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All Journal Informatics and Computer Engineering Journal Jurnal Informatika Dan Tekonologi Komputer
Michael Jeconiah Yonathan
Universitas Esa Unggul
Computer Science & IT Control & Systems Engineering Other

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Perbandingan Metode K-Means dan Hierarchical Clustering pada Rekomendasi Musik Berbasis Audio Spotify Features Sistem Samuel Mahesa Sinulingga; Farrel Reyhan Putra; Andhika Dwi Rachmawanto; Michael Jeconiah Yonathan; Valentino Wijaya; Vitri Tundjungsari
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11895

Abstract

Penelitian ini membandingkan metode K-Means dan Hierarchical Clustering dalam sistem rekomendasi musik berbasis audio features Spotify. Dataset yang digunakan berasal dari Spotify Tracks Dataset yang terdiri dari sekitar 114.000 lagu, kemudian melalui tahap pra-pemrosesan diperoleh sekitar 81.000 lagu valid. Untuk efisiensi komputasi, digunakan 5.000 lagu sebagai data eksperimen. Clustering dilakukan menggunakan 6 cluster dengan sembilan atribut audio. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan bahwa K-Means memperoleh nilai Silhouette Score 0,1900 dan Davies–Bouldin Index 1,4445, sedangkan Hierarchical Clustering memperoleh nilai Silhouette Score 0,1782 dan Davies–Bouldin Index 1,4522. Hasil ini menunjukkan bahwa K-Means menghasilkan cluster yang lebih kompak. Sistem rekomendasi yang dibangun mampu memberikan rekomendasi lagu yang relevan berdasarkan kemiripan karakteristik audio.
Sistem Rekomendasi Musik Berdasarkan Playlist dengan Collaborative Filtering Michael Jeconiah Yonathan; Daniel Prasetyo Dodi Darmawan; Andhika Dwi Rachmawanto; Ary Prabowo
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 3 (2025): November : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i3.8039

Abstract

Modern music streaming platforms offer millions of songs, creating a challenge for users in discovering content that matches their tastes. This research addresses this problem by designing a music recommendation system using a Hybrid Collaborative Filtering approach. This method combines the strengths of Item-Based (track similarity) and User-Based (playlist similarity) filtering for higher accuracy. Utilizing 100,000 playlists from Spotify's Million Playlist Dataset (MPD), the system was developed through data preprocessing, cosine similarity calculation, and weighted score combination. The evaluation was designed using metrics like Precision@K and Hit Ratio. The results demonstrate that the hybrid model can provide thematically relevant song recommendations based on an input playlist, proving its effectiveness in personalizing the music discovery experience for users.
Optimalisasi Summarization Berita BBC dengan Metode BiLSTM-Transformer Rafael Austin; Andhika Dwi Rachmawanto; Michael Jeconiah Yonathan; M Naufal Arriz; Vitri Tundjungsari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10669

Abstract

The rapid growth of digital news, such as that from the BBC, presents challenges for readers in absorbing dense information within limited time. This research proposes an automated text summarization system using a hybrid BiLSTM Transformer architecture to produce concise yet contextually accurate summaries. The model integrates BiLSTM to capture local sequential relationships and Transformer’s self-attention mechanism to handle global context, overcoming the computational limitations of standalone Transformers. Utilizing a self-embedding approach, the system processes text in an unsupervised manner, making it suitable for datasets without ground truth summaries. Evaluation was conducted using 50 samples from the Xsum dataset and 25 live BBC news links, with performance measured via cosine similarity to assess contextual preservation. The results demonstrated a consistent average cosine similarity of 0.7959 for dataset samples and 0.7877 for new data. These findings indicate that the hybrid model effectively maintains semantic integrity and provides reliable summaries for complex news articles.
Co-Authors Andhika Dwi Rachmawanto Ary Prabowo Daniel Prasetyo Dodi Darmawan Farrel Reyhan Putra M Naufal Arriz Rafael Austin Samuel Mahesa Sinulingga Valentino Wijaya Vitri Tundjungsari