@thesis{thesis,
		author={MASRUROH  A Isatul},
		title ={KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA 
CALIFORNIA DALAM RUANG WARNA HSV (HUE SATURATION VALUE) DENGAN ALGORITMA K-NEAREST NEIGHBORS},
		year={2022},
		url={http://eprints.peradaban.ac.id/1455/},
		abstract={Papaya is one of the horticultural commodities developed in Indonesia. Papaya fruit in addition to being consumed directly can also be used to make various types of preparations. In addition, papaya fruit is in great demand by the public at home and abroad, this has led to a large number of consumer demands for papaya fruit for consumption and for resale. So that it proves that this one agricultural product has become a global need that is in great demand and sought after. The current condition of the papaya plantation sector is to determine the papaya harvest based on the color of the fruit skin, the ripeness of the papaya fruit starts from raw, ripe (half ripe) and ripe so that researchers propose an idea to answer the problem of determining the maturity of papaya fruit, which is mostly still being done. manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is due to the subjective determination of the maturity level by farmers. Based on these problems, a system was made to classify the level of ripeness of papaya fruit by utilizing the HSV color feature using the K-Nearest Neighbor (K-NN) algorithm. This classification uses image processing using MATLAB software to create a classification system with three classification classes, namely raw, semi-cooked and ripe. The classification generated using the K-Nearest Neighbor (K-NN) algorithm shows an accuracy of  86,6667 %.}
		}