IMPLEMENTASI ALGORITMA GENETIKA DAN FUZZY TSUKAMOTO DALAM PREDIKSI NILAI SKRIPSI

Abstract

A thesis is a scientific paper written by a student as one of the requirements for graduating from the Bachelor degree program (S1). Thesis is considered important because it is the final evaluation of students in studying in tertiary institutions. One indicator of students' success in completing their thesis is their understanding of the subjects that support their thesis. Therefore we need a system that can predict the value of the thesis so that it can be a benchmark and help in the process of improving the quality of learning. Based on these problems, this study uses a Genetic Algorithm to determine which subjects have an effect on the value of the thesis. Furthermore, with Fuzzy Tsukamoto, the value of the thesis will be predicted based on the influential subjects. From the trials conducted, the best parameters were population 65, generation 150, a combination of cr 0.7 and mr 0.2. Based on the results of three experiments with the Genetic Algorithm and Fuzzy Tsukamoto, the average RMSE error value is 5.494839313.