Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Negoro, Izz Sabdo
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of Hybrid Entropy and MABAC Methods in a Decision Support System for Selecting The Best Anime Series Astuti, Femi Dwi; Winarno, Basuki Heri; Negoro, Izz Sabdo
Journal of Applied Informatics and Computing Vol. 10 No. 5 (2026): October 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i5.13915

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.
Co-Authors Femi Dwi Astuti Winarno, Basuki Heri