Implementasi Web Klasifikasi Suasana Hati Berdasarkan Potongan Lagu dengan Memanfaatkan Convolutional Neural Network
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Music is often used to accompany the user according to his heart condition. So users often create a playlist by adjusting the mood they are feeling. However, there are some users who have had difficulties in making playlists because in making a playlist it has to be done manually, that is, listening to music one at a time, wasting a lot of time. Therefore, the author conducted research on the classification of the mood contained in music and created a system that works to help classify music automatically by using one method that is part of deep learning, the method mentioned by the author is the Convolutional Neural Network (CNN) method. As for the data used by the investigator in this study is music data with a lot of data amounting to 400 data, on such data is done preprocessing data by cutting the duration of music and converting music into image. The next step is to split the data, dividing it into training data and test data. The training data is divided by 80% and the test data is also split by 20% of the total datasets used by the author. The results of this data division were used to build a model using the CNN model. The accuracy results obtained in this study were 95% for the training accurately and 68% for the data validation accurate.
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Amelia, C., & Aryaneta, Y. (2022). Pengaruh Musik Terhadap Emosi. Jurnal Ilmiah Zona Psikologi, 4(3).
Izzah, L. I. (2020). Pengaruh Mendengarkan Musik Terhadap Mood Belajar Pada Mahasiswa Manajemen Dakwah Uin Suska Riau. Nathiqiyyah, 3(1), 38-43.
Maulana, P. I., Aranta, A., Bimantoro, F., & Andika, I. G. (2022). Klasifikasi Mood Musik berdasarkan Mel Frequency Cepstral Coefficients dengan Backpropagation Neural Network. Jurnal RESISTOR (Rekayasa Sistem Komputer), 5(1), 72-85.
Asfi, M., Ulva, M., & Triyani, I. (2023). Penerapan Metode Shuffle Random pada Aplikasi Sistem Penentuan Playlist Lagu. Journal of Practical Computer Science, 3(1), 1-8. DOI: https://doi.org/10.37366/jpcs.v3i1.2355.
Harsemadi, G., Sudarma, M., & Pramaita, N. (2017). Implementasi Algoritma K-Nearest Neighbor pada Perangkat Lunak Pengelompokan Musik untuk Menentukan Suasana Hati. Maj. Ilm. Teknol. Elektro, 16(1), 14-20.
Luis, R., & Rokhman, N. Traditional Music Regional Classification using Convolutional Neural Network (CNN). IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 16(4), 379-388. https://doi.org/10.22146/ijccs.73910.
Giria, I. N. Y. T., Putria, L. A. A. R., Giria, G. A. V. M., Putraa, I. G. N. A. C., Widiarthaa, I. M., & Suprianaa, I. W. (2022). Music genre classification using modified k-nearest neighbor (MK-NN). Jurnal Elektronik Ilmu Komputer Udayana p-ISSN, 2301, 5373.
Septhyan, S., Magdalena, R., & Pratiwi, N. K. C. (2023). Deep Learning Untuk Deteksi Covid-19, Pneumonia, Dan Tuberculosis Pada Citra Rontgen Dada Menggunakan CNN Dengan Arsitektur Alexnet. eProceedings of Engineering, 9(6).
Via, Y. V., Purbasari, I. Y., & Pratama, A. P. (2022). ANALISA ALGORITMA CONVOLUTION NEURAL NETWORK (CNN) PADA KLASIFIKASI GENRE MUSIK BERDASAR DURASI WAKTU. Scan: Jurnal Teknologi Informasi dan Komunikasi, 17(1), 35-41.
Lionel, D., Adipranata, R., & Setyati, E. (2019). Klasifikasi Genre Musik Menggunakan Metode Deep Learning Convolutional Neural Network dan Mel-Spektrogram. Jurnal Infra, 7(1), 51-55.
Wairata, C. R., Swedia, E. R., & Cahyanti, M. (2021). Pengklasifikasian Genre Musik Indonesia Menggunakan Convolutional Neural Network. Sebatik, 25(1), 255-261. DOI: https://doi.org/10.46984/sebatik.v25i1.1286.
Susanti, E. (2020). Implementasi RESTful API dalam Pembuatan Master Data Planogram Menggunakan Framework Flask (Studi Kasus: PT Sumber Alfaria Trijaya, Tbk). Techno. Com, 19(3), 295-307. DOI: https://doi.org/10.33633/tc.v19i3.3468.
Amini, N., Saragih, T. H., Faisal, M. R., Farmadi, A., & Abadi, F. (2022). IMPLEMENTASI ALGORITMA GENETIKA UNTUK SELEKSI FITUR PADA KLASIFIKASI GENRE MUSIK MENGGUNAKAN METODE RANDOM FOREST. Jurnal Informatika Polinema, 9(1), 75-82. DOI: https://doi.org/10.33795/jip.v9i1.1028.
Nurfita, R. D., & Gunawan Ariyanto, S. T. (2018). Implementasi Deep Learning Berbasis Tensorflow Untuk Pengenalan Sidik Jari (Doctoral dissertation, Universitas Muhammadiyah Surakarta).
Maulana, F. F., & Rochmawati, N. (2019). Klasifikasi Citra Buah Menggunakan Convolutional Neural Network. Journal of Informatics and Computer Science (JINACS), 1(02), 104-108. DOI: https://doi.org/10.26740/jinacs.v1n02.p104-108.
Cahya, F. N., Hardi, N., & Riana, D. (2021). Klasifikasi penyakit mata menggunakan convolutional neural network (CNN). Sistemasi: Jurnal Sistem Informasi, 10(3), 618-626. DOI: https://doi.org/10.32520/stmsi.v10i3.1248.
Magdalena, R., Saidah, S., Pratiwi, N. K. C., & Putra, A. T. (2021). Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN). JEPIN (Jurnal Edukasi dan Penelitian Informatika), 7(3), 335-339. DOI: http://dx.doi.org/10.26418/jp.v7i3.48195.
Nurkhasanah, N., & Murinto, M. (2022). Klasifikasi Penyakit Kulit Wajah Menggunakan Metode Convolutional Neural Network. Sainteks, 18(2), 183-190. DOI: 10.30595/sainteks.v18i2.13188.
Putra, W. S. E. (2016). Klasifikasi citra menggunakan convolutional neural network (CNN) pada caltech 101. Jurnal Teknik ITS, 5(1). DOI: 10.12962/j23373539.v5i1.15696.