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All Journal JURNAL SISTEM INFORMASI BISNIS TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Format : Jurnal Imiah Teknik Informatika Applied Information System and Management JURNAL PENDIDIKAN TAMBUSAI Generation Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Research Student Jurnal Penelitian Teknologi Informasi dan Sains Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Mashuri, Chamdan
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences Other

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PENERAPAN ALGORITMA APRIORI UNTUK MENENTUKAN REKOMENDASI ANIME RATING TINGGI DENGAN JUMLAH PENONTON SEDIKIT (Studi Kasus: Situs MyAnimeList.net) Diah Puspita, Dwi Ratna Ayu; Mashuri, Chamdan; Ahmad Heru Mujianto; Indana Lazulfa
Inovate Vol 10 No 1 (2025): September
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v10i1.9386

Abstract

Abstract High-quality yet less popular anime recommendations are a need for users on MyAnimeList.net, a platform containing thousands of anime titles with varying ratings and viewership numbers. This study aims to apply the Apriori algorithm to determine anime recommendations with high ratings but low viewership, using data from MyAnimeList.net. In the growing anime industry, many high-quality anime titles receive little attention from viewers. Therefore, it is essential to develop a recommendation system that can help users discover anime they may have missed. The dataset used consists of rating and viewership data from MyAnimeList.net. Through filtering and analysis, the Apriori algorithm successfully identified relationships among anime titles that meet the criteria of high ratings and low viewership, resulting in more targeted recommendations. This research is expected to contribute to the development of data mining-based recommendation systems, especially for recommending content that is rarely viewed yet has high quality. The use of the Apriori algorithm in content recommendations also shows potential in helping users find preferences that are more diverse and relevant to their interests.
Co-Authors AA Sudharmawan, AA Achmad Imam Agung Adi Susanto, Adi Susanto Agus Subagio Ali Imron, Muhammad Alvin Mubarok, Muhamad Ananda Wildan Fadhlulloh Anita Andriani ANITA ANDRIANI, ANITA Ardiansyah Yudistira Putra, Rocky Aries Dwi Indriyanti, Aries Dwi Arsam, Rinaldo Yudianto BAMBANG SUJATMIKO Beta Setiawan Claudia Afaf Azzahrah Diah Puspita, Dwi Ratna Ayu Didiek Rusdyanto Dwi Saputra, Septian Faizah, Arbiati Fajar Alfiansyah Fajar Rohman Hariri Gayuh Afwa Maulana Alhasy Ginanjar Setyo Permadi Hadi Sucipto Hadi Sucipto, Hadi Indana Lazulfa Indana Lazulfa, Indana Kapindho Febriyanto Kistofer, Terdy Kristianto, Hery M. Hamzah Ulumuddin Mahfudiyanto Mahrus Ali Mahrus Ali Mauludi, Kartika Mohammad Fikri Agus Setiawan Muhammad Fatkhur Rizal Muhammad Rizal Dwiki Mujianto, Ahmad Heru Nasruddin, Hammam Pawindyanto, Aditya Danang Prastyo, Edwin Hari Agus Putra Eka Prismana, IGL Putra, Rocky Ardiansyah Yudistira Putri, Unzilla Savika Qois Al’Ariq Radityo Wiratsongko Rizal, Muhammad Fatkhur Rofi’ul Azis, Moh. Sari, Waskita Sri Widoyoningrum Tanhella Zein Vitadiar Tofan Putra Irianto Vitadiar, Tanhela Zein Wiratsongko, Radityo Yudistira Putra, Rocky Ardiansyah Yusuf, Lutfi Zainal Ikhwan, Muhammad Zein Vitadiar, Tanhella Zuyyinal Haqqul Barir , Much