Implementasi Machine Learning dalam Penentuan Rekomendasi Musik dengan Metode Content-Based Filtering

Aldy Istna Putra, Reva Ragam Santika

Abstract


The industry that is experiencing significant development is the music industry. An example of its development is the many online music service providers of application platforms. The amount of data stored makes it difficult to analyze existing data, the presence of Machine Learning is felt to be able to answer these challenges. Improving user experience is important to attract users to use the applications they have. The recommendation system is one way to improve that. This research aims to create a system that can present music recommendations according to user preferences so that the user's comfort level will increase. The system developed in this research uses the Extreme Programming method with several stages, namely planning, design, coding, and testing. This research utilizes Machine Learning in searching for data patterns and Content-Based Filtering (CBF) methods in finding recommendations. The recommendation system with the CBF method can produce a song similarity level of up to 0.6684, as well as the value of precision reaching 0.125 and 0.200 at recall. The results of Performance Testing and System Testing obtained stated that the recommendation system can run well with an average response time 3.5 seconds. The conclusion of this research is that the recommendation system using the CBF method can produce recommendations that are in accordance with user preferences, but with not too much data. More effective algorithms are needed for larger data.


Keywords


Content-Based Filtering; Machine Learning; Recommendation System

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References


Adelin, & Effendi, H. (2017). Aplikasi Audit Mutu Akademik Internal dengan Pendekatan Extreme Programming. Jurnal TI Atma Luhur, 4(1), 13–24.

Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer. Basel. Switzerland.

Alpaydın, E. (2020). Introduction to Machine Learning. 4th, Massachusetts: MIT Press.

Apandi, T. H., & Sugianto, C. A. (2018). Analisis Komparasi Machine Learning pada Data Spam SMS. Jurnal TEDC, 12(1), 58–62.

Bachtiar, F. A., Syahputra, I. K., & Wicaksono, S. A. (2019). Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah. Jurnal Teknologi Informasi Dan Ilmu Komputer, 6(5), 543–548.

Budianto, T., & Hermawan, G. (2013). Rancang Bangun Music Recommender System dengan Metode User-Based Collaborative. Jurnal Ilmiah Komputer Dan Informatika (KOMPUTA), 2(2), 1–10.

Cunha, T., Soares, C., & de Carvalho, A. C. P. L. F. (2018). Metalearning and Recommender Systems: A literature Review and Empirical Study on the Algorithm Selection Problem for Collaborative Filtering. Information Sciences, 423(1), 128–144.

Fathurrahman, M. I., Nurjanah, D., & Rismala, R. (2017). Sistem Rekomendasi pada Buku dengan Menggunakan Metode Trust-Aware Recommendation Recommendation System for Book by using Trust-Aware Recommendation Method. E-Proceeding of Engineering, 4(3), 4966–4977.

Harrington, P. (2012). Machine Learning in Action. New York: Manning..

Marsland, S. (2015). Machine Learning: An Algorithmic Perspective. USA: CRC Press.

Müller, A. C., & Guido, S. (2016). Introduction to ML with Python: A Guide for Data Scientists. California: O’Reilly Media, Inc.

Nastiti, P. (2019). Penerapan Metode Content Based Filtering dalam Implementasi Sistem Rekomendasi Tanaman Pangan. Teknika, 8(1), 1–10.

Netti, S. Y. M., & Irwansyah, I. (2018). Spotify: Aplikasi Music Streaming untuk Generasi Milenial. Jurnal Komunikasi, 10(1), 1–16.

Suryantara, I. G. N. (2017). Merancang Aplikasi dengan Metodologi Extreme Programmings. Jakarta: PT Elex Media Komputindo

Thorat, P. B., Goudar, R. M., & Barve, S. S. (2015). Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications, 110(4), 31–36.


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