Penerapan Probabilistic Neural Network pada Klasifikasi Patogen Daun Bibit Jabon Berdasarkan Ciri Morfologi Spora
DOI:
https://doi.org/10.47065/bulletincsr.v4i2.325Keywords:
Probabilistic Neural Network; Classification; Pathogen; Jabon’s Leaf Seedling; Morphological FeatureAbstract
The aim of this research is to clasify pathogen of Jabon’s leaf seedling based on spora morphological features using Probabilistic Neural Network classifier. Three types of pathogen to be classified are Colletotrichum sp., Curvularia sp., and Fusarium sp.. The methodologies used are data acquisition using optilab camera microscope to obtain microscopic image data , preprocessing (grayscale, median smoothing, thresholding Otsu, region filling, median smoothing and dilate), morphology feature extraction (area, perimeter, area convex, convex perimeter, compactness, solidity, convexity and roundness), Probabilistic Neural Network classification, and evaluation. The basic morphological characteristics consisting of area, perimeter, convex area, convex perimeter, and derived morphological characteristics consisting of compactness, solidity, convexity and roundness. The experimental results of the morphological feature extraction showed that the compactness and roundness characteristics can be used to identify the three types of pathogens because with these characteristics each class of pathogen is separate. Testing for this research was carried out using 150 test data from three classes of objects from the dataset, namely class 1 (Colletotrichum sp.), class 2 (Curvularia sp.), and class 3 (Fusarium sp.). Then the results of pathogen classification using the application of the PNN algorithm in testing this research obtained an average accuracy value of 86.8% with a proportion of training data and test data of 80:20. The results of the PNN classification on 150 test data were that there were 36 data classified into Colletotrichum sp., 44 data classified into Curvularia sp., and 50 data classified into Fusarium sp. Further research could be done with the identification of digital microscopic images without cropping and systems that could clasify a colony image of pathogens clearly.
Downloads
References
H. Krisnawati, M. Kallio, and M. Kanninen, Anthocephalus cadamba Miq. Ecology, silviculture and productivity. Bogor (ID): Center for International Foresty Research, 2011. [Online]. Available: http://www.cifor.org/publications/pdf_files/Books/BKrisnawati1105.pdf
F. I. Mulyana, D, Asmarahman C, Bertanam Jabon. Jakarta: Agro Media Pustaka, 2011.
Warisno and K. Dahana, Peluang Investasi: Jabon Tanaman Kayu Masa Depan. Jakarta: PT Gramedia Pustaka Utama, 2011.
A. AR, “Identifikasi dan patogenisitas cendawan penyebab primer penyakit mati pucuk pada bibit jabon (Anthocephalus cadamba (Roxb.) Miq),” Institut Pertanian Bogor, 2014.
E. N. Herliyana, Biodiversitas dan Potensi Cendawan di IndonesiaNo Title. Bogor (ID): IPB Pr., 2014.
T. Yudiarti, Ilmu Penyakit Tumbuhan. Yogyakarta: Graha Ilmu, 2007.
E. Nina Herliyana and A. Putra, “Pengaruh Pupuk Organik Cair terhadap Pertumbuhan Bibit Jabon (Anthocephalus cadamba miq.) dan Ketahanannya terhadap Penyakit,” J. Silvikultur Trop., vol. 3, no. 3, pp. 168–173, 2012.
I. Anggraeni, “Colletotrichum sp. penyebab penyakit bercak daun pada beberapa bibit tanaman hutan di persemaian,” Mitra Hutan Tanam., vol. 4, no. 1, pp. 29–35, 2009.
T. Watanabe, Pictorial Atlas of Soil and Seed Fungi Morphologies of Cultured Fungi and Key to Species. London: CRC Pr., 1994.
M. Purba, “Identifikasi fungi pada pembibitan jabon (Anthocephalus cadamba (Roxb.) Miq) di Sampali Medan,” Universitas Sumatera Utara, 2013.
C. M. Alvarez-Ramos, E. Nino, and M. Santos, “Automatic classification of Nosema pathogenic agents through machine vision techniques and kernel-based vector machines,” 2013 8th Comput. Colomb. Conf. 8CCC 2013, 2013, doi: 10.1109/ColombianCC.2013.6637516.
F. Scotti, “CIMSA 2005-IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images,” no. July, pp. 20–22, 2005, [Online]. Available: http://piurilabs.di.unimi.it/Papers/cimsa_2005.pdf
L. Putzu, G. Caocci, and C. Di Ruberto, “Leucocyte classification for leukaemia detection using image processing techniques,” Artif. Intell. Med., vol. 62, no. 3, pp. 179–191, 2014, doi: 10.1016/j.artmed.2014.09.002.
W. Ford, K. Xiang, W. Land, R. Congdon, Y. Li, and O. Sadik, “A multi-class probabilistic neural network for pathogen classification,” Procedia Comput. Sci., vol. 20, pp. 348–353, 2013, doi: 10.1016/j.procs.2013.09.284.
P. Dwi, I. Azizah, P. Statistika, and U. I. Bandung, “Penerapan Probabilistic Neural Network pada Klasifikasi Berat Bayi Baru Lahir,” vol. 1, pp. 152–159.
D. D. B. D. Tahun, R. D. Adyati, Y. N. Nasution, and S. Wahyuningsih, “KLASIFIKASI PROBABILISTIC NEURAL NETWORK ( PNN ) PADA DATA DIAGNOSA PENYAKIT DEMAM BERDARAH,” pp. 15–21, 2019.
S. J. Siregar, A. I. Lubis, and E. F. Ginting, “Penerapan Neural Network Dalam Klasifikasi Citra Permainan Batu Kertas Gunting dengan Probabilistic Neural Network,” vol. 3, no. 3, pp. 420–425, 2021, doi: 10.47065/bits.v3i3.1143.
I. Amalia, I. Mawardi, and M. Arhami, “Klasifikasi Citra Songket Aceh Menggunakan Metode Probabilistic Neural Network,” vol. VIII, no. 3, pp. 6349–6357, 2023.
S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, and Q. L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network,” ISSPIT 2007 - 2007 IEEE Int. Symp. Signal Process. Inf. Technol., pp. 11–16, 2007, doi: 10.1109/ISSPIT.2007.4458016.
M. Saraswat and K. V. Arya, “Automated microscopic image analysis for leukocytes identification: A survey,” Micron, vol. 65, pp. 20–33, 2014, doi: 10.1016/j.micron.2014.04.001.
W. Burger and M. Burge, Principles of Digital Image Processing-Core Algorithm. London: Springer, 2009.
M. Yang, K. Kpalma, and J. Ronsin, “A survey of shape feature extraction techniques,” IN-TECH, pp. 43–90, 2008.
P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. New York: Addison Wesley, 2005.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Probabilistic Neural Network pada Klasifikasi Patogen Daun Bibit Jabon Berdasarkan Ciri Morfologi Spora
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2024 Melly Br Bangun, Yeni Herdiyeni, Elis Nina Herliyana, Rossy Nurhasanah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).