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Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM

*Sumarlin Sumarlin  -  STIKOM Uyelindo Kupang, Indonesia

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In line with the growth in the academic field especially college, scholarship is a problem that is interesting to study. Several studies in the field of computers for the screening or classification scholarships have been carried out in the academic authorities to minimize the error in awarding scholarships. This study discusses the classification of PPA and BBM scholarships based on variables that have been determined by applying the k-nearest neighbor algorithm. The process of selecting awardees PPA and BBM requires a decision support system (DSS) to help provide alternative solutions. The results of the classification system will be used as a decision in awarding scholarships to students who submit. Results of testing to measure the performance of k - nearest neighbor algorithm using cross validation method, Confusion Matrix and the Receiver Operating Characteristic (ROC) curve, the accuracy obtained for PPA scholarships reached 88.33% with a value of 0.925 area under curve (AUC)  dataset of 227 records, while accuracy is obtained for fuel BBM scholarships reached 90% with a value of  0.937% AUC dataset of 183 records​​, accuracy for PPA and BBM scholarships reached 85,56% and AUC value 0,958. Because AUC values ​​were in the range of 0.9 to 1.0  the method falls within the category of very good (excellent).

Keywords: Decision Support System; K-nearest neighbor; Classification; Scholarship

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Last update: 2024-05-24 23:08:09

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