Analysis of Student Performance Determination Using K-Means Clustering and Perceptron at Private Madrasah Tsanawiyah Koto Anau

Abstract

The way we normally evaluate how well students are doing at MTsS Koto Anau is not good enough. This is because it does not show us the hidden patterns of how students learn. This study wants to use a way to evaluate how well students are doing. It uses K-Means Clustering and Perceptron together to get an idea of what helps students achieve. We used a two step method. First K-Means Clustering puts students into three groups based on how similar they're in terms of school work. Then we use these groups to teach the Perceptron model. The information we used came from the office at MTsS Koto Anau. It had details about 113 students. Included things like their average grades how often they came to school and their final exam scores. When we tested the model it was very good at putting students into the group. It was 99 percent of the time. This shows that the model is very good, at figuring out which group a student belongs to. The main thing we found out is that using K-Means Clustering and Perceptron is a good way to understand how students learn and predict how well new students will do. This study helps because it gives madrasahs an useful way to analyze student data. This means they can make learning plans that're just right for each student. The MTsS Koto Anau can use this to make sure students get the help they need to do their best.