Klasifikasi Data Menggunakan Algoritma K-Means Clustering Dan Naive Bayes Classifier Berdasarkan Analisa Tekstur Metode Gray Level Co-Occurrence Matrix (GLCM) - Samso Supriyatna (Institut Sains dan Teknologi Nasional, 2023)
ABSTRACT Data classification in the system is widely used in various fields of information technology, data classification is obtained by analyzing objects based on the level of similarity and mapping the characteristics that are protected in the object based on the specified cluster. The use of classification in this study by use texture patterns contained in data by use K-Means Clustering algorithm and Naive Bayes Classifier. The K-Means Clustering algorithm is the process of grouping data based on the same characteristics, while Naive Bayes Classifier use probability and statistic calculations. This study aims to analyze data through texture patterns and the level of accuracy generated through K-Means Clustering algorithms and naïve bayes classifiers. Testing data on textures based on extract parameters with contrast and correlation classifications in GLCM (Gray-Level Co-Occurrence Matrix) methods and calculating the distance expressed in pixels with angle intervals of 0º, 45º, 90º, and 135º. Texture imagery is classified into three clusters by comparing accuracy values obtained. The accuracy value resulting from testing using the K-means Classifier algorithm is very good with an accuracy value of 100% and the accuracy value resulting from testing using the Naive Bayes Classifier method is very good with an accuracy value of 100%. Keywords: K-Means Clustering, Naive Bayes, and Classifier