PENERAPAN ALGORITMA K-NEAREST NEIGHBOUR TERHADAP KLASIFIKASI NILAI PESERTA TOEFL (STUDI KASUS : MAHASISWA JURUSAN TEKNOLOGI INFORMASI) --

ABSTRACT
This research is set in the background by the need for English because it is one of the most important languages and has a role as an international language. TOEFL is a standardized test that is usually used to determine a person's English skills as a speaker of a foreign language. The Test of English as a Foreign Language (TOEFL) consists of three sections of questions, including listening, grammar and reading.The purpose of the study was to apply the K-Nearest Neighbor (K-NN) method in classifying toefl participants' scores at the elementary level, lower intermediate level, upper intermediate level or advanced level. In this study, researchers selected the K-Nearest Neighbour (K-NN) algorithm. In this study, researchers selected the K-Nearest Neighbour (K-NN) algorithm. The K-Nearest Neighbour (K-NN) algorithm is one of the simplest algorithms to solve classification problems, the K-Nearest Neighbour (K-NN) algorithm often produces competitive and significant results. Implement the K-Nearest Neighbor (K-NN) method in classifying TOEFL participants' scores whether the grades are at the elementary level, lower intermediate level, upper intermediate level or advanced level. Based on the results of the Implementation of the K-Nearest Neighbor Algorithm to classify TOEFL score data at what level the value is then at 70% training data and 30% data testing with a value of K = 3 has the highest accuracy rate managed to get an accuracy score of 79.54%
Keywords : Data Mining, Algortima K- Nearest Neighbor, Classification