[1] KBBI Online, Big Indonesian Dictionary (KBBI), [Online] 25 July 2021, https://kbbi.web.id/musik (Accessed: 30 July 2021).
[2] Bunt, Leslie. Music therapy: An art beyond words. Routledge, 2003.
[3] Alperson, Philip. "Musical time" and music as an" art of time." The Journal of Aesthetics and Art Criticism 38.4 (1980): 407-417.
[4] J. Alexander, “Streaming Makes up 80 Percent of the Music Industry’s Revenue,” The Verge, 2019.
[5] Coffey, Aoife. "The impact that music streaming services such as Spotify, Tidal and Apple Music have had on consumers, artists and the music industry itself." Interactive Digital Media. University of Dublin, 2016.
[6] Bhoot, Gurpreet. "Music Industry Sales: How streaming services such as Spotify, Apple Music and TIDAL affect album sales", 2017.
[7] M. Kaminskas and F. Ricci, “Contextual music information retrieval and recommendation: State of the art and challenges”, Computer Science Review, vol. 6, (2-3) pp. 89-119, 2012.
[8] Yoshii, Kazuyoshi, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Transactions on Audio, Speech, and Language Processing, 2008, 16.2: 435-447.
[9] Katarya, Rahul; VERMA, Om Prakash. Efficient music recommender system using context graph and particle swarm. Multimedia Tools and Applications, 2018, 77.2: 2673-2687.
[10] Shakirova, Elena. "Collaborative filtering for music recommender system." 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2017.
[11] C. Boyd, “How Spotify Recommends Your New Favorite Artist”, (Towards Data Science), [Online] 2019, https://towardsdatascience.com/how-spotify-recommends-your-new-favorite-artist-8c1850512af0 (Accessed: 30 July 2021).
[12] Millecamp, Martijn, et al. "Controlling Spotify recommendations: effects of personal characteristics on music recommender user Interfaces." Proceedings of the 26th Conference on user modeling, adaptation and personalization. 2018.
[13] Pichl, Martin, Eva Zangerle, and Günther Specht. "Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation." Grundlagen von Datenbanken. 2014.
[14] Amini, Rafika, Martijn C. Willemsen, and Mark P. Graus. "Affective Music Recommender System (MRS): Investigating the effectiveness and user satisfaction of different mood inducement strategies", 2019.
[15] Bernhardsson, Erik. Implementing a scalable music recommender system. Skolan för datavetenskap och kommunikation, Kungliga Tekniska högskolan, 2009.
[16] L. Villazon, “Does music affect our heart rate?”, (Science Focus), [Online] 2020, https://www.sciencefocus.com/the-human-body/does-music-affect-our-heart-rate/ (Accessed: 30 July 2021).
[17] Orman, Evelyn K. The effect of listening to specific musical genre selections on measures of heart rate variability. Update: Applications of Research in Music Education, 2011, 30.1: 64-69.
[18] Sills, David, and Amber Todd. "Does Music Directly Affect a Person’s Heart Rate?." Journal of Emerging Investigators , 2015.
[19] M. Braunhofer, M. Kaminskas and F. Ricci, "Location-aware music recommendation," International Journal of Multimedia Information Retrieval, vol. 2, (1) pp. 31-44, 2013.
[20] A. Al-Ajlan, "The comparison between forward and backward chaining," International Journal of Machine Learning and Computing, vol. 5, (2) pp. 106, 2015.
[21] M. 'Alim, R. Dewi and K. Brata, “Recommended Application Development Based on User Emotion Music on the Android Platform,” Journal of Information Technology and Computer Science, vol. 5, (1) pp. 242-249, Jan 2021, ISSN 2548-964X.
[22] K. Arimbawa, R. Dewi and L. Fanani, “Development of Location Based Music Recommendation Applications on the Android Platform,” Journal of Information Technology and Computer Science, vol. 5, (1) pp. 130-137, Jan 2021, ISSN 2548-964X.
[23] M. Ramadhan, R. Dewi and K. Brata, “Music Recommendation Application Development based on Heartbeat on Android Platform,” Journal of Information Technology and Computer Science, vol. 4, (8) pp. 2364-2368, Aug 2020, ISSN 2548-964X.
[24] A. Rizaldy, R. Dewi and K. Brata, “Development of Weather Based Music Recommendation Applications on the Android Platform,” Journal of Information Technology and Computer Science Development, vol. 5, (1) pp. 124-129, Jan 2021, ISSN 2548-964X.
[25] D. Harjananto, R. Dewi and K. Brata, “Music Recommendation System Development based Time based on Android,” Journal of Information Technology Development and Computer Science, vol. 5 (5) pp. 1729-1733, Apr 2021, ISSN 2548-964X.
[2] Bunt, Leslie. Music therapy: An art beyond words. Routledge, 2003.
[3] Alperson, Philip. "Musical time" and music as an" art of time." The Journal of Aesthetics and Art Criticism 38.4 (1980): 407-417.
[4] J. Alexander, “Streaming Makes up 80 Percent of the Music Industry’s Revenue,” The Verge, 2019.
[5] Coffey, Aoife. "The impact that music streaming services such as Spotify, Tidal and Apple Music have had on consumers, artists and the music industry itself." Interactive Digital Media. University of Dublin, 2016.
[6] Bhoot, Gurpreet. "Music Industry Sales: How streaming services such as Spotify, Apple Music and TIDAL affect album sales", 2017.
[7] M. Kaminskas and F. Ricci, “Contextual music information retrieval and recommendation: State of the art and challenges”, Computer Science Review, vol. 6, (2-3) pp. 89-119, 2012.
[8] Yoshii, Kazuyoshi, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Transactions on Audio, Speech, and Language Processing, 2008, 16.2: 435-447.
[9] Katarya, Rahul; VERMA, Om Prakash. Efficient music recommender system using context graph and particle swarm. Multimedia Tools and Applications, 2018, 77.2: 2673-2687.
[10] Shakirova, Elena. "Collaborative filtering for music recommender system." 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2017.
[11] C. Boyd, “How Spotify Recommends Your New Favorite Artist”, (Towards Data Science), [Online] 2019, https://towardsdatascience.com/how-spotify-recommends-your-new-favorite-artist-8c1850512af0 (Accessed: 30 July 2021).
[12] Millecamp, Martijn, et al. "Controlling Spotify recommendations: effects of personal characteristics on music recommender user Interfaces." Proceedings of the 26th Conference on user modeling, adaptation and personalization. 2018.
[13] Pichl, Martin, Eva Zangerle, and Günther Specht. "Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation." Grundlagen von Datenbanken. 2014.
[14] Amini, Rafika, Martijn C. Willemsen, and Mark P. Graus. "Affective Music Recommender System (MRS): Investigating the effectiveness and user satisfaction of different mood inducement strategies", 2019.
[15] Bernhardsson, Erik. Implementing a scalable music recommender system. Skolan för datavetenskap och kommunikation, Kungliga Tekniska högskolan, 2009.
[16] L. Villazon, “Does music affect our heart rate?”, (Science Focus), [Online] 2020, https://www.sciencefocus.com/the-human-body/does-music-affect-our-heart-rate/ (Accessed: 30 July 2021).
[17] Orman, Evelyn K. The effect of listening to specific musical genre selections on measures of heart rate variability. Update: Applications of Research in Music Education, 2011, 30.1: 64-69.
[18] Sills, David, and Amber Todd. "Does Music Directly Affect a Person’s Heart Rate?." Journal of Emerging Investigators , 2015.
[19] M. Braunhofer, M. Kaminskas and F. Ricci, "Location-aware music recommendation," International Journal of Multimedia Information Retrieval, vol. 2, (1) pp. 31-44, 2013.
[20] A. Al-Ajlan, "The comparison between forward and backward chaining," International Journal of Machine Learning and Computing, vol. 5, (2) pp. 106, 2015.
[21] M. 'Alim, R. Dewi and K. Brata, “Recommended Application Development Based on User Emotion Music on the Android Platform,” Journal of Information Technology and Computer Science, vol. 5, (1) pp. 242-249, Jan 2021, ISSN 2548-964X.
[22] K. Arimbawa, R. Dewi and L. Fanani, “Development of Location Based Music Recommendation Applications on the Android Platform,” Journal of Information Technology and Computer Science, vol. 5, (1) pp. 130-137, Jan 2021, ISSN 2548-964X.
[23] M. Ramadhan, R. Dewi and K. Brata, “Music Recommendation Application Development based on Heartbeat on Android Platform,” Journal of Information Technology and Computer Science, vol. 4, (8) pp. 2364-2368, Aug 2020, ISSN 2548-964X.
[24] A. Rizaldy, R. Dewi and K. Brata, “Development of Weather Based Music Recommendation Applications on the Android Platform,” Journal of Information Technology and Computer Science Development, vol. 5, (1) pp. 124-129, Jan 2021, ISSN 2548-964X.
[25] D. Harjananto, R. Dewi and K. Brata, “Music Recommendation System Development based Time based on Android,” Journal of Information Technology Development and Computer Science, vol. 5 (5) pp. 1729-1733, Apr 2021, ISSN 2548-964X.
- Abstract viewed - 835 times
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Affiliations
Ratih Kartika Dewi
Brawijaya University
M. Salman Ramadhan
Brawijaya University
Dwi Yovan Harjananto
Brawijaya University
Chindy Aulia Sari
Brawijaya University
Zumrotul Islamiah
Brawijaya University
Forward Chaining for Contextual Music Recommendation System
Abstract
Music is an important aspect of people's daily lives. The reasons people listen to music include to fill their free time and to keep the mood in good condition. Music recommendations are a recommendation system that exists not only because of the many types of music available, but also because people's perceptions of music are still not fully understood. But with so many music choices it makes it difficult for users to find music that fits their context. Examples include considering music based on the current user's location or current activities. A system is required that can recommend music in the context faced by the user.Music Recommendation System Development, Based on user context is a mobile application that uses the Android operating system. The recommendations provided by this system use expert system methods with forward chaining flow. The system will process inputs obtained from users and provide musical recommendations in accordance with the references provided by experts. The result of this study is a rule that is built to produce an average accuracy between user choice and system recommendations of 72%.