Indani, Agnes Sintya (2023) Klasifikasi Penerimaan Beasiswa Peserta Didik Baru di SDI Kenggu Menggunakan Algoritma K-Nearest Neighbor dan Naïve Bayes. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

Scholarships are financial incentives for a person or group of people to help smooth education financing. The conditions for getting a scholarship must follow the rules that have been applied, namely on the income of parents, the number of dependents and the distance of departure of female students. SDI Kenggu applies scholarship acceptance manually so that the process of accepting scholarships for new students is less effective. The problem is clear that the scholarship acceptance rate has not been fully targeted because there are still female students from families who can afford to get scholarships, on the contrary from families who cannot afford not to get scholarships. The methods used in this study are KNN and Naive Bayes. After comparing the accuracy results of the two methods, the researcher concluded that the provision of scholarships to new students at SDI Kenggu based on students was less able to recommend the KNN algorithm in accepting new student scholarships because the accuracy results of the method were higher than the Naïve Bayes algorithm. Classification using KNN and Naïve Bayes Algorithm in Scholarship Acceptance of New Students DI SDI Kenggu found that the use of KNN Algorithm was superior with higher accuracy results of 71% and accuracy results from Naïve Bayes by 69%, while female students who did not receive scholarships from KNN Algorithm as much as 29% and Naïve Bayes 31%. The results of this calculation were obtained from the number of student data as many as 66 people. Comparison of the use of KNN and Naïve Bayes Algorithm results in the accuracy of the K-Nearest Neighbor Algorithm superior to the Naïve Bayes Algorithm.

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