Performance of the Decision Tree Algorithm in the Classification of Edible and Poisonous Mushrooms with Information Gain Optimization
(1) Department of Informatics Engineering, Universitas Dian Nuswantoro, Indonesia
(2) Department of Informatics Engineering, Universitas Dian Nuswantoro, Indonesia
(3) Department of Informatics Engineering, Universitas Dian Nuswantoro, Indonesia
(4) Department of Informatics Engineering, Universitas Wahid Hasyim, Indonesia
Abstract
Purpose: This study proposes a new mushroom classification model using a decision tree algorithm to classify edible and poisonous mushrooms by applying machine learning whose algorithm has better performance in terms of accuracy.
Methods: The information gain technique was applied at the data feature selection stage to increase the accuracy of the suggested decision tree model. This study used the same mushroom dataset as that employed in previous studies.
Result: The proposed decision tree model in this study can classify edible and poisonous mushrooms with a good accuracy of 99.61%, outperforming a previous study whose final accuracy was 97.05%.
Novelty: The novelty of this s is the use of information gain as a filter technique at the feature selection stage. This study aims to optimize the previous mushroom classification models with improved accuracy.
Keywords
Full Text:
PDFReferences
I. N. P. A. Indah Juwita Sari, “Screening and Identification of Mushrooms Growth Promoting Bacteria on Straw Mushrooms (Volvariella volvacea),” J. Trop. Biodivers. Biotechnol., vol. 6, no. 1, 2021, doi: 10.22146/jtbb.60618.
H. Rathore, S. Prasad, M. Kapri, A. Tiwari, and S. Sharma, “Medicinal importance of mushroom mycelium: Mechanisms and applications,” J. Funct. Foods, vol. 56, pp. 182–193, May 2019, doi: 10.1016/j.jff.2019.03.016.
S. C. Khoo et al., “Development of formaldehyde-free bio-board produced from mushroom mycelium and substrate waste,” J. Hazard. Mater., vol. 400, p. 123296, Dec. 2020, doi: 10.1016/j.jhazmat.2020.123296.
R. W. deVere White, R. M. Hackman, S. E. Soares, L. A. Beckett, and B. Sun, “Effects of a mushroom mycelium extract on the treatment of prostate cancer,” Urology, vol. 60, no. 4, pp. 640–644, Oct. 2002, doi: 10.1016/S0090-4295(02)01856-3.
H. A. B. W. Nienke Braat, Margot C. Koster, “Beneficial interactions between bacteria and edible mushrooms,” Fungal Biol. Rev., vol. 39, pp. 60–72, 2022, doi: https://doi.org/10.1016/j.fbr.2021.12.001.
H. L. Jiang, C. A. Mbajiorgu, J. W. Ngambi, and C. P. Tlabela, “Economic Importance of Lentinulas edodes as an Alternative to Antibiotics Growth Promoters to Small Scale Rural Pig Farmers of Limpopo Province, South Africa: A Review,” vol. 4, no. 2, pp. 486–493, 2016.
N. A. R. G. Alistair J.P. Brown, Gordon D. Brown, Mihai G. Netea, “Metabolism impacts upon candida immunogenicity and pathogenicity at multiple levels,” Trends Microbiol., vol. 22, no. 11, pp. 614–622, 2014, doi: 10.1016/j.tim.2014.07.001.
D. Tjokrokusumo, “Diversitas jamur pangan terhadap kandungan beta-glukan dan manfaatnya terhadap kesehatan,” vol. 1, no. September, pp. 1520–1523, 2015, doi: 10.13057/psnmbi/m010646.
J. Preechasuk, O. Chaowalit, F. Pensiri, and P. Visutsak, “Image Analysis of Mushroom Types Classification by Convolution Neural Networks,” in Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, Dec. 2019, pp. 82–88. doi: 10.1145/3375959.3375982.
N. S. Iwan Saskiawan, Nur Hasanah, “Cultivation of Pleurotus ostreatus using sorghum-supplemented spawn on various substrates,” vol. 23, no. 4, pp. 179–182, 2016.
K. M. Phillips, D. M. Ruggio, and D. B. Haytowitz, “Folate composition of 10 types of mushrooms determined by liquid chromatography–mass spectrometry,” Food Chem., vol. 129, no. 2, pp. 630–636, Nov. 2011, doi: 10.1016/j.foodchem.2011.04.087.
M. Nagulwar, DR More, and L. Mandhare, “Nutritional properties and value addition of mushroom: A review,” Pharma Innov., vol. 9, no. 10, pp. 395–398, 2020, doi: 10.22271/tpi.2020.v9.i10f.5266.
O. Tarawneh, M. Tarawneh, and Y. Sharrab, “Mushroom classification using machine-learning techniques,” researchgate, vol. 9, no. July, pp. 395–398, 2022.
I. A. Essa and R. Dhanalakshmi, “Machine Learning-based Classification of Edible and Poisonous Mushrooms : A Performance,” Ijraset, vol. 11, no. May, pp. 1364–1370, 2023.
K. Tank, “A Comparative Study on Mushroom Classification using Supervised Machine Learning Algorithms,” Int. J. Trend Sci. Res. Dev., vol. 5, no. 5, pp. 716–723, 2021.
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2016.
E. N. O. C.E. Onwukwe, “Fisher’s Linear Discriminant Classifier And Rank Transformation Approach To Discriminant Analysis,” J. Nat. Sci. Res., vol. 4, no. 8, pp. 16–30, 2014.
A. Nurdina and A. B. I. Puspita, “Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, pp. 83–92, 2023, doi: 10.52465/joiser.v1i2.167.
P. Bielza, C., Robles, V., dan Larrañaga, “Regularized Logistic Regression Without a Penalty Term: An Application to Cancer Classification With Microarray Data,” Expert Syst. Appl., vol. 38, no. 5, p. 5110—5118, 2011.
K. L. Kohsasih and B. H. Hayadi, “Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network,” Sci. J. Informatics, vol. 9, no. 2, pp. 197–204, 2022, doi: 10.15294/sji.v9i2.36583.
N. D. Gökhan Aksu, “Comparison of decision trees used in data mining,” Pegem J. Educ. Instr., vol. 9, no. 4, pp. 1183–1208, 2019, doi: https://doi.org/10.14527/pegegog.2019.039.
N. T. & A. J. Barbara Fallon, Jennifer Ma, Kate Allan, Melanie Pillhofer, “Opportunities for prevention and intervention with young children: lessons from the Canadian incidence study of reported child abuse and neglect,” Child Adolesc. Psychiatry Ment. Health, vol. 7, no. 4, 2013.
W. S. Sumitra Nuanmeesri, “Development of the Edible and Poisonous Mushrooms Classification Model by using the Feature Selection and the Decision Tree Techniques,” Int. J. Eng. Adv. Technol., vol. 9, no. 2, 2019.
A. Nazir, A. Akhyar, Y. Yusra, and E. Budianita, “Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization,” Sci. J. Informatics, vol. 9, no. 1, pp. 32–41, 2022, doi: 10.15294/sji.v9i1.33158.
D. K. R. Sudrajat, I. Irianingsih, “Analysis of Data Mining Classification by Comparison of C4.5 and ID algorithms,” IOP Conf. Ser. Mater. Sci. Eng., 2017.
Y. F. Sihombing, A. Septiarini, A. H. Kridalaksana, and N. Puspitasari, “Chili Classification Using Shape and Color Features Based on Image Processing,” Sci. J. Informatics, vol. 9, no. 1, pp. 42–50, 2022, doi: 10.15294/sji.v9i1.33658.
O. D. Rifan Halomoan Tua Sinaga, “Binary Logistic Regression Analysis Using Stepwise Method on Tuberculosis Events,” J. Math. Educ. Appl., vol. 2, no. 1, pp. 01–10, 2023.
H. Citra Mirna Wati, Abd. Charis Fauzan, “Performance Comparison of Mushroom Type Classification Based on Multi-Scenario Dataset Using Decision Tree C4.5 and C5.0,” J. Ris. Inform., vol. 4, no. 3, 2022.
J. Heland et al., “Analysis of Performance of Classification Algorithms in Mushroom Poisonous Detection using Confusion Matrix Analysis,” vol. 9, no. 1, 2020.
G. R. P. B. Lavanya, “Performance Analysis of Decision Tree Algorithms on Mushroom Dataset,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 5, no. 11, 2017, doi: 10.22214/ijraset.2017.11029.
R. Hamonangan, M. B. Saputro, C. Bagus, S. Dinata, and K. Atmaja, “Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors,” J. Soft Comput. Explor., vol. 2, no. 1, 2021, doi: 10.52465/joscex.v2i1.26.
S. A. A. and W. S. Bhaya, “Review of Data Preprocessing Techniques in Data Mining,” J. Eng. Appl. Sci, vol. 12, no. 16, pp. 4102–4107, 2017.
S. Tangirala, “Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, 2020.
M. husni Omar Tarawneh, Monther Tarawneh, Yousef Sharrab, “Mushroom Classification using Machine-Learning Techniques,” Proc. 4th Int. Comput. Sci. Informatics Conf., 2022, doi: doi.org/10.1063/5.0174721.
Y. Fakir, M. Azalmad, and R. Elaychi, “Study of The ID3 and C4.5 Learning Algorithms,” J. Med. Informatics Decis. Mak., vol. 1, no. 2, pp. 29–43, Apr. 2020, doi: 10.14302/issn.2641-5526.jmid-20-3302.
Ismail Yusuf Panessai, M. M. Lakulu, M. H. Abdul Rahman, N. A. Z. Mohd Noor, N. S. Mat Salleh, and Aldrin Aran Bilong, “PSAP: Improving Accuracy of Students’ Final Grade Prediction using ID3 and C4.5,” Int. J. Artif. Intell., vol. 6, no. 2, pp. 125–133, 2019, doi: 10.36079/lamintang.ijai-0602.42.
I. Ç. Taşkin Kavzoğlu, “Karar Ağaçları İle Uydu Görüntülerinin Sınıflandırılması,” Har. Teknol. Elektron. Derg, vol. 2, no. 1, pp. 36–45, 2010.
M. B. Gülen Yildiz Turp, “Tıbbi ve Yenilebilir Mantarlar & Et Ürünlerinde Kullanımı,” YYU J AGR SCI, vol. 28, no. 1, pp. 144–153, 2018.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.