Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor

Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms

Fittria Shofrotun Ni'mah -  Department of Informatics, Universitas Dian Nuswantoro, Indonesia
T Sutojo -  Department of Informatics, Universitas Dian Nuswantoro, Indonesia
De Rosal Ignatius Moses Setiadi* -  Department of Informatics, Universitas Dian Nuswantoro, Indonesia
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer

Medicinal plants can be used as an alternative natural treatment, instead of chemical drugs. But because of too many types of plants and lack of knowledge, it will be difficult to identify these herbs. Computer assistance can be used to facilitate the identification of these herbs. This research proposes the identification of herbal plants based on leaf image using texture analysis. There are 10 types of herbal medicinal plants used in this study. The texture analysis used was GLCM by extracting contrast, correlation, energy, and homogeneity. Classification is done by KNN. The result of the experiment showed that the accuracy of identification using 9-fold cross-cross validation method was 83.33% using 9 subsets.

Note: This article has supplementary file(s).

Keywords
digital leaf image identification; herbal medicinal plants; GLCM analysis; KNN classification

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Dataset Citra Daun Herbal dan Pengolahanya
Subject Dataset dan pengolahan data citra 10 daun herbal
Type Data Set
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Article Info
Submitted: 2017-11-30
Published: 2018-03-14
Section: Articles
Language: ID
Statistics: 229 161
  1. S. Saputra, B. Hidayat, dan G. Budiman, “Aplikasi Identifikasi Daun Obat Herbal Menggunakan Transformasi Wavelet Dan Jaringan Saraf Tiruan-Back Propagation Berbasis Web Server,” Skripsi, Institut Teknologi Telkom, Bandung, 2012.
  2. S. Ifandi, Jumari, dan S. Widodo AS, “Keanekaragaman Jenis Tumbuhan Obat Masyarakat Suku Kaili di Dusun Tompu Kecamatan Sigi Biromaru Kabupaten Sigi Sulawesi Tengah,” dalam Seminar Nasional Biologi II, Semarang, 2015.
  3. Y. Garis K, I. Santoso, dan R. Isnanto, “Klasifikasi Citra dengan Matriks Ko-Okurensi Aras Keabuan (Gray Level Co-Occurrence Matrix-GLCM) pada Limakelas Biji-Bijian,” Skripsi, Universitas Diponegoro, Semarang, 2011.
  4. I. Permatasari dan T. Sutojo, “Pengenalan Ciri Garis Telapak Tangan Menggunakan Ekstraksi Fitur (GLCM) dan Metode (KNN),” Skripsi, Universitas Dian Nuswantoro, Semarang, 2016.
  5. T. Sutojo, P. S. Tirajani, D. R. I. M. Setiadi, C. A. Sari, dan E. H. Rachmawanto, “CBIR for Classification of Cow Types using GLCM and Color Features Extraction,” dalam International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE 2017), Yogyakarta, 2017.
  6. O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto dan D. R. I. M. Setiadi, “Tomatoes Classification Using K-NN Based on GLCM and HSV Color Space,” dalam International Conference on Innovative and Creative Information Technology (ICITECH 2017), Salatiga, 2017.
  7. I. Amalia, “Pengenalan Citra Tanda Tangan Menggunakan Gray Level Co-Occurrence Matrix (GLCM) Dan Probabilistic Neural Network (PNN),” Jurnal Teknologi, vol. 14, no. 1, pp. 29-34, 2014.
  8. Azwar, “Integrasi Ekstrasi Fitur Local Binary Pattern Gray-Level Co-Occurrence Metrix Untuk Pengenalan Ekspresi Mulut Pembelajar,” ILKOM Jurnal Ilmiah, vol. 9, no. 1, pp. 17-24, 2017.
  9. A. Jundullah dan M. S. A. Mubarok, “Analisis dan Implementasi Deteksi Citra Spam Menggunakan Gray Level Co-occurences Matrix dan Naive Bayes,” dalam Indonesian Symposium on Computing, 2016, pp. 319-334.
  10. E. S. Y. Pandie, “Implementasi Algoritma Data Mining K-Nearest Neighbor (KNN) Dalam Pengambilan Keputusan Pengajuan Kredit,” Skripsi, Universitas Nusa Cendana, Kupang, 2012.