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All Journal Proceeding SENDI_U JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Sains Manajemen:Jurnal Manajemen UNSERA JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Data Mining dan Sistem Informasi Jurnal Pendidikan dan Teknologi Indonesia CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Permata, Permata
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Hybrid Music Recommendati1on System Using K-Means Clustering and Neural Collaborative Filtering for Spotify Playlist Personalization Pamungkas, Rastomi; Permata, Permata; Gunawan, Rakhmat Dedi; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9181

Abstract

Personalizing music recommendations has become a significant challenge on music streaming platforms such as Spotify due to the vast number of available songs and the limitations of conventional recommendation systems in accurately capturing user preferences. In addition, traditional single-method recommendation approaches often face the cold start problem, which reduces the effectiveness of generated recommendations. Therefore, this study aims to develop and evaluate a hybrid recommendation system that integrates the K-Means Clustering algorithm and Deep Collaborative Filtering based on Neural Matrix Factorization to improve the relevance of music playlist recommendations. The dataset used in this study consists of more than 15,151 Spotify songs obtained from the Spotify dataset available on Kaggle. The dataset was processed through several stages including data inspection, data cleaning, feature selection, and standardization. Audio features used in the analysis include danceability, energy, acousticness, instrumentalness, valence, tempo, and duration. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, resulting in five clusters with a relatively balanced data distribution. The clustering results were then used as the basis for Cluster-Based Filtering to narrow the search space of candidate songs before being processed by the Neural Matrix Factorization model. Performance evaluation was conducted using Hit Ratio at rank 10 and Normalized Discounted Cumulative Gain at rank 10. The proposed model achieved values of 0.1110 and 0.0507, respectively, indicating that the integration of clustering and deep collaborative filtering can improve the effectiveness and personalization of music recommendation systems. This study contributes by proposing a hybrid recommendation framework that integrates clustering-based item grouping with deep collaborative filtering to improve recommendation efficiency and playlist personalization in large-scale music streaming platforms.
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.
Co-Authors Achmad Badjuri Agus Wantoro Aryani, Venty Damayanti Damayanti Desyandri Desyandri Farida Ariyani Gunawan, Rakhmat Dedi Harry Cahyono Hayatunnisa, Destaria Jaeni Jaeni Julinha Betty Guterres Riu Lutfy, Azza’zunda Choibar Megawaty, Dyah Ayu Pamungkas, Rastomi Pasaribu, A. Ferico Octaviansyah Priandika, Adhie Thyo Setiawan, Dandi Setiawansyah Setiawansyah Sumanto, Sumanto Sunarto Sunarto Wadiyan, Wadiyan Wahyudi, Agung Deni Wang, Junhai Yuditiyani, Yuditiyani Zaenal Abidin