Implementation of the K-Nearest Neighbor (kNN) Method to Determine Outstanding Student Classes
| Autor: | Munazhif, Nanda Fahrezi, Yanris, Gomal Juni, Hasibuan, Mila Nirmala Sari |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Sinkron : jurnal dan penelitian teknik informatika; Vol. 8 No. 2 (2023): Research Article, Volume 8 Issue 2 April, 2023; 719-732 |
| ISSN: | 2541-2019 2541-044X |
| DOI: | 10.33395/sinkron.v8i2.12227 |
| Popis: | Education being one factor supporting students / I to be able to increase their knowledge. Each student has their own potential that they have obtained in the world of education. Therefore, every school has created an education program that functions to increase the potential of high achieving students. The program is a flagship class program. What is meant by a superior class program is a process of selecting and classifying students to be placed in the classroom superior (grade student / I achievement). Therefore, this study aims to implement classification on student data using the K-Nearest Neighbor (kNN) algorithm. K-Nearest Neighbor (kNN) is a method used to classify data based on training data (data set). The data that the writer will use is student data of 60 student data. In this classification using the kNN method aims to classify data on students who are eligible to enter the superior class (class of outstanding students). The first step is the process of determining data requirements. Then cleaning or pre-processing and the next is to design a widget model of the kNN method on the orange application to carry out the data classification process. The test results using 60 student data using the KNN method and using the Confusion Matrix obtained an Accuracy value of 91.6%, then a Precision value of 89.2% and a Recall value of 92.5%. The conclusion is that this study succeeded in obtaining a method that the best and also get the best results for Classification of superior student classes. |
| Databáze: | OpenAIRE |
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