Implementation of Hybrid Entropy and MABAC Methods in a Decision Support System for Selecting The Best Anime Series

Femi Dwi Astuti; Basuki Heri Winarno; Izz Sabdo Negoro

doi:10.30871/jaic.v10i5.13915

Authors

Femi Dwi Astuti Universitas Teknologi Digital Indonesia
Basuki Heri Winarno Universitas Teknologi Digital Indonesia
Izz Sabdo Negoro Universitas Teknologi Digital Indonesia

DOI:

https://doi.org/10.30871/jaic.v10i5.13915

Keywords:

Decision Support System (DSS), Entropy Method, MABAC Method, Anime Series Recommendation

Abstract

Anime is a form of animation originating from Japan that has become an integral part of the lifestyle of teenagers in various countries, including Indonesia. With the large number of anime titles released annually, viewers often struggle to select series that align with their preferences. This study aims to integrate the Entropy and MABAC methods into a Decision Support System (DSS) to assist users in identifying the best anime based on relevant criteria, such as user ratings, popularity, community membership, episode count, and duration. The Entropy method is used to determine criterion weights, while the MABAC method provides a more accurate evaluation during the selection process. Through this approach, it is expected that users will receive more precise and objective anime recommendations and find it easier to navigate the available options. Among the 35 anime titles analyzed, the results indicate that the best anime series is the 16th title (A16), with an S-score of 0.17699. Following testing via comparison with the TOPSIS method, A16 remained the best alternative; this demonstrates that the Entropy-MABAC method is consistent in determining the best anime.

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