Comparative Analysis of Machine Learning and Deep Learning Algorithms for Sentiment Analysis of Feedback Text on Lecturer Teaching Evaluation

Abstract

Evaluation of lecturer performance is very important because it helps in monitoring and ensuring that lecturers fulfill their duties effectively in maintaining integrity and teaching lecture material. By assessing lecturer performance based on criteria such as teaching, it can identify areas for improvement and provide support if needed. This study aims to determine the accuracy level of machine learning and deep learning combined with word-embedding for text analysis of lecturer teaching performance evaluation using preprocess techniques.The dataset consisted of 663 positive data, 552 negative data, and 465 neutral data. Successful in the results of the experiment, the training accuracy value for each classification method included KNN of 74.75%, SVM of 65.78%, RF of 98.58%, LSTM of 95.64% and Bi-LSTM of 95.91%. The test accuracy value for each classification method includes KNN of 59.82%, SVM of 62.88%, RF of 69.37%, LSTM of 70.81% and Bi-LSTM of 72.25%. The most superior method in processing data of 663 positive data, 552 negative data, and 465 neutral data by applying the word-embedding method, namely BiLSTM with a training accuracy of 95.91% and a testing accuracy of 72.25%.