Prediksi Prestasi Akademik Mahasiswa Bekerja Paruh Waktu Menggunakan Artificial Neural Network

Abstract

Abstrack -Students who work part-time are required to be able to divide their time effectively and efficiently between time for work and time for study. The prediction of those who study while working is expected to be one of the policy considerations for the academic side so that students who work while working can complete their study period on time. This research begins with the stage of collecting data from students who are studying while working for the next data cleaning process. The data is then divided into two groups of data, namely training data and testing data which are normalized by the minmax method. Neural network algorithms are used to predict the results of studies for those who study while working which are categorized into 3 labels. Optimization is carried out on the parameters by utilizing the optimize parameter tool. In model testing, the parameters displayed are training cycle, learning rate, momentum, accuracy and RMSE value with a range of learning rate and momentum values from 0.1 to 0.9, with a sigmoid activation function. The best value validation was obtained in the training cycle 201, learning rate 0.74, momentum 0.9 with an accuracy value of 89.62%, RMSE 0.263 with a value of k-fold = 3.

Scales Feature Foot Scanners as Parameters of Flat Feet in Children

“…Studies about the implementation of machine learning in predicting or classifying someone's academic talent are enough to show that it is feasible to use this method to help determine a children's talent based on their hobbies and activities. Some of these studies include college students' talent classification based on classroom behavior using the convolutional neural network algorithm [4], predicting academic talent capacity using the decision tree algorithm [5], and and using a neural network to predict students' academic performance [6]. Some studies on sports talent assessment using machine learning show that machine learning is an excellent tool to predict and analyze someone's sports talent.…”