Sistem Klasifikasi Genre Musik Berdasarkan Ritme dan Frekuensi Menggunakan Ekstraksi Fitur MFCC dan F0 dengan Algoritma Decision Tree
Kata Kunci:
Algoritma Decision Tree, Dataset GTZAN, Ekstraksi Fitur, Klasifikasi Genre Musik, Mel Frequency Cepstral Coefficient (MFCC)Abstrak
Musik adalah salah satu bentuk hiburan yang sangat diminati dan esensial dalam kehidupan manusia. Dalam konteks industri musik saat ini, pengelompokan genre musik memiliki peran penting dalam penyediaan konten musik yang lebih terarah dan sesuai dengan preferensi pendengar. Meskipun telah banyak platform musik canggih yang tersedia, pengguna masih mengalami kesulitan dalam menemukan musik yang sesuai dengan preferensi mereka. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi genre musik yang akurat dengan menggunakan metode ekstraksi fitur ritme dan frekuensi. Menggunakan dataset GTZAN yang terdiri dari 10 genre musik, yaitu Blues, Classical, Country, Disco, Hip-hop, Jazz, Metal, Pop, Reggae, dan Rock. Teknik ekstraksi fitur yang digunakan adalah Mel Frequency Cepstral Coefficient (MFCC) dan F0, dengan algoritma Decision Tree. Hasil penelitian menunjukkan bahwa algoritma Decision Tree mampu mengklasifikasikan genre musik dengan tingkat akurasi sebesar 52%. Fitur MFCC dan F0 terbukti memberikan kontribusi signifikan dalam meningkatkan akurasi klasifikasi, dengan MFCC memberikan informasi yang lebih detail mengenai spektrum frekuensi, sementara F0 membantu dalam mengenali pola ritme yang khas dari masing-masing genre. Sistem ini mampu memprediksi genre musik secara otomatis dengan efisien, namun tantangan masih ditemukan dalam membedakan genre yang memiliki kemiripan dalam ritme dan melodi, seperti Rock dan Metal.
Referensi
Abhyankar, S. G., Bharadwaj, S. S., Rani, G. S., Karigiri, P. G., Srikanth, S., & Gurugopinath, S. (2023). A Survey on Music Genre Classification Using Multimodal Information Processing and Retrieval. 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), 1–6. https://doi.org/10.1109/ICRTEC56977.2023.10111926
B. Panigrahi, R. Bhandari, K. Priya, V. Gandhi and Shivraj, "Harmony in Algorithms: Exploring Music Genre Classification Through Machine Learning," 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2023, pp. 1-6, doi: 10.1109/ICCAKM58659.2023.10449573.
Castillo, J. R., & Flores, M. J. (2021). Web-Based Music Genre Classification for Timeline Song Visualization and Analysis. IEEE Access, 9, 18801–18816. https://doi.org/10.1109/ACCESS.2021.3053864
Dugar, M. (2023). Music Genre Classification. 2023 Computer Applications & Technological Solutions (CATS), 1–6. https://doi.org/10.1109/CATS58046.2023.10424379
F. W. Wibowo and Wihayati, "Detection of Indonesian Dangdut Music Genre with Foreign Music Genres Through Features Classification Using Deep Learning," 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), Jakarta, Indonesia, 2022, pp. 313-318, doi: 10.1109/ISMODE53584.2022.9743085.
Fardhani, S. M., Wihardi, Y., & Piantari, E. (2021). Klasifikasi Genre Musik Dengan Mel Frequency Cepstral Coefficient Dan Spektogram Menggunakan Convolutional Neural Network.
Ghildiyal, A., & Sharma, S. (2021). Music Genre Classification Using Data Filtering Algorithm: An Artificial Intelligence Approach. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 922–926. https://doi.org/10.1109/ICIRCA51532.2021.954459
Habibi, C. B., & Irwansyah, I. (2020). KONSUMSI DAN PRODUKSI MUSIK DIGITAL PADA ERA INDUSTRI KREATIF. Metacommunication: Journal of Communication Studies, 5(1), 23. https://doi.org/10.20527/mc.v5i1.7449
Hikmah Fratiwi, T., Putu Suniantara, I. K., & Chandrarani, T. (2023). RANCANG BANGUN SISTEM AKUISISI DATA UNTUK MENGELOMPOKAN MUSIK PADA PLATFORM MEDIA SOSIAL TIKTOK BERDASARKAN SUASANA HATI. Simtek : jurnal sistem informasi dan teknik komputer, 8(2), 309–314. https://doi.org/10.51876/simtek.v8i2.264
Ignatius Moses Setiadi, D. R., Satriya Rahardwika, D., Rachmawanto, E. H., Atika Sari, C., Susanto, A., Wahyu Mulyono, I. U., Zuni Astuti, E., & Fahmi, A. (2020). Effect of Feature Selection on The Accuracy of Music Genre Classification using SVM Classifier. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 7–11. https://doi.org/10.1109/iSemantic50169.2020.9234222
Islam, M. S., Hasan, M. M., Rahim, M. A., Hasan, A. M., Mynuddin, M., Khandokar, I., & Islam, M. J. (2022). Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 7(3), 491. https://doi.org/10.26555/jiteki.v7i3.22327
M. P. V. N. Sai and S. Kalaiarasi, "Implementation of Music genre classification using Support Vector Clustering algorithm and KNN Classifier for improving accuracy," 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-6, doi: 10.1109/ICONSTEM56934.2023.10142741.
Manikandan, K., & Mathivanan, G. (2023). An Intelligent Music Genre Classification Method with Feature Extraction based on Deep Learning Techniques. 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 902–907. https://doi.org/10.1109/IDCIoT56793.2023.10053460
Nugraa, D. R. (2020). APLIKASI KLASIFIKASI GENRE MUSIK MENGGUNAKAN METODE NAIVE BAYES BERBASIS DESKTOP. 01(02).
P. S. K, Prathyakshini, Prathwini, Jayashree and S. Salian, "Identification of Automated Music Genre by Exploring Machine Learning Approaches," 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 2023, pp. 1-6, doi: 10.1109/NMITCON58196.2023.10276157.
Patil, A., Tawte, S., Deshmukh, S., Dhumale, S., Gidwani, M., & Nadkarni, S. (2023). Music Genre Classification using various Machine Learning Algorithms. 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA), 1–6. https://doi.org/10.1109/ICACTA58201.2023.10392965
Pavan, V., & Dhanalakshmi, R. (2022). Analysis of Audio Data and Prediction of the Genre using Novel Random Forest and Decision Tree. 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), 1773–1777. https://doi.org/10.1109/ICIRCA54612.2022.9985019
Pelchat, N., & Gelowitz, C. M. (2020). Neural Network Music Genre Classification. Canadian Journal of Electrical and Computer Engineering, 43(3), 170–173. https://doi.org/10.1109/CJECE.2020.2970144
Prashanthi, V., Kanakala, S., Akila, V., & Harshavardhan, A. (2021). Music Genre Categorization using Machine learning Algorithms. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 1–4. https://doi.org/10.1109/ICCICA52458.2021.9697137
Prince, S., Thomas, J. J., J, S. J., Priya, K. P., & Daniel, J. J. (2022). Music Genre Classification using Deep learning—A review. 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 1–5. https://doi.org/10.1109/CSITSS57437.2022.10026394
Qothrunnada, F., Saidah, S., Hidayat, B., Busrizal Putri, T., & Darwindra. (2023). TONE DETECTION ON TERANIKA MUSICAL INSTRUMENT USING DISCRETE WAVELET TRANSFORM AND DECISION TREE CLASSIFICATION. Jurnal Teknik Informatika (Jutif), 4(2), 373–380. https://doi.org/10.52436/1.jutif.2023.4.2.570
S. J and K. S, "Obtain Better Accuracy Using Music Genre Classification Systemon GTZAN Dataset," 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, 2022, pp. 1-5, doi: 10.1109/NKCon56289.2022.10126991.
S. U. Masruroh, A. S. Pratama, L. K. Wardhani, F. Fahrianto, W. A. Tsaqofi and R. A. Putri, "Classification of Popular Music Genre Using Convolutional Neural Network Method with Data Augmentation," 2023 Eighth International Conference on Informatics and Computing (ICIC), Manado, Indonesia, 2023, pp. 1-4, doi: 10.1109/ICIC60109.2023.10381995.
Salsabila, S. (n.d.). MODUL DATA MINING KLASIFIKASI PERTEMUAN 8 (ONLINE).
Yehezkiel, S. Y., & Suyanto, Y. (2022). Music Genre Identification Using SVM and MFCC Feature Extraction. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 12(2), 115. https://doi.org/10.22146/ijeis.70898
Abhyankar, S. G., Bharadwaj, S. S., Rani, G. S., Karigiri, P. G., Srikanth, S., & Gurugopinath, S. (2023). A Survey on Music Genre Classification Using Multimodal Information Processing and Retrieval. 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), 1–6. https://doi.org/10.1109/ICRTEC56977.2023.10111926
B. Panigrahi, R. Bhandari, K. Priya, V. Gandhi and Shivraj, "Harmony in Algorithms: Exploring Music Genre Classification Through Machine Learning," 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2023, pp. 1-6, doi: 10.1109/ICCAKM58659.2023.10449573.
Castillo, J. R., & Flores, M. J. (2021). Web-Based Music Genre Classification for Timeline Song Visualization and Analysis. IEEE Access, 9, 18801–18816. https://doi.org/10.1109/ACCESS.2021.3053864
Dugar, M. (2023). Music Genre Classification. 2023 Computer Applications & Technological Solutions (CATS), 1–6. https://doi.org/10.1109/CATS58046.2023.10424379
F. W. Wibowo and Wihayati, "Detection of Indonesian Dangdut Music Genre with Foreign Music Genres Through Features Classification Using Deep Learning," 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), Jakarta, Indonesia, 2022, pp. 313-318, doi: 10.1109/ISMODE53584.2022.9743085.
Fardhani, S. M., Wihardi, Y., & Piantari, E. (2021). Klasifikasi Genre Musik Dengan Mel Frequency Cepstral Coefficient Dan Spektogram Menggunakan Convolutional Neural Network.
Ghildiyal, A., & Sharma, S. (2021). Music Genre Classification Using Data Filtering Algorithm: An Artificial Intelligence Approach. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 922–926. https://doi.org/10.1109/ICIRCA51532.2021.954459
Habibi, C. B., & Irwansyah, I. (2020). KONSUMSI DAN PRODUKSI MUSIK DIGITAL PADA ERA INDUSTRI KREATIF. Metacommunication: Journal of Communication Studies, 5(1), 23. https://doi.org/10.20527/mc.v5i1.7449
Hikmah Fratiwi, T., Putu Suniantara, I. K., & Chandrarani, T. (2023). RANCANG BANGUN SISTEM AKUISISI DATA UNTUK MENGELOMPOKAN MUSIK PADA PLATFORM MEDIA SOSIAL TIKTOK BERDASARKAN SUASANA HATI. Simtek : jurnal sistem informasi dan teknik komputer, 8(2), 309–314. https://doi.org/10.51876/simtek.v8i2.264
Ignatius Moses Setiadi, D. R., Satriya Rahardwika, D., Rachmawanto, E. H., Atika Sari, C., Susanto, A., Wahyu Mulyono, I. U., Zuni Astuti, E., & Fahmi, A. (2020). Effect of Feature Selection on The Accuracy of Music Genre Classification using SVM Classifier. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 7–11. https://doi.org/10.1109/iSemantic50169.2020.9234222
Islam, M. S., Hasan, M. M., Rahim, M. A., Hasan, A. M., Mynuddin, M., Khandokar, I., & Islam, M. J. (2022). Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 7(3), 491. https://doi.org/10.26555/jiteki.v7i3.22327
M. P. V. N. Sai and S. Kalaiarasi, "Implementation of Music genre classification using Support Vector Clustering algorithm and KNN Classifier for improving accuracy," 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-6, doi: 10.1109/ICONSTEM56934.2023.10142741.
Manikandan, K., & Mathivanan, G. (2023). An Intelligent Music Genre Classification Method with Feature Extraction based on Deep Learning Techniques. 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 902–907. https://doi.org/10.1109/IDCIoT56793.2023.10053460
Nugraa, D. R. (2020). APLIKASI KLASIFIKASI GENRE MUSIK MENGGUNAKAN METODE NAIVE BAYES BERBASIS DESKTOP. 01(02).
P. S. K, Prathyakshini, Prathwini, Jayashree and S. Salian, "Identification of Automated Music Genre by Exploring Machine Learning Approaches," 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 2023, pp. 1-6, doi: 10.1109/NMITCON58196.2023.10276157.
Patil, A., Tawte, S., Deshmukh, S., Dhumale, S., Gidwani, M., & Nadkarni, S. (2023). Music Genre Classification using various Machine Learning Algorithms. 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA), 1–6. https://doi.org/10.1109/ICACTA58201.2023.10392965
Pavan, V., & Dhanalakshmi, R. (2022). Analysis of Audio Data and Prediction of the Genre using Novel Random Forest and Decision Tree. 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), 1773–1777. https://doi.org/10.1109/ICIRCA54612.2022.9985019
Pelchat, N., & Gelowitz, C. M. (2020). Neural Network Music Genre Classification. Canadian Journal of Electrical and Computer Engineering, 43(3), 170–173. https://doi.org/10.1109/CJECE.2020.2970144
Prashanthi, V., Kanakala, S., Akila, V., & Harshavardhan, A. (2021). Music Genre Categorization using Machine learning Algorithms. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 1–4. https://doi.org/10.1109/ICCICA52458.2021.9697137
Prince, S., Thomas, J. J., J, S. J., Priya, K. P., & Daniel, J. J. (2022). Music Genre Classification using Deep learning—A review. 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 1–5. https://doi.org/10.1109/CSITSS57437.2022.10026394
Qothrunnada, F., Saidah, S., Hidayat, B., Busrizal Putri, T., & Darwindra. (2023). TONE DETECTION ON TERANIKA MUSICAL INSTRUMENT USING DISCRETE WAVELET TRANSFORM AND DECISION TREE CLASSIFICATION. Jurnal Teknik Informatika (Jutif), 4(2), 373–380. https://doi.org/10.52436/1.jutif.2023.4.2.570
S. J and K. S, "Obtain Better Accuracy Using Music Genre Classification Systemon GTZAN Dataset," 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, 2022, pp. 1-5, doi: 10.1109/NKCon56289.2022.10126991.
S. U. Masruroh, A. S. Pratama, L. K. Wardhani, F. Fahrianto, W. A. Tsaqofi and R. A. Putri, "Classification of Popular Music Genre Using Convolutional Neural Network Method with Data Augmentation," 2023 Eighth International Conference on Informatics and Computing (ICIC), Manado, Indonesia, 2023, pp. 1-4, doi: 10.1109/ICIC60109.2023.10381995.
Salsabila, S. (n.d.). MODUL DATA MINING KLASIFIKASI PERTEMUAN 8 (ONLINE).
Yehezkiel, S. Y., & Suyanto, Y. (2022). Music Genre Identification Using SVM and MFCC Feature Extraction. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 12(2), 115. https://doi.org/10.22146/ijeis.70898
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2024 Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Artikel ini berlisensiCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
