Pengenalan Citra Daun Herbal Menggunakan Metode CNN dan Ekstraksi Fitur Tekstur LBP - Setyawan, Marselinus Putu Harry; Darmawan, I Dewa Made Bayu Atmaja (Jurnal Nasional Teknologi Informasi dan Aplikasnya; Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022DO - 10.24843/JNATIA.2022.v01.i01.p40, 2022)
Indonesia is one of the countries with the highest number of herbal plant species in the world. However, it is not linear with people's knowledge about herbal plants and their health benefits. Leaves are one of the characteristics of plants that can be used to identify plant species because each plant has leaves and is easier to distinguish than tree bark. This research uses the Local Binary Pattern (LBP) method to obtain the texture features of leaf herbal plants, and the Convolutional Neural Network (CNN) to perform the classification. The highest accuracy was obtained with an epoch value of 25 and a batch size of 32. This combination resulted in a model with an accuracy of 95%, and when tested with validation data it produced an accuracy of 84%. Overall, the model that was built was able to identify the types of herbal plants very well.