skip to main content

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

Department of Informatics, Universitas Dian Nuswantoro, Indonesia

Received: 30 Nov 2017; Published: 14 Mar 2018.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract

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).

Fulltext View|Download |  Data Set
Dataset Citra Daun Herbal dan Pengolahanya
Subject Dataset dan pengolahan data citra 10 daun herbal
Type Data Set
  Download (69KB)    Indexing metadata
Keywords: digital leaf image identification; herbal medicinal plants; GLCM analysis; KNN classification

Article Metrics:

  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

Last update: 2021-07-24 13:39:12

  1. Identification using the K -Means Clustering and Gray Level Co-occurance Matrix (GLCM) At Maturity Fruit Oil Head

    Sukemi, Edi Sukrisno. 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019. doi: 10.1109/ICIC47613.2019.8985681
  2. Use of Support Vector Machine to Classify Rhizomes Based on Color

    M Maimunah, E R Arumi. Journal of Physics: Conference Series, 127 , 2019. doi: 10.1088/1742-6596/1381/1/012031
  3. Identification of Medicinal Plant Leaves Using Convolutional Neural Network

    Yuanita A. Putri, Esmeralda C. Djamal, Ridwan Ilyas. Journal of Physics: Conference Series, 127 (1), 2021. doi: 10.1088/1742-6596/1845/1/012026

Last update: 2021-07-24 13:39:13

  1. Identification using the K -Means Clustering and Gray Level Co-occurance Matrix (GLCM) At Maturity Fruit Oil Head

    Sukemi, Edi Sukrisno. 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019. doi: 10.1109/ICIC47613.2019.8985681
  2. Use of Support Vector Machine to Classify Rhizomes Based on Color

    M Maimunah, E R Arumi. Journal of Physics: Conference Series, 127 , 2019. doi: 10.1088/1742-6596/1381/1/012031
  3. Identification of Medicinal Plant Leaves Using Convolutional Neural Network

    Yuanita A. Putri, Esmeralda C. Djamal, Ridwan Ilyas. Journal of Physics: Conference Series, 127 (1), 2021. doi: 10.1088/1742-6596/1845/1/012026