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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Setyawan, Marselinus Putu Harry
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Computer Science & IT

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Identifikasi Citra Daun Tanaman Herbal menggunakan Convolutional Neural Network 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 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

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.
Co-Authors I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu