Gorontalo Medicinal Plants Image Identification System Using Artificial Neural Network with Back Propagation

Mukhlisulfatih Latief; Rampi Yusuf

doi:10.22146/ijitee.42154

Gorontalo Medicinal Plants Image Identification System Using Artificial Neural Network with Back Propagation

https://doi.org/10.22146/ijitee.42154

Mukhlisulfatih Latief^(1*), Rampi Yusuf⁽²⁾

(1) State University of Gorontalo
(2) State University of Gorontalo
(*) Corresponding Author

Abstract

The purpose of this research is to design the application of digital image processing system to identify the image of medicinal plants of Gorontalo region using artificial neural network method using back propagation. This research used a digital image processing method with segmentation and extraction techniques. Segmentation process was carried out using thresholding method. Furthermore, a process of characteristic extraction from medicinal plants drawings was carried out using feature and color feature extractions to obtain the value of metric, eccentricity, hue, saturation and value. these five values were used as parameters for input neurons and one output neuron which denoted the class of the medicinal plants image. Data of this research consisted of 91 images which had been divided into two types, training data and test data. The training data consisted of 80 images and the test data consisted of eleven images. A network architecture was obtained from the training result and it provided the highest accuracy level (100%) and least number of iteration with a number of 50 neurons on hidden layer and 143 epochs. The testing result showed a lower accuracy of 54.54%.

Keywords

Artificial Neural Network, back propagation, image processing

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References

[1] N.Y. Kandowangko, M. Solang, and J. Ahmad, “Kajian Etnobotani Tanaman Obat oleh Masyarakat Kabupaten Bone Bolango Provinsi Gorontalo,” Univ. Negeri Gorontalo, Research Report, 2011.

[2] G. Tjitrosoepomo, Taksonomi Umum, 3rd print, Yogyakarta, Indonesia: Gadjah Mada University Press, 2005.

[3] M.A. Agmalaro, A. Kustiyo, and A.R. Akbar, “Identifikasi Tanaman Buah Tropika Berdasarkan Tekstur Permukaan Daun Menggunakan Jaringan Syaraf Tiruan,” Jurnal Ilmu Komputer Agri-Informatika., Vol. 2, No.2, pp. 72-83, Nov. 2013.

[4] M. Effendi, Fitriyah, and U. Effendi, “Identifikasi Jenis dan Mutu Teh Menggunakan Pengolahan Citra Digital dengan Metode Jaringan Syaraf Tiruan,” Jurnal Teknotan, Vol. 11, No. 2, Agustus 2017.

[5] F. Indrawan, “Aplikasi Pengenalan Pola Daun Menggunakan Jaringan Syaraf Learning Vector Quantification untuk Penentuan Tanaman Obat,” Proc. SemnasIF, 2010, pp. 16-21.

[6] A. Kadir and A. Susanto, Pengolahan Citra: Teori dan Aplikasi, Yogyakarta, Indonesia: ANDI, 2012.

[7] T. Sutoyo, E. Mulyanto, V. Suhartono, O. Nurhayati, and Wijanarto, Teori Pengolahan Citra Digital, Yogyakarta, Indonesia: ANDI, 2009.

[8] E. Prasetyo, Pengolahan Citra Digital dan Aplikasinya Menggunakan MATLAB, Yogyakarta, Indonesia : ANDI, 2011.

[9] Suyanto, Artificial Intelligence Searching, Reasoning, Planning, Learning, Revisi Kedua, Bandung, Indonesia: Informatika, 2014.

[10] B. Pradhan, S. Lee, “Landslide Risk Analysis Using Artificial Neural Network Model Focussing on Different Training Sites,” International Journal of Physical Sciences, Vol. 4, No. 1, pp. 001-015, Januari, 2009.

[11] J.J. Siang, Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan MATLAB, Yogyakarta, Indonesia: ANDI, 2005.

[12] K. Adi, S. Pujianto, R. Gernowo, A. Pamungkas, and A.B. Putranto, “Identification of Plasmadium Falciparum Phase in Red Blood Cells Using Artificial Neural Networks,” International Journal of Applied Engineering Research., Vol. 9, No. 23, pp. 13917-13924, 2014.

[13] K. Warman, L. Harahap, and A. Munir, “Identifikasi Kematangan Buah Jeruk dengan Teknik Jaringan Syaraf Tiruan,” Jurnal Rekayasa Pangan dan Pertanian, Vol. 3, No. 2, pp. 248-253, 2015.

[14] M.M. Sebatubun and M.A. Nugroho, “Ekstraksi Fitur Circularity untuk Pengenalan Varietas Kopi Arabika,” Jurnal Teknologi Informasi dan Ilmu Komputer, Vol. 4, No.4, pp. 283-289, Des. 2017.

DOI: https://doi.org/10.22146/ijitee.42154