Analisis Dan Penerapan Algoritma Naȉve Bayes Untuk Klasifikasi Kelayakan Penerimaan Beasiswa PIP (Studi Kasus : SMPN 7 Kota Jambi)
Keywords:
Kata Kunci: Data mining; Klasifkasi; Algoritma Naïve Bayes; Rapid Miner; Penerimaan Beasiswa PIPAbstract
Scholarships are financial aid provided to the community with the aim of supporting education. Scholarship programs were established to help students overcome financial problems in completing their education. Scholarships are awarded selectively according to individual needs. To determine the accuracy of the predicted results of eligibility for the Indonesia Pintar Scholarship Program (PIP), classification techniques can be used as part of data mining. Testing is done by using all attributes and the best attributes on 3 options test, among others Use Training Set, 5 Fold Cross Validation, 10 Fold Cross Validation. Data on the acceptance of PIP scholarships has a high accuracy in the options test Use Training Set (best attribute) which is 93.18% compared to other tests. As for the lowest accuracy is 5 Fold Cross Validation 10% (2021 – 2022) which obtained an accuracy of 81.82%. Naïve Bayes algorithm can be said to be one of the effective algorithms both from the calculation and the final result where the test can be used as a foundation related to scholarship acceptance.
Keywords: Data Mining, Classification, Naïve Bayes Algorithm, Rapid Miner, PIP scholarship recipients
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References
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