arpan Aurelia, Cindy Aisha Ayumi Kartika Sari Bayu Angga Wijaya Daniel Panjaitan Darmeli Nasution Datin, Maha Valne Defri Abdul Majid Nasution Dian Kurnia Fachri, Barany Fajri Razak Fathia, Aulia Ukhti Febby Sittah Gunawan Fitri Anugrah, Maisya Gunawan, Andri Harahap, Nur Azizah Hardinata, Rio Septian Harefa, Ade May Luky Haryadi, Patrialman Heri Eko Rahmadi Putra Ibnu Gunawan Ilka Zufria IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khumairoh, Annisa Limbong, Yohannes France Lubis, Syaiful Rahman Mestika, Dani Mufida Padilla, Eva Muhammad Iqbal Muhammad Rizki Muhammad Wahyudi Muhammad Zen, Muhammad Muhardi Saputra Nasution, Indra Padilla, Eva Mufida Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Rambe, Siska Mayasari Ramlan Marbun Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rizal, Chairul Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Safii, Aidul Safi’i, Aidul Sari Harahap, Nurlina Sella Monika Br Tarigan Selvida, Desilia Septiansyah, Yudha Setiawan, Ahmad Deni Setiawan, Albin Simanullang, Rahma Yuni Siregar, Andree Rizky Yuliansyah Sitepu, Andri Ismail Sitepu, Nabila Putri Br Siti Aisyah Sitorus , Zulham Solly Aryza Suhendar - Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputra, Zulfahmi Syahputri, Maulisa Syahri, Rahma Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Zulfahmi Syahputra Zulfahmi Syahputra Zulfahmi Syahputra