Warman, Budi Tia (2025) Penerapan Algoritma K-Nearest Neighbor Untuk Prediksi Kelulusan Siswa SMKS KORPRI Duri. Other thesis, Universitas Islam Riau.

Text skripsi_183510697_watermark.pdf - Published Version Restricted to Registered users only Download (5MB) | Request a copy

Abstract

Graduation has an important role in academic achievement for students and as an indicator of educational success for schools. Therefore, it is necessary to predict graduation in order to minimize the level of student failure. However, this is a problem for SMKS Korpri Duri because the determination of graduation in the school is still done manually so that there are still human errors and the level of accuracy is not good, which results in some students not graduating on time. Thus to overcome this, researchers are interested in conducting research on predicting graduation using data mining techniques which aim to make it easier to predict graduation in order to get a good quality of graduates with a higher percentage. Data mining is needed to process large amounts of data, especially student graduation data which continues to grow every year so that the information produced becomes more accurate in current and future developments. The data mining method used in this research is using the K-Nearest Neighbor (KNN) algorithm. KNN is one of the best and widely used data mining classification algorithms. K in KNN is a variable number of nearest neighbors that will be selected for the classification process. This research produces the best K in the K = 5 experiment with an accuracy rate of 100%. This system is implemented with flask using the python programming language.

Actions (login required)

View Item