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All Journal ComEngApp : Computer Engineering and Applications Journal ELKHA : Jurnal Teknik Elektro JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) CCIT (Creative Communication and Innovative Technology) Journal Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Engagement: Jurnal Pengabdian Kepada Masyarakat
Kanda Januar Miraswan
Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : journal of applied informatics and computing

 1 
Performance Comparison of Naive Bayes and Support Vector Machine Methods in Music Genre Classification Based on Audio Signal Feature Extraction Using Mel-Frequency Cepstral Coefficients (MFCC) Naserwan, Kevin Putrayudha; Miraswan, Kanda Januar; Utari, Meylani
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11770

Abstract

Music genre classification has gained increasing attention with the emergence of digital music platforms. One of the relevant features extracted from audio signals is Mel-Frequency Cepstral Coefficients (MFCC), which is widely recognized as an effective technique. MFCC features are extracted at the frame level and aggregated at the clip level to represent each music track, making them suitable for audio-based classification tasks. This study applies Naïve Bayes and Support Vector Machine (SVM) algorithms for classification using the GTZAN dataset consisting of 1,000 audio files from 10 music genres, each with a duration of 30 seconds. The performance of these methods is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM demonstrates superior performance, achieving an accuracy of 95.25% compared to 50.37% for Naïve Bayes. This performance gap can be attributed to SVM’s ability to model non-linear decision boundaries and effectively handle high-dimensional MFCC feature spaces. The main contribution of this study lies in the systematic evaluation of multiple SVM kernel configurations and parameter settings, providing empirical insights into the robustness of classical machine learning methods for MFCC-based music genre classification. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction.
Factor Analysis Influencing Review Scores on E-Commerce Platforms Using Machine Learning Meylani Utari; Kanda Januar Miraswan; Anna Dwi Marjusalinah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12631

Abstract

In recent years, the rapid growth of e-commerce has made customer reviews an important indicator of product quality, service performance, and customer satisfaction. Review scores play a crucial role in influencing purchasing decisions and evaluating overall shopping experiences on e-commerce platforms. This research aims to analyze the main factors influencing customer review scores by integrating logistics, transaction, and product-related variables using a machine learning approach. The dataset consists of various e-commerce transaction attributes, including delivery information, payment details, and product characteristics. A Random Forest classifier is employed to predict customer review scores and to identify dominant influencing factors through feature importance analysis. The results show that logistics-related factors, particularly delivery time, are the most influential variables affecting review scores, followed by payment value, freight value, and product price. This study also emphasizes the significance of understanding how models work and their real-world applications, offering useful guidance on enhancing logistics efficiency, ensuring clear transaction records, and maintaining high standards of product information. Product attributes such as description length, weight, and physical dimensions also contribute significantly to customer satisfaction. By combining predictive capability and interpretability, this research provides valuable insights for sellers and e-commerce platform managers to improve service quality, optimize logistics performance, and enhance customer satisfaction.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah Ahmad Fali Oklilas Ahmad Gustano Akbar, Muhammad Rafi Al Ashri, Muhammad Rizky Al Farissi Alvi Syahrini Utami Anita Desiani Anna Dwi Marjusalinah Anna Dwi Marjusalinah Ardani, Vanya Terra Arhami, Muhammad Ari Wedhasmara Arief Wijaya Auzan Lazuardi Buchari, Muhammad Ali Darmawahyuni, Annisa Dellin Irawan Desty Rodiah Dewi Sartika Dhiya Fairuz Dian Palupi Rini Eni Triningsih Firdaus Firdaus Hidayatullah, Altundri Wahyu Irawan, Dellin Junia Kurniati Junia Kurniati, Junia Kartika Rahmayani M. Fachrurrozi . Marjusalinah, Anna Dwi Mastura Diana Marieska Meylani Utari Mgs Afriyan Firdaus Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Husein Nashr Muhammad Ukkasyah Naserwan, Kevin Putrayudha Novi Yusliani Nur ‘Azah Osvari Arsalan Redo Jufarda Rendyansyah Samsuryadi Samsuryadi Sarmayanta Sembiring septi ana Sri Indra Maiyanti Vanya Terra Ardiani Wiwik Anum Puspita Yogi Pratama YUNITA Yunita Yunita