Application of the K-Nearest Neighbors Algorithm in Anime Recommendation System Based on Genre Preferences and Web-Based Ratings

Medeline Cen; Huliman; Robby Wijaya

doi:10.59934/jaiea.v5i3.2491

Authors

Medeline Cen STMIK TIME
Huliman STMIK TIME
Robby Wijaya STMIK TIME

DOI:

https://doi.org/10.59934/jaiea.v5i3.2491

Keywords:

Recommendation System, Anime, K-Nearest Neighbors (KNN), Genre, Rating, Web-Based System.

Abstract

The rapid growth of the anime industry has resulted in an increasing number of anime titles being released every year. The large variety of available anime often makes it difficult for users to find anime that match their interests and preferences. The process of searching for anime manually requires considerable time, as users need to review genres and ratings individually. Therefore, a recommendation system is needed to assist users in finding suitable anime more quickly and efficiently. This study aims to develop a web-based anime recommendation system using the KNearest Neighbors (KNN) algorithm based on users' genre and rating preferences. The dataset used in this research was obtained from the Anime Recommendations Database available on Kaggle, consisting of 12,294 anime records. The research process includes data collection, data cleaning, attribute selection, data transformation, KNN algorithm implementation, and web-based system development. The KNN algorithm is applied to calculate the similarity between user preferences and anime data using the Euclidean Distance method. The results of this study indicate that the developed system is capable of providing anime recommendations that match users' preferences based on selected genres and ratings. The system also provides features for anime data management, recommendation searches, anime detail views, and a watchlist feature for saving anime that users intend to watch. The implementation of this system helps users find suitable anime more easily, quickly, and efficiently compared to manual searching methods.

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References

D. Datri Murdoko dan D. Bisnis Darmajaya Bandar Lampung, “Face Recognition Presensi Mahasiswa IIB Darmajaya Menggunakan Metode EigenFace,” 2023.

“Ida Aisyah"ANIME DAN GAYA HIDUP MAHASISWA"(Studi pada Mahasiswa yang Tergabung dalam Komunitas,Jakarta, 11 Maret 2019 Japan Freak UIN Jakarta).”

J. Pendidikan Sosiologi, A. Sebagai Budaya Populer, dan P. Ardi Nugroho dan Grendi Hendrastomo, “Anime Sebagai Budaya Populer... (Prista Ardi Nugroho),” 2017. Diakses: 7 Desember 2025. [Daring]. Tersedia pada: https://doi.org/10.21831/e-societas.v6i3.9099

H. Nurdin, A. Irma, A. Sulaiman, Al-Zuhri, dan M. Jannah, “ANALISIS PESAN DAN DAMPAK ANIME BERGENRE AKSI PADA MAHASISWA UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH,” 2023.

Diakses: 6 Desember 2025. [Daring]. Tersedia pada: https://www.kompirasi.com/ternyata-abg-pembunuh- balita-di-sawah-besar-seorang-wibu-psikopat/. 12 Desember

Luqyana Zakiya Almas, Yuliana Susanti, dan Sri Sulistijowati Handajani, “Penerapan Algoritma K-Nearest Neighbors dalam Sistem Rekomendasi Makanan Berdasarkan Kebutuhan Nutrisi dengan Content-Based Filtering,” Statistika, vol. 24, no. 1, hlm. 115–122, Mei 2024, doi: 10.29313/statistika.v24i1.3558.

L. Anshori, R. Regasari Mardi Putri, dan Tibyani, “Implementasi Metode K-Nearest Neighbor untuk Rekomendasi Keminatan Studi (Studi Kasus: Jurusan Teknik Informatika Universitas Brawijaya),” 2018. [Daring]. Tersedia pada: http://j-ptiik.ub.ac.id

C. P. Helena Panjaitan, “Penerapan Metode K-Nearest Neighbor Untuk Sistem Rekomendasi Paket Wisata Laut Labuan Bajo,” 2022.

J. Elektronik dan I. Komputer Udayana, “Sistem Rekomendasi Seri Animasi Jepang (Anime) Menggunakan User-Based Collaborative Filtering dan Spearman Rank Correlation Coefficient,” 2022.

E. Salim, J. Pragantha, dan M. D. Lauro, “Perancangan Sistem Rekomendasi Film menggunakan metode Content-based Filtering,” 2023.

I. Gede Iwan Sudipa dkk., DATA MINING. 2023. [Daring]. Tersedia pada: www.globaleksekutifteknologi.co.id

M. Ridwan, H. Suyono, dan Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,” EECCIS, vol. 7, no. 1, 2013.

H. A. Dwi Fasnuari, H. Yuana, dan M. T. Chulkamdi, “PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENYAKIT DIABETES MELITUS,” Antivirus : Jurnal Ilmiah Teknik Informatika, vol. 16, no. 2, hlm. 133–142, Okt 2022, doi: 10.35457/antivirus.v16i2.2445.

S. SAHRI, “PERANCANGAN SISTEM INFORMASI ANIME BERBASIS WEB MENGUNAKAN WORDPRESS PUBLIKASI ILMIAH,” 2016.

Sarwono dan Husni, “Pengembangan Website Jurnal Teknologi Informasi sebagai Media Publikasi Artikel Ilmiah,” 2023.

L. Apriliany dan D. Hermiati, “PERAN MEDIA FILM DALAM PEMBELAJARAN SEBAGAI PEMBENTUK PENDIDIKAN KARAKTER,” 2021.