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Br Barus, Maya Theresia
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Computer Science & IT

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CLASSIFICATION OF STUDENT SCHOLARSHIP ACCURATE CLASSIFICATION USING THE K NEAREST NEIGHBOR ALGORITHM Sinaga, Bosker; Marpaung, Meman; Br Barus, Maya Theresia; Laia, Erlina
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1217

Abstract

Providing the right student scholarships will produce graduates who are permanent and reliable. Scholarships are usually given in the form of financial assistance, both education money and even pocket money (living expenses). This research classifies the accuracy of awarding student scholarships, where in the research conducted the number of scholarship recipients was not on target, resulting in the scholarship recipients not being serious about attending lectures and even dropping out of studies. The research method, namely the survey research method, is a research method that is carried out using surveys or data collection through research respondents. The algorithm used in analyzing the data is the KNN algorithm. The purpose of this study was to apply data mining using the KNN algorithm to find out the classification results of the accuracy of awarding student scholarships. The results of this study make a classification of scholarships, namely 19 students are eligible to receive scholarships and 49 students are not eligible to receive scholarships.
Co-Authors Bosker Sinaga Laia, Erlina Marpaung, Meman