SONG LIST RECOMMENDATION FOR STUDENT ACTIVITIES USING K-NEAREST NEIGHBORS (K-NN) METHOD

I Made Aan Nugraha; I Gede Iwan Sudipa; Ni Putu Suci Meinarni; Made Leo Radhitya; Ketut Jaya Atmaja

Authors

I Made Aan Nugraha Prodi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia
I Gede Iwan Sudipa Prodi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia
Ni Putu Suci Meinarni Prodi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia
Made Leo Radhitya Prodi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia
Ketut Jaya Atmaja Prodi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia

Keywords:

Music Recommendation, K-Nearest Neighbors, Study Concentration, Spotify API, Energy and Valence

Abstract

Music plays an important role in influencing an individual's mood, concentration and productivity. However, choosing the right music for learning activities is still a challenge, as each individual has different music preferences and the right music can have a positive impact on learning focus and motivation. Based on this problem, this research aims to develop an individual preference-based music recommendation system that can help users choose music that suits their learning needs. The solution offered is the implementation of a recommendation system using the K-Nearest Neighbor (K-NN) algorithm, which combines user profile data (such as age, gender, and hobbies) with music characteristics (such as energy and valence) to determine the most suitable song. The research data was collected through surveys and Spotify API to obtain song parameters. The analysis process uses K-NN to calculate the congruence between user profiles and song data in the dataset, with the aim of recommending suitable music. The results show that the system is effective in supporting concentration and study motivation, providing suitable song recommendations based on user preferences and needs.

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