Penerapan Algoritma K-Nearest Neighbor Untuk Penentuan Peminatan Studi (Studi Kasus : Program Studi Teknik Informatika STMIK Amik Riau)

Nora Lizarti, Aniq Noviciatie Ulfah


Concentration of study at STMIK Amik Riau is a choice of interests based on special abilities and student interests. Informatics Engineering Program at STMIK Amik Riau has two subjects of interest, namely business and networking. The studi concentration is tailored to the abilities and interests of students and must be chosen properly and correctly because it is very influential on the final assignment and graduation level of students. The classification system of interest is one of the solutions to solve the problem of choosing concentration in the study program because it is considered capable of providing good and appropriate spinning recommendations. K-Nearest Neighbor (K-NN) is one of classification algorithm that can be used as a solution in classifying data. In this study, the data used was obtained from the value of prerequisite courses during semester one to semester five. Data is processed by building applications that implement the K-NN algorithm using PHP and MySQL. The output of the system have 100% accuracy compared to the results of manual calculations using Microsoft Excel. The Testing process used  RapidMiner  software to measure algorithm performance. The results of the tests carried out on 183 training data and 100 test data stated that the K-NN algorithm had performance with the results of Acuracy, Recall, Precision, Measure, and Classification Error with values of 98%, 100%, 100%, 91.67%, and 2% . This study can provide a system that can help giving some study concentration recommendations  to the student of Informatics Engineering Program at STMIK Amik Riau.


Study Interest, Classification, K-Nearest Neighbor (K-NN) Algorithm

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