JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI NILAI KELULUSAN SIDANG (STUDI KASUS : STMIK KAPUTAMA BINJAI ) - Siregar, Retni Noviyanti; Kadim, Lina Arliana Nur; Maulita, Yani (STMIK KAPUTAMA, 2019)
Thesis session is a process that must be followed by a student in order to account for the thesis that has been done. Thesis trial scores determine student graduation, and student graduation rates are used as a measure of campus quality. The problem is that many students are depressed and afraid in the face of a thesis hearing, not a few among students who are stressed in facing thesis and some even delay the work of the thesis so that it affects the trial value obtained. Besides that there are students who have good IP but the trial value is not good, and vice versa. This method the Artificial Neural Network using the Backpropagation algorithm was chosen because it was able to predict the graduation value of the thesis trial based on input from the value of the semester IP from semester I to semester VII and the value of the trial. The study was conducted in two ways, namely training and testing. The training process aims to recognize or look for expected results by using a lot of training, so that it will produce the best pattern for training the data. After the training reaches the goal based on the best pattern, it will be tested with new data to see the accuracy between the targets using Matlab R2014a software. Based on the results of testing using Matlab R2014a software, the results are convergent, with a target error of 0.2. From the results of the training and the tests carried out, it was predicted that the graduation score of the thesis trial was predicted to be 0.8727. This research can also help predict the graduation score of thesis students at STMIK Kaputama Binjai