Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.
Indonesia is one of the countries with high plant diversity. Almost every region in Indonesia has distinctive plants and may not be present in other countries. Based on these facts required a strategic step to record and identify plants in Indonesia. One method that can be used to leaf image feature extraction is the Gray Level Co-occurrence Matrix (GLCM). This research will implement k-Nearest Neighbor (k-NN) method to classify type of plants based on leaf texture. The classification result based on GLCM using k-NN classifier showed that the accuracy using k = 3 was 83%. The use of parameter k influence classification results, the greater the value of k then the accuracy would be smaller. Classification errors for some types of leaf images occurred because the value extraction traits generated by GLCM was very similar and had a small range of values.
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