Analisis klasifikasi Algoritma K-Nearest Neighboar (K-NN) pada struktur Daerah di Kota Medan - Safa Nadia Bakri; Lailan Sofinah Harahap (LKP Unity Academy, 2025)
This research aims to analyze the application of the K-Nearest Neighbor (KNN) algorithm in classifying regional structures in Medan City. Medan, one of the largest cities in Indonesia, has a variety of characteristics of regional structure that requires an appropriate analysis approach for spatial management and spatial planning. The KNN algorithm was chosen because of its ability to categorize data based on its proximity to other data points, which is very suitable for the needs of spatial planning and management. other data points, which is very suitable for the needs of regional classification analysis. In this research, the data used includes various attributes of the regional structure such as structure attributes such as population density, land use, and infrastructure in each sub-district. infrastructure in each sub-district in Medan City. In this research, the method used is statistical data processing statistical data processing to group areas with similar characteristics, using the KNN algorithm as a classification method. The classification process process involves selecting the right parameters, calculating the distance between data points, and selecting the optimal number of nearest neighbors. data points, as well as selecting the optimal number of nearest neighbors. The expected results The expected results of this analysis will provide a clear picture of the distribution pattern of the distribution pattern of the regional structure in Medan City, as well as assisting in the planning and development of a more efficient and directed city. The accuracy of the KNN model in classifying the regions will also be compared with other algorithms to assess its effectiveness and reliability in the context of this study.