Open AccessSelection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic
Author(s) -
Yohanssen Pratama,
Monalisa Pasaribu,
Joni Nababan,
Dayani Sihombing,
Dicky Gultom
Publication year - 2021
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 - 10.1088/1742-6596/1933/1/012069
Subject(s) - scholarship , fuzzy logic , computer science , genetic algorithm , population , crossover , selection (genetic algorithm) , machine learning , initialization , data mining , set (abstract data type) , algorithm , artificial intelligence , sociology , political science , programming language , demography , law
PPA (Academic Achievement Improvement) scholarship acceptance Institut Teknologi Del is open annually for students who have improved academic performance but are economically disadvantaged. Every year there is significant increasing of the number of students to apply for the scholarship. As a result, it becomes more difficult to determine students who are entitled to receive the PPA scholarship since the procedure was done manually. As a result, the selection time needed to produce a decision is longer. In response to this, the authors propose the implementation of Genetic Algorithms and Fuzzy Logic in determining scholarship recipients as well as using data from the Institut Teknologi Del Information System (CIS) using student data of 2016, 2017 and 2018. The data obtained is done by data pre-processing, then being input into the system. In the system the state has been set with CR = 0,9, MR = 0,1 and fuzzy parameters will be used. Following the genetic algorithm stages of population initialization, fitness evaluation, selection, crossover, mutation, and elitism, the fuzzy logic process is carried out in the form of fuzzyfication, inference and defuzzyfication. After the fuzzy process is completed, 10 scholarship recipient student data are obtained. Since the results of the membership function provided by the genetic algorithm always changed, it caused different result in each experiment. Four data similar to the original data of the scholarship recipient was finally shown in the 37 th experiment from the total of 50 experiments carried out.