Main Article Content


At this time to overcome difficulties in identifying medicinal plants that have an impact on the frequent errors in the use of medicinal plants. The formulation of the problem to be discussed in this study is how to identify medicinal plants based on feature extraction of color, texture, and leaf shape. Steps to resolve this problem by collecting image data of medicinal plants, then the image data extracted leaf color features using Red Green Blue (RGB) and Hue Saturation Value (HSV), based on leaf texture using the Gray Level Co-occurrence Matrix (GLCM), based on the shape leaves use eccentricity and metrics. and then classified by the K-Nearest Neighbor (KNN) method. The results in this study the accuracy of Chinese Petai leaves is superior to other types of leaves, which is 98%, which occurs at each K value. Other types of leaves have various values. Saga leaves range between 94% - 97%, Green Betel leaves between 92.8% - 97%, and Red Betel leaves between 91.7% - 95%, Optimal K values ​​indicated by K = 3 have an average accuracy rate of 96.7% also have sensitivity value of 93.3%. The addition of K = 5, K = 7, K = 9, and K = 11 tends to decrease the average value of accuracy and sensitivity.

Article Details


  2. Aditama., T. Y. (2015) Jamu & Kesehatan. Available at: Manuscript-372-1-10-20150521.pdf.
  3. Alviansyah, F., Ruslianto, I. and Diponegoro, M. (2017) ‘Identifikasi Penyakit Pada Tanaman Tomat Berdasarkan Warna Dan Bentuk Daun Dengan Metode Naive Bayes Classifier Berbasis Web’, Jurnal Coding Sistem Komputer Untan, 05(1), pp. 23–32.
  4. Eliyen, K., Tolle, H. and Muslim, M. A. (2017) ‘K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENILAIAN PADA VIRTUAL PATIENT CASE Kunti’, Scholarpedia, 4(2), p. 1883. doi: 10.4249/scholarpedia.1883.
  5. Gustina, S., Fadlil, A. and Umar, R. (2016) ‘Identifikasi Tanaman Kamboja menggunakan Ekstraksi Ciri Citra Daun dan Jaringan Syaraf Tiruan’, 2(1), pp. 128–132.
  6. Han, J., Kamber, M. and Pei, J. (2012) Data Mining. Concepts and Techniques, 3rd Edition.
  7. Hwang, W. and Wen, K.-W. (1998) ‘Fast kNN Clasification Algoritma Based On partial Distance Search’, Electronics Letters, pp. 3–4.
  8. Karomi, M. A. A. (2015) ‘Optimasi Parameter K Pada Algoritma KNN Untuk Klasifikasi Heregistrasi Mahasiswa Program Studi Teknik Informatika STMIK Widya Pratama’, Information Processing and Management, p. IC-TECH X (285): 5.
  9. Kasim, A. A. and Harjoko, A. (2014) ‘Klasifikasi Citra Batik Menggunakan Jaringan Syaraf Tiruan Berdasarkan Gray Level Co- Occurrence Matrices ( GLCM )’, Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Yogyakarta, 21 Juni 2014, pp. 7–13. Available at:
  10. Li, X., Jiang, H. and Yin, G. (2014) ‘Detection of Surface Crack Defect on Ferrite Magnetic’.
  11. McAndrew, A. (2004) An Introduction to Digital Image Processing with Matlab. Australia: Thomson.
  12. Nixon, M. S. and Aguado, A. S. (2008) ‘Feature Extraction & Image Processing (Second)’, Elsevier B.V.
  13. Pardosi, I. (2016) ‘Salt and Pepper Noise Removal dengan Spatial Median Filter dan Adaptive Noise Reduction’, 17(2), pp. 127–136.
  14. Sikki, M. H. (2009) ‘Pengenalan Wajah Menggunakan KNearest Neighbor dengan Proses Transformasi Wavelet’.
  15. Sokolova, M. and Lapalme, G. (2009) ‘A systematic analysis of performance measures for classification tasks’, Information Processing and Management. Elsevier Ltd, 45(4), pp. 427–437. doi: 10.1016/j.ipm.2009.03.002.
  16. Tapsell LC1, Hemphill I, Cobiac L, Patch CS, Sullivan DR, Fenech M, Roodenrys S, Keogh JB, Clifton PM, Williams PG, Fazio VA, I. K. (2014) ‘Health benefits of herbs and spices: the past, the present, the future’, Journal Of the australian medical Association, 185(21 Agustus 2006).
  17. Zhao, Y., Qian, Y. and Li, C. (2018) ‘Improved KNN text classification algorithm with MapReduce implementation’, 2017 4th International Conference on Systems and Informatics, ICSAI 2017, 2018–Janua(Icsai), pp. 1417–1422. doi: 10.1109/ICSAI.2017.8248509.