PEMANFAATAN TEKNOLOGI TEPAT GUNA IDENTIFIKASI TUMBUHAN OBAT BERBASIS CITRA

• Main Authors: Yeni Herdiyeni, Julio Adisantoso, Ellyn K Damayanti, Ervizal AM Zuhud, Elvira Nurfadhila, Kristina Paskianti
• Format: Article
• Language: English
• Published: Bogor Agricultural University 2013-08-01
• Series: Jurnal Ilmu Pertanian Indonesia
• Subjects: leaf identification; leaf morphology; leaf shape; medicinal plants; probabilistic neural network; product decision rule
• Online Access: http://journal.ipb.ac.id/index.php/JIPI/article/view/8376

Indonesia is a mega biodiversity country including many kind medicinal plants. It is not easy to identify the various kinds of the medicinal plants especially for common people. Therefore, we need a computer-based automatic system as a tool to identify these various types of the medicinal plants. Developing of computer-based automatic system for medicinal plant identification has been done based on leaf image. There are 30 species of medicinal plants used in this study. There are 3 features for identification, i.e. morphology, texture, and shape. To improve the accuracy of identification we applied probabilistic neural network to classify the species of medicinal plant. The experiment results showed that the accuracy of identification increase to 74.67%. Developing of search engine has been done as well. We used 32 species of medicinal plant. The number of document was 132 documents. The document consists of name, family, description, diseases, and chemical substances. To improve the accuracy of searching, we applied KNN Fuzzy to classify document into 2 categories, i.e., family and diseases. The experiment results showed that the accuracy of average of precision is 96% for only word of length query and 89% for two words of length query. The system is very beneficial for people in society because it can be used to identify medicinal plants easily and the relevant communitis become independent in maintaining family health and giving opportunities as well as income of the people. Hence, the system is promising for leaf identification and supporting plant biodiversity in Indonesia.