@thesis{thesis,
		author={Asri  Yessy and Fathullah  M.Akmal and Prayitno  Budi},
		title ={ImpIementasi Algoritma K-Nearest Neighbor Untuk Prediksi Kebutuhan Manajemen Aset Sekolah Berbasis Web 
(Studi Kasus: Man 1 Banda Aceh)},
		year={2020},
		url={http://156.67.221.169/4299/},
		abstract={To date, the needs of schools related to the purchase of goods to be provided each year still face difficulties in determining the number of goods so that it requires applications that can facilitate the prediction of the needs of school goods. Based on these problems, the design and creation of applications used to predict asset requirements with the K-Neighbor Nearest (KNN) approach. The method used is a data mining step known as the Cross-Industry Standard Process for Data Mining (CRISP-DM). Test results at MAN 1 Banda Aceh showed the accuracy of the K-Nearest Neighbor (KNN) method using merchandise supply data, the data consisted of 32 tranings and 32 test data obtained the result of a 98.5% accuracy score and an error value of 1.5%, with the highest error value of 4.8% and the lowest error value of 0%.}
		}