Comparative Study of Machine Learning Knn, Svm, and Decision Tree Algorithm to Predict Student’s Performance

Abstract

Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.

Performance Evaluation of Different Machine Learning Algorithms on Student Dataset Clustered by K-means Algorithm

“…Decision Trees, Random Forest, and Support Vector Machine, they observed that Support Vector Machine (SVM) obtained the highest accuracy of 91.22%. The authors concluded that the SVM model was not over t or under t. [35] Slamet Wiyono et al (2019) predicted student performance to prevent inactive students and increase the number of students graduating on time. After comparing KNN, SVM and DT algorithms on student's data, they concluded that the SVM algorithm got the highest accuracy, achieving 95% precision in predicting student performance.…”

Future Price Prediction of Pulses Using Machine Learning

“…The SVM system can accurately predict 311 active students and 53 non-active students, as can be observed. After the overall accuracy calculation is performed, it is found that SVM has the best classification accuracy of 95 Slamet Wiyono et al [4] have used Multiple Linear Regression (MLR) to predict food prices, especially in the modern market, based on the predicted prices, then a decision support system is made to make an alternative ranking of food selection accumulation. They have used a Simple Additive Weighting (SAW) method to rank alternative food staples that have nutritional weight and price.…”

Forecasting model with machine learning in higher education ICFES exams

“…The variable selection was made through the documents with the needed information acquired from the application of the use of information and the FTP server (for transferring files), based on the studies carried out by several authors such as Hanushek [1], Colemann [2], Barrera [3] and in Finland [4] and the articles [8], [9], [17]- [19] that have shown that the results of standardized tests go beyond knowledge, characterizing variables such as student's financial capacity, identity, learning, objects they have in their home and expressions of their personality in addition to determining other variables that may be affecting the student, either at family or educational level, among others. The variables are shown as follows: Socioeconomic level, parent's education, family, parents' educational level, average overall score, academic results in critical reading, mathematics, natural and social sciences, English, and two sub-tests for quantitative reasoning.…”