@thesis{thesis,
		author={Djamain  Yasni and Indrianto  Indrianto and PUTRA  WINGGAR WAHARI},
		title ={KLASTERISASI HASIL PANEN SARANG WALET MENGGUNAKAN METODE K-MEANS CLUSTERING},
		year={2021},
		url={http://156.67.221.169/4894/},
		abstract={Clustering of the quality level of swallow's nests will be used to assist swallow breeders in
producing swallow nests that are maximum in terms of quality. With this system, breeders
can determine the quality of swiftlet nests more efficiently and without the help of a
consultant. In this study, the author applies a method that is suitable for this case, namely
the K-Means Clustering Method. The data used is an RGB (Red Green Blue) image in the
image. In this system, the quality of swallow's nests is divided into 3 (three) parts, namely
class 1 for the best quality swallow's nest with a color image that tends to be white, class 2
for a good quality/enough swallow's nest with a color image that tends to be yellow, and
class 3 for swallow's nest of poor quality with a color image close to brown. And based on
the results of testing the level of accuracy and precision produced by the system using 20
samples, it shows that the results for testing using the Confusion Matrix get test results of
76.9% for accuracy and 77.4% for precision.}
		}