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All Journal Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Bayuda Luqman Al-Farisi
Departement of Mathematics and Natural Sciences, Institut Sains dan Teknologi Annuqayah
Engineering Environmental Science Physics

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Classification of Longan Types Using The Back-Propagation Neural Network Algorithm Based on Leaf Morphology With Shape Characteristics Mohammad Waail Al Wajieh; Bayuda Luqman Al-Farisi
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 3 (2022): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v3i.196

Abstract

In Indonesia, longan fruit is abundantly accessible. The Longan fruit comes in a number of kinds, and they vary in terms of their morphology, including the characteristics of their leaves. The varieties of longan fruit are to be categorized in this study based on the shape of the leaves. The author uses the RGB color extraction function, the Grey Level Co-occurrence Matrix (GLCM), and the Shape feature to get data for each cultivar. The accuracy value is then processed using the Back-Propagation Neural Network (BPNN) technique to determine the accuracy value that will be used as a determinant of the categorization of the Longan leaf image. The eccentricity and metric parameters are key components of the method. The BPNN algorithm demonstrated its usefulness for categorizing various kinds of longan fruit leaves during testing by obtaining an accuracy of 70%.
Co-Authors Mohammad Waail Al Wajieh