IMPLEMENTASI METODE GREY LEVEL CO-OCCURRENCE MATRIX UNTUK MENGIDENTIFIKASI PENYAKIT PADA TANAMAN KELAPA SAWIT BERDASARKAN CITRA DAUN KELAPA SAWIT

Oil palm (Elaeis guneensis Jacq.) is one of several oil-producing plants. Production costs
can also increase significantly if plant diseases are not detected and cured at an early
stage. Leaf spot disease, anthracnose disease, blast diases disease and yellow line are
diseases that often attack oil palm leaves at an early stage in nurseries. The disease can
cause complications that affect seed yields that are not good, resulting in sub-optimal oil
yields. This disease must be classified according to its type to get treatment. To be
classified, some information is required. These four diseases can be recognized visually
because they have unique color and texture characteristics. But visual observation has
some disadvantages such as subjectivity and less accuracy. Through an image,
information about plant diseases can be learned, such as: texture and color. Image
processing is one of the most widely used techniques for detecting and classifying plant
leaf diseases. The area of interest is found by using K-Means Clustering segmentation,
then extracting texture features using the Gray Level Co-occurrence Matrix Method.
while the classifier uses the Euclidean distance method. The proposed research is able to
identify leaf diseases in oil palm plants with an accuracy of 60%.
Keyword : Implementation, oil palm Disease, Gray Level Co-Occurrence.