Prediksi Tingkat Kerugian Peternak Akibat Penyakit pada Sapi Menggunakan Algoritma K-Means Clustering - Rian Kurniawan; Sarjon Defit; Sumijan (Jurnal Informasi dan Teknologi, 2021)
Data mining is very appropriate for processing data, producing added value from a pile of data in the form of knowledge that is not known manually. K Means Clustering The method of analyzing data and grouping them based on similarities, this method is very appropriate to predict the level of farmer losses due to disease in cattle. Predicting the level of farmer losses due to disease in cows by grouping them based on similarities and similarities of disease types, making it easier to draw conclusions. The data processed in this study were 9 data which were sourced from cow disease data in the UPTD Puskeswan Palangki from January to December 2019. Based on the analysis of these data by veterinarians on duty at UPTD Puskeswan Palangki, there are 9 types of diseases. Then the data is processed using the K means clustering method and proven using the WEKA application. The results of testing for this method are 3 diseases with a high loss rate and 6 diseases with a low loss rate. The data from the test results have been able to predict disease in cattle by grouping them into two parts, namely 3 diseases with a high loss rate and 6 diseases with a low loss rate.