ANALISIS NILAI AKURASI PENGOLAHAN CITRA PENDETEKSIAN RINTANGAN KERJA TRAKTOR MENGGUNAKAN K-MEANS CLUSTERING

Abstract

The use of agricultural tractors as mechanical aids for tillage using agricultural tractors can make work lighter, faster, more efficient and do big jobs in a relatively short time. Along with the development of technology, many innovations have been developed by humans, including in the field of digital image processing. Segmentation is one of the methods in digital image processing to distinguish objects in an input image. One of the algorithms that can be used for image segmentation is K-Means. Many algorithms are used in image classification. Algorithms that can be used to complete the supervised classification include Paralleepiped, Minimum Distance, Mahalanobis Distance, Maximum Likelihood, Naive Bayesian, K-Nearest Neighbor. Algorithms that can be used to solve unsupervised classifications include Isodata, K-Means, Improved Split and Merge Classification (ISMC), and Adaptive Clustering (CA). Based on this description, this research was conducted to facilitate the data processing process, it is necessary to have a data grouping system to determine decisions in the analysis to determine the level of accuracy in the detection of obstacles resulting from image processing where obstacles are detected, noise and obstacles are not detected. processed and grouped based on their characteristics so that it is known that the cluster is low, medium cluster and high cluster to be able to analyze the data, it is necessary to have a deeper analysis using the K-Means Clustering method and implement K-Means results into Rapidminer to see the results of visualizing the K-Means algorithm data grouping.