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All Journal Jurnal Informatika dan Teknik Elektro Terapan
Nurrofiq, Ainan Zaky
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Computer Science & IT

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SISTEM REKOMENDASI METADATA LAGU BERDASARKAN DETEKSI EMOSI WAJAH MENGGUNAKAN VIT-B/16 Nurrofiq, Ainan Zaky; Bambang Irawan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8950

Abstract

This study presents a music metadata recommendation system based on facial emotion detection using the Vision Transformer (ViT-B/16) model. The system classifies user emotions into seven categories using the KDEF facial dataset and matches them with music metadata (title, artist, genre, mood) labeled with corresponding emotional tags. The ViT-B/16 model was trained using transfer learning and evaluated with accuracy, precision, recall, and F1-score. The model achieved an accuracy of 89% and an average F1-score of 0.89. The recommendation system was assessed by 30 participants, with 87% indicating that the suggested song metadata matched the detected emotion. The system offers real-time emotion recognition and automatic mood-based song suggestions. However, classification accuracy for visually similar emotions such as “fear” and “angry” remains a challenge. Future development may include audio and lyric analysis, as well as user preference integration, to enhance recommendation relevance.
Co-Authors Bambang Irawan