<![CDATA[The rapid growth of digital technology has revolutionized how people access and listen to music, especially through online streaming platforms. However, the overwhelming number of available songs often confuses users, particularly new users who have no listening history. To address this, the study proposes a music recommendation system using a content-based filtering approach that recommends songs based on similarities in both textual and numerical features, such as genre, artist, lyrics, tempo, energy, and danceability. The system operates in two main stages. First, it classifies the popularity of songs into two categories, “High” and “Low,” using three classification algorithms: Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Second, it generates music recommendations based on content similarity using TF-IDF and cosine similarity. Random Forest is chosen as the main algorithm due to its superior performance in high-dimensional data and its ensemble learning mechanism. The evaluation uses confusion matrix metrics including accuracy, precision, recall, and F1 score, tested across multiple data split ratios (90:10, 80:20, 70:30, 60:40). The results show that Random Forest consistently delivers better classification and recommendation performance compared to KNN and SVM. It demonstrates higher accuracy and F1 score, making it suitable for real-world applications. The system is developed using Streamlit, allowing users to interactively receive music recommendations through a user-friendly web interface. The findings support the integration of Random Forest in content-based recommendation systems to improve accuracy and solve cold-start problems effectively in digital music platforms.]]>
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