ANALISIS JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI KELULUSAN MAHASISWA BERDASARKAN DATA AKADEMIK MENGGUNAKAN ALGORITMA PERCEPTRON

Abstract

This study aims to analyze and classify student graduation based on academic data using the Artificial Neural Network (ANN) method with the Perceptron algorithm. The background of this research is the low rate of on-time student graduation caused by differences in students’ academic achievements during their studies. The academic data used in this research include Grade Point Average (GPA), total passed credits, and total failed credits. The study was conducted using data from Informatics Engineering students of Universitas Asahan class of 2021.The research method used is a quantitative method with stages including data preprocessing, classification target determination, weight initialization, perceptron training process, activation function calculation, error evaluation, and weight updating until convergence is achieved. The dataset was divided into training data and testing data to evaluate the model’s ability to classify student graduation into two categories, namely on-time graduation and delayed graduation. The system was developed using the PHP programming language and MySQL database and designed using Unified Modeling Language (UML).The results of this study indicate that the Artificial Neural Network method with the Perceptron algorithm is capable of classifying student graduation based on academic data effectively. The perceptron model is able to recognize the relationship patterns between GPA, passed credits, and failed credits variables toward student graduation status. The developed system can also assist the study program in academic evaluation and decision-making processes related to improving the quality of student graduation.