IMPLEMENTASI GRAPH CONVOLUTIONAL NETWORK UNTUK MEMPREDIKSI PENGAMBILAN MATA KULIAH PILIHAN
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Ramadhan, Wahyu
Susanti, Meilia Nur Indah
Ningrum, Rahma Farah
Susanti, Meilia Nur Indah
Ningrum, Rahma Farah
Subject
Teknik Informatika
Datestamp
2023-07-17 06:54:02
Abstract :
Currently, the selection of elective courses that run only based on the wishes of students without looking at the scores of previous courses and course prerequisites, resulting in students not focusing on one concentration of elective courses. In this study, a prediction system for taking elective courses based on the scores obtained in previous courses was made, so that the study program can predict the taking of student elective courses to provide information to students so that students can focus on one concentration of elective courses and as an illustration of the study program to open classes for elective courses. In this study, the Graph Convolutional Network method is a message passing operation to accumulate neighboring vertex features into vertex features. In general, the GCN model architecture is composed of several hidden layers in the form of convolutional layers where the function of each layer is to aggregate and transform the vertex feature representation accordingly with the graph topology becoming a graph representation and propagating that graph representation to the next hidden layer. Making a prediction process for the maintenance of student courses using the Graph Convolutional Network method consists of several stages. Starting from data collection and understanding, data preparation with data sharing, data modeling. From the training process, a model with an accuracy of 62,50% was obtained. From the results of this study with the editorial using the Graph Convolutional Network, students will find out the recommended elective courses based on the predictions of the courses that have been taken.
Currently, the selection of elective courses that run only based on the wishes of students without looking at the scores of previous courses and course prerequisites, resulting in students not focusing on one concentration of elective courses. In this study, a prediction system for taking elective courses based on the scores obtained in previous courses was made, so that the study program can predict the taking of student elective courses to provide information to students so that students can focus on one concentration of elective courses and as an illustration of the study program to open classes for elective courses. In this study, the Graph Convolutional Network method is a message passing operation to accumulate neighboring vertex features into vertex features. In general, the GCN model architecture is composed of several hidden layers in the form of convolutional layers where the function of each layer is to aggregate and transform the vertex feature representation accordingly with the graph topology becoming a graph representation and propagating that graph representation to the next hidden layer. Making a prediction process for the maintenance of student courses using the Graph Convolutional Network method consists of several stages. Starting from data collection and understanding, data preparation with data sharing, data modeling. From the training process, a model with an accuracy of 62,50% was obtained. From the results of this study with the editorial using the Graph Convolutional Network, students will find out the recommended elective courses based on the predictions of the courses that have been taken.
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