Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Klasifikasi Tingkat Literasi Siswa
DOI:
https://doi.org/10.55606/jutiti.v5i3.6086Keywords:
classification, K-Nearest Neighbor, Literacy Level, MySQL, Student LiteracyAbstract
Literacy is a fundamental skill in reading, thinking, and writing that plays an important role in improving the quality of education. Indonesia faces serious challenges in literacy, and according to 2020 data from Statistics Indonesia, only 10% of Indonesians have an interest in reading. This study aims to implement the K-Nearest Neighbor (KNN) algorithm to classify students' literacy levels objectively and accurately. The research method used a quantitative approach with data collection through questionnaires administered to fourth, fifth, and sixth grade students at UPT SD Negeri 22 Barung-Barung. The variables used were reading books, reading duration, internet duration, and library visits. The data was divided into training data and 20 testing data. The classification process was carried out through the stages of data normalization, determining the value of K=5, calculating the Euclidean distance, and determining categories based on the majority of the nearest neighbors. The system was designed web-based using PHP and MySQL to support the automatic simulation process. The results showed that the KNN algorithm was able to classify students' literacy levels into three categories, namely low, medium, and high, with an accuracy rate of 55.00%. From the 20 testing data, there were 11 correct predictions and 9 incorrect predictions. The implementation of this system is expected to assist and serve as a basis for schools in designing more effective literacy interest improvement programs and is expected to increase
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