Teknik Data Mining Dalam Prediksi Jumlah Siswa Baru Dengan Algoritma Naive Bayes

Aston Maruli Sitompul(1*), S Suhada(2), S Saifulah(3),

(1) STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(2) AMIK Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(3) STIKOM Tunas Bangsa, Pematangsiantar, Sumatera Utara, Indonesia
(*) Corresponding Author


Sukosari Public Elementary School 095126 is an elementary school located in Gunung Malela sub district, Simalungun Regency. Prediction is an important tool in planning effective and efficient, especially in the field of education. In the modern world knowing the situation to come is not only important to see the good or bad but also aims to make forecast preparation. An important step after a prediction has been made is to verify the prediction in such a way that it reflects past data and the underlying system that underlies the request. As long as the prediction representation can be trusted, the predicted results can continue to be used. Schools are formal educational institutions that systematically carry out guidance, teaching, and training programs in order to help students to be able to develop their potential both in terms of moral, spiritual, intellectual, emotional and social aspects. The inference method used is the Naive Bayes method, the system will input student data such as the criteria needed for admission of new students at SD N 095126 Sukasari by collecting data on Indonesia Smart Cards (KIP), BPJS cards, Parent income, these data will be grouped and obtained from the past period is processed into the system and training process with the Rapid Miner application.

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DOI: https://doi.org/10.30645/kesatria.v2i2.65

DOI (PDF): https://doi.org/10.30645/kesatria.v2i2.65.g65


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