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All Journal Jurnal Riset Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Journal of Community Service and Empowerment Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komputer dan Elektro Sains
Budi Mukhamad Mulyo
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Journal : bit-tech

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Mobile-Based Book Recommendation System Based on Film Preferences Using Content-Based Filtering Nabil Anshari; Retno Mumpuni; Budi Mukhamad Mulyo
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3177

Abstract

The low reading interest among the Indonesian population remains one of the main challenges in improving national literacy quality. One contributing factor is the difficulty in finding reading materials that align with individual interests. Conversely, the increasing public interest in films can be leveraged as a bridge to foster reading habits. This study discusses the development of a mobile-based book recommendation system based on users’ film preferences to facilitate the discovery of books relevant to their favorite films. The proposed method here employs a Content-Based Filtering approach using Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity to measure the similarity between film synopsis and book descriptions. Data are retrieved in real time through the integration of The Movie Database (TMDB) API and Google Books API. System evaluation was conducted using User Acceptance Testing (UAT) with ISO 9126 as the evaluation framework, focusing on functionality, usability, and reliability aspects. The results show that the application successfully provides relevant book recommendations based on users’ selected films, achieving functionality, usability, and reliability scores of 88%, 84%, and 86%, respectively. Therefore, the system is considered feasible for use and has the potential to serve as a literacy enhancement medium based on film preference.
Implementasi CNN Untuk Klasfikasi Emosi Dalam Lagu Berdasarkan Fitur Audio Fredrik Sahalatua Pakpahan; M. Muharrom Al Haromainy; Budi Mukhamad Mulyo
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3438

Abstract

Music is a powerful art form for conveying and evoking emotions; however, the vast volume of digital music data makes manual emotion categorization difficult. This study aims to implement a Convolutional Neural Network (CNN) to classify emotions in instrumental songs based on audio features. The dataset used is the Database for Emotional Analysis of Music (DEAM), containing 1,802 songs with valence and arousal annotations, which is divided with a 70:15:15 ratio for training, validation, and testing. The feature extraction methods applied include Mel-Frequency Cepstral Coefficients (MFCC) with variations of 13, 24, and 30 coefficients, and Mel-spectrograms with variations of 128, 256, and 512 bins. Data is processed through pre-emphasis and framing stages before being input into a CNN architecture with four convolutional blocks. Evaluation was conducted using 4-quadrant classification scenarios and a simplification into 2 quadrants. The results showed that in the 4-quadrant classification, the best model was achieved using MFCC with 30 coefficients with an accuracy of 66%, but model performance was hindered by extreme minority class imbalance. Conversely, simplifying the emotion space into 2 quadrants (valence or arousal) significantly improved accuracy to 77%. This study concludes that while increasing feature resolution has a minor impact, simplifying emotion dimensions proves more effective in addressing complexity and data imbalance in music emotion classification.
Co-Authors Ardhon Rakhmadi Ardhon Rakhmadi Bayu Permana Sukma Devi Ambarwati Puspitasari Firza Prima Aditiawan Fredrik Sahalatua Pakpahan Hazna At Thooriqoh Henni Endah Wahanani Kalvin Edo Wahyudi M. Muharrom Al Haromainy Nabil Anshari Nanik Hariyana Putri Nur Rahayu Retno Mumpuni Rhiziqo Adjie Syahputra Saiful Abroriy