BINARY CLASSIFICATION USING SINGLE LAYER PERCEPTRON ON COMPUTER LAB ASSISTANT APPLICANT QUESTIONNAIRE DATA - Nur Shafwa Aulia Sitorus; Armansyah (Universitas Islam Balitar, 2025)
Students' interest in becoming computer lab assistants needs to be analyzed to understand the factors that influence it. This research uses a Single Layer Perceptron (SLP) Neural Network to perform binary classification on the questionnaire data of lab assistant applicants collected through Google Forms. The SLP model was trained with initial weights and biases of zero, a learning rate of 0.1, and a threshold of 0.5. The results show that within two epochs, the model was able to recognize patterns with an accuracy of 75%. This model has a precision of 100%, but a recall of only 50%, resulting in an F1 Score of 67%. These findings indicate that SLP can process questionnaire data well and has the potential to be applied to larger datasets to improve model performance.