@thesis{thesis,
		author={Ayuningsari  Rezky Putri and Indrianto  Indrianto and Susanti  Meilia Nur Indah},
		title ={IMPLEMENTASI METODE K-MEANS CLUSTERING POTENSI KECEPATAN ANGIN UNTUK PEMBANGKIT LISTRIK TENAGA ANGIN DI ITPLN},
		year={2022},
		url={http://156.67.221.169/5881/},
		abstract={Wind energy is flexible energy because it can be used anywhere. One of the potential applications of wind energy is in high-rise buildings. PLN Jakarta Institute of Technology is one of the universities in Jakarta which has a building height of 53.5 m and eleven floors. Then an application for determining the Wind Potential was made by applying the K-Means Algorithm method. The K-Means algorithm is a centroid model that uses centroids to create clusters. This method partitions data so that data with the same characteristics are grouped into one cluster, and data with different characteristics are grouped in another cluster. This study aims to help the ITPLN campus to determine whether the wind potential is included in the low, medium, and hight categories. In this study using 13 attributes, using the K-Means algorithm to get data classification in cluster C1 there are 85 data, then 184 data in cluster C2 and 44 data cluster C3. The iteration of the k-means algorithm stops at the 3rd iteration and the results will be displayed in the application. In testing the accuracy of the system using the confusion matrix, the result is 93% accuracy in the number of data records that are classified correctly by the algorithm.}
		}