Perbandingan Decision Tree, KNN, dan Naive Bayes pada Klasifikasi Mood Musik Menggunakan Dataset Emotion Kaggle

Authors

  • Miftakhur Rahman Sistem Informasi, Universitas Semarang
  • Muhammad Arham Lutfi Sistem Informasi, Universitas Semarang
  • Nur Wakhidah Teknik Informatika, Universitas Semarang https://orcid.org/0000-0002-6486-8939

DOI:

https://doi.org/10.33020/saintekom.v16i1.1019

Keywords:

music mood, audio features, MFCC, Decision Tree, KNN, Naive Bayes

Abstract

The classification of music mood characteristics is a crucial instrument in Music Information Retrieval (MIR) systems to support recommendation technology and AI-based emotion analysis. This study aims to evaluate and compare the performance of three classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes. The dataset utilized is sourced from the Kaggle Emotion Dataset, comprising 1,440 audio files. The feature extraction process was conducted using the Librosa library to capture acoustic parameters, including Mel-Frequency Cepstral Coefficients (MFCC), Delta-MFCC, Chroma, Spectral Contrast, Spectral Centroid, Spectral Bandwidth, and Tempo. All features were normalized using StandardScaler and distributed into training and testing sets with an 80:20 ratio. Based on the experimental results, the K-Nearest Neighbors algorithm demonstrated the most superior performance with an accuracy of 71.52%. Meanwhile, the Decision Tree algorithm achieved an accuracy of 54.16%, and Naive Bayes obtained 53.47%. The primary contribution of this research is the empirical evidence of the effectiveness of distance-based algorithms in identifying emotional patterns within multidimensional audio data. These findings provide a robust methodological reference for the future development of music emotion recognition systems

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Author Biographies

Miftakhur Rahman, Sistem Informasi, Universitas Semarang

Miftakhur Rahman is a student at Semarang University with a study program in Information Systems, who created or is the main author of this research

Muhammad Arham Lutfi, Sistem Informasi, Universitas Semarang

Muhammad Arham Lutfi is a student at the University of Semarang with an information systems study program who is interested in and helped in the creation of this research.

Nur Wakhidah, Teknik Informatika, Universitas Semarang

Lecturer at the Faculty of Information and Communication Technology, Universitas Semarang.

References

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Published

31-03-2026

How to Cite

Rahman, Miftakhur, Muhammad Arham Lutfi, and Nur Wakhidah. 2026. “Perbandingan Decision Tree, KNN, Dan Naive Bayes Pada Klasifikasi Mood Musik Menggunakan Dataset Emotion Kaggle”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 16 (1):15-26. https://doi.org/10.33020/saintekom.v16i1.1019.

Issue

Vol. 16 No. 1 (2026): Maret 2026

Section

Articles

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Copyright (c) 2026 Miftakhur Rahman, Muhammad Arham Lutfi, Nur Wakhidah

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