Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron

Abstract

The implementation of online distance learning affects the psychosocial aspects of students so that a combination of offline and face-to-face learning is carried out using Hybrid Learning. The problem in this research is the analysis of the competencies that will be generated, student characteristics, face-to-face interactions, online learning strategies, and lecturers by calculating the data from the Hybrid Learning evaluation to get the best level of accuracy using the Machine Learning technique method. The purpose of this study is to evaluate the results of Hybrid learning using the Artificial Neural Network (ANN) method which is expected to have the right level of accuracy and prediction and tolerate errors so that it can produce good predictions and can be used to model good relationships in finding patterns in the data. . Prediction of Hybrid Learning learning outcomes during the Covid 19 pandemic using Machine Learning techniques consisting of 12 attributes with a total of 1,231 datasets of Hybrid Learning learning outcomes in 2022. The Artificial Neural Network algorithm model uses Retrive operators, Set Roles, optimization parameters, Cross Validation, Apply Models, Performance and Logs. Accuracy results show 99.35% meaning that the results of the learning evaluation using Hybrid Learning with predictions that match and turn out to be appropriate are 1039, then those that do not match are 8. The predictions are very suitable and turn out to be very suitable in 104. Artificial Neural Network with Multilayer Perceptron, with two hidden layer and the format of the first hidden layer is 2 nodes, then the second hidden layer is 5 nodes with an output of 3 nodes

Airport Runway Defect Detection Device: A Project-Based Learning Media

“…Thus, all cadets quickly understand the learning that is done in class. Then, Anwar et al (2022) successfully evaluated the accuracy of predicting learning outcomes with artificial neural networks. The device's performance for feature extraction methods and picture data processing is still being worked on.…”