Moodify: An Intelligent Music Player Based on User Mood Using Machine Learning - Lubis, Abdul Hadi; Sakti, Rendra Jogia; Royhan, Abdullah; Sunandri, Ika Bagus; Sitanggang, Andrian Agustin; Efendi, Yoyon (Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu, 2025)
Mood plays a vital role in influencing human productivity, emotions, and learning effectiveness. Music is widely known as one of the media that can influence and adjust human mood. However, most music player applications still rely on manual playlist selection and do not adapt dynamically to the user's emotional condition. This study proposes Moodify, an intelligent music player application that utilizes machine learning techniques to detect user mood and provide personalized music recommendations. The problem addressed in this research is the lack of adaptive music recommendation systems based on real-time user mood. The purpose of this research is to design and analyze a mood-based music recommendation system that can enhance user experience. The research method uses a machine learning-based classification approach to identify user mood from input data, followed by a recommendation algorithm that matches music characteristics with detected moods. The results show that Moodify is able to recommend music that aligns with user emotional states, improving comfort and engagement. This research contributes to the development of intelligent multimedia applications that support emotional well-being and personalized digital experiences.