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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Technologia: Jurnal Ilmiah Competitive Media Informatika Indonesian Community Service and Empowerment Journal (IComSE) Jurnal Responsif : Riset Sains dan Informatika Jurnal Sains Manajemen Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Sosial dan Abdimas Jurnal Financia : Akuntansi dan Keuangan Hexatech: Jurnal Ilmiah Teknik Competitive SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Hariyanti, Ifani
Universitas Adhirajasa Reswara Sanjaya
Aerospace Engineering Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Social Sciences Transportation

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Journal : SEMINAR TEKNOLOGI MAJALENGKA (STIMA)

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ANALISIS SISTEM REKOMENDASI MUSIK BERDASARKAN LIRIK DENGAN METODE TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY Muhyidin, Muhammad Sahel; Hariyanti, IFani; Novianto, Muhammad Fahmi; Apriliani, Anna Alifia; Rohmah, Sophia Nur
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 8 (2024): STIMA 8.0 : Menuju Kesinambungan : Inovasi dan Adaptasi Teknologi untuk Pembangunan Be
Publisher : Universitas Majalengka

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Abstract

This study develops and evaluates a music recommendation system using content-based filtering, focusing on lyrical analysis. Utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity metrics, the system analyzes a dataset of 6,049 songs to identify thematically related music based on lyrical content. The methodology involves data preprocessing, feature extraction, and the application of a content-based filtering algorithm to compare song attributes. Results indicate the system's ability to generate relevant recommendations, potentially enhancing user experience in music discovery. This research contributes to the field of personalized content delivery systems, offering insights into the effectiveness of lyric-based music recommendation algorithms
Co-Authors Agung Rachmat Raharja Ali Akbar Rismayadi, Ali Akbar Apriliani, Anna Alifia Bagas Oxy Exa Andriansyah Drajat, Devi Yuniati Dwinta Mulyanti Egi Septiana Fadila Andini Febrianto, Rudhi Wahyudi Feti Fatimah Maulyan Hafizh Cahaya Putra, Vito Husaini, Al - Iedam Fardian Anshori, Iedam Fardian Ina Najiyah Irawan, Rivaldy MAXSI ARY Muhamad Ridwan Ramadan Muhyidin, Muhammad Sahel Novianto, Muhammad Fahmi Rahadi, Panji Firman Ria Yuli Angliawati Rissa Nurfitriana Handayani Riyanto Setiyono, Riyanto Rohmah, Sophia Nur Safra, Candra Edmond Sandini, Dwi Sari Susanti, Sari Sulaeman, Aqil Tama Wildan Wiguna Yudi Ramdhani Yuniati Drajat, Devi