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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology
Desiana Nur Kholifah
Software Engineering, Universitas Bina Sarana Informatika, Jakarta, Indonesia
Computer Science & IT

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IDENTIFICATION OF HERBAL PLANT BASED ON LEAF IMAGE USING GLCM FEATURE AND K-MEANS Recha Abriana Anggraini; Fanny Fatma Wati; Muhammad Ja’far Shidiq; Ade Suryadi; Haerul Fatah; Desiana Nur Kholifah
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (983.958 KB) | DOI: 10.33480/techno.v17i1.1087

Abstract

Medicinal plants are one of the groups of plants that have enormous benefits for humans because they can help the medical process for healing disease. Herbal plants can be used as ingredients for medicines, medicines produced from herbal plants are also natural. Lack of knowledge of herbal plants causes people to prefer chemical-based medicines to help cure their diseases, even though chemical-based drugs have side effects on human health. This study aims to identify types of herbal plants based on the extraction of contrast, correlation, energy, and homogeneity features as well as shape recognition based on metric and eccentricity values. The method used in this research is GLCM features and K-means clustering. In this study, the data used consisted of 352 data divided into 320 training data and 32 testing data. This research succeeded in identifying and classifying herbal plant species using GLCM features and K-means clustering segmentation with an average accuracy value of 85.94%.
Co-Authors Ade Suryadi Anggraini, Recha Abriana Fanny Fatma Wati Fatah, Haerul Muhammad Ja’far Shidiq