Open AccessImplementation of fuzzy tsukamoto method in decision support system of journal acceptance
Author(s) -
Eka Nugraha,
Aji Prasetya Wibawa,
Muhammad Luqman Hakim,
U. Kholifah,
Robih Dini,
M. Irwanto
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1280/2/022031
Subject(s) - fuzzy logic , computer science , decision support system , set (abstract data type) , fuzzy set , data mining , artificial intelligence , operations research , machine learning , mathematics , programming language
Journal acceptance is a difficult problem to solve since in the practice it involves some reviewers who can produce different decisions from various perspectives. Therefore, a decision support system is needed to assist the reviewers to decide the acceptance of the paper. This study was purposed to develop the decision support system using the Fuzzy Tsukamoto method for journal acceptance. The Fuzzy Tsukamoto method described the relationship between input and output of the system by using a set of fuzzy if-then rules. From the comparison results, It is obtained that the accuracy from the result of comparison of the manual method, expert decision, and DSS of journal acceptance using Fuzzy Tsukamoto Method is 95% with 5% errors. Based on the results of the accuracy and error, it shows that the DSS of journal acceptance using the Fuzzy Tsukamoto Method is accurate and has high precision.