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Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad

A recommender system is a system that analyses data and makes recommendations to the user based on their preferences, and rating. Anime is one of the famous entertainments beside movie. Anime has a great community online then since pandemic hit us on March 2020, many people start to watch anime to s...

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Main Author: Ahmad, Luqmanul Hakim
Format: Thesis
Language:English
Published: 2022
Subjects:
Database management
Online Access:https://ir.uitm.edu.my/id/eprint/95047/1/95047.pdf
https://ir.uitm.edu.my/id/eprint/95047/
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id my.uitm.ir.95047
record_format eprints
spelling my.uitm.ir.950472024-05-12T07:57:51Z https://ir.uitm.edu.my/id/eprint/95047/ Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad Ahmad, Luqmanul Hakim Database management A recommender system is a system that analyses data and makes recommendations to the user based on their preferences, and rating. Anime is one of the famous entertainments beside movie. Anime has a great community online then since pandemic hit us on March 2020, many people start to watch anime to spend their time at home which make the community bigger. People can watch anime through steaming website that available but with the increasing list of anime month by month, year by year, it makes harder to choose preferred anime. People spend a lot of time than necessary to pick their preferred anime from the massive list of anime. The goal of anime recommender system is to provide a recommendation list of anime to the user based on their preferred anime. So, users will spend less time to search for anime. K- nearest neighbor algorithm is chosen to be implemented in the recommender system. This algorithm will receive an input consist of anime name then it will calculate distances between other anime in the existing dataset. Next, the 10 nearest distances between data and the input will be given to the user. As a results, the recommender system using k- nearest neighbor is successfully be implemented in this project. This recommender system model can be considered as reliable after undergo evaluation phase. The system had a low value of both metrics measured which are 0.67 of RMSE and 19.87 of MAPE. This project report end with summary of project been made to highlight the limitations, contribution, and recommendation for the project. 2022 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/95047/1/95047.pdf Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad. (2022) Degree thesis, thesis, Universiti Teknologi MARA, Terengganu.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Database management
spellingShingle Database management
Ahmad, Luqmanul Hakim
Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
description A recommender system is a system that analyses data and makes recommendations to the user based on their preferences, and rating. Anime is one of the famous entertainments beside movie. Anime has a great community online then since pandemic hit us on March 2020, many people start to watch anime to spend their time at home which make the community bigger. People can watch anime through steaming website that available but with the increasing list of anime month by month, year by year, it makes harder to choose preferred anime. People spend a lot of time than necessary to pick their preferred anime from the massive list of anime. The goal of anime recommender system is to provide a recommendation list of anime to the user based on their preferred anime. So, users will spend less time to search for anime. K- nearest neighbor algorithm is chosen to be implemented in the recommender system. This algorithm will receive an input consist of anime name then it will calculate distances between other anime in the existing dataset. Next, the 10 nearest distances between data and the input will be given to the user. As a results, the recommender system using k- nearest neighbor is successfully be implemented in this project. This recommender system model can be considered as reliable after undergo evaluation phase. The system had a low value of both metrics measured which are 0.67 of RMSE and 19.87 of MAPE. This project report end with summary of project been made to highlight the limitations, contribution, and recommendation for the project.
format Thesis
author Ahmad, Luqmanul Hakim
author_facet Ahmad, Luqmanul Hakim
author_sort Ahmad, Luqmanul Hakim
title Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
title_short Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
title_full Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
title_fullStr Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
title_full_unstemmed Anime recommender system using K-nearest neighbor algorithm / Luqmanul Hakim Ahmad
title_sort anime recommender system using k-nearest neighbor algorithm / luqmanul hakim ahmad
publishDate 2022
url https://ir.uitm.edu.my/id/eprint/95047/1/95047.pdf
https://ir.uitm.edu.my/id/eprint/95047/
_version_ 1800100639736332288
score 13.211869

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