Konsep Algoritma KNN (K-Nearest Neigbors) dan Tips Menentukan Nilai K

This video explains classification algorithms. The KNN, or K-Nearest Neighbors, algorithm is one of the easiest to interpret. KNN works by finding the nearest neighbor according to a specified K value. After determining the K value, the next step in KNN is to analyze which neighbor output dominates. In classification or class prediction using the K-Nearest Neighbors algorithm, you must remember the keyword "who is your nearest neighbor?" Important parameters in KNN are distance calculation formulas such as Euclidean distance, Minkowski distance, Manhattan distance, etc., and how we determine the K value or neighborhood we will use. This video explains how to determine the optimal k in KNN. The KNN algorithm is also called a lazy learner algorithm because it does not train but only stores data and is provided and calculated when needed. One weakness of the KNN algorithm is computation. Other advantages and disadvantages, as well as details about the KNN algorithm, can be seen in this video. #KNNAlgorithm #k-nearestneighbor #KValue #majorityclass