KLASIFIKASI KELAYAKAN KONSUMSI JAMUR MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS, DECISION TREE, SUPPORT VECTOR MACHINE DAN GRADIENT BOOSTING
Article Sidebar
Read Counter : 10
Download : 5
Main Article Content
Abstract
Mushrooms are one of the organisms that are important for human survival. With various types of mushrooms, they can be divided into 2 categories: edible mushrooms and poisonous mushrooms. Therefore, with the help of technology, it is hoped that the classification of edible and poisonous mushrooms can be done. The method used is an experimental classification of 4 classification algorithms: K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Gradient Boosting Classifier (GBC). With a dataset consisting of 9 columns and 54000 samples, satisfactory results were obtained with the GBC algorithm at 88%. It can be concluded that the GBC algorithm is the best.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Menawarkan akses terbukaReferences
[1] M.-I. E, G.-M. MB and A.-L. XA, "Climatic and Socioeconomic Aspects of Mushrooms: The Case of Spain," p. 1, 2019.
[2] N. Igiehon and O. Babalola, "Fungal bio-sorption potential of chromium in Norkrans liquid medium byshake flask technique," J. Basic Microbiol, pp. 59-73, 2019
[3] M. B. Balletini and F. A. Fiorda, "Factors affecting mushroom Pleurotus spp," Saudi Journal of Biological Sciences, vol. 26, no. 4, pp. 633-646, 2019.
[4] J. white, S. A and L. D. Haro, "Mushroom poisoning: A proposed new clinical classification,"Toxicon, vol. 157, p. 53, 2019.
[5] W. R, E. F and S. A, "Mushroom Poisoning," Dtsch Arztebl Int, vol. 117, p. 701, 2020.
[6] D. G and A. G. Karegowda, "Identification of Edible and Non-Edible Mushroom," Atlantis Highlights in Computer Sciences, vol. 4, p. 312, 2021.
[7] S. C, H. M and R. B, "COMPARATIVE STUDY ON EDIBLE AND NON EDIBLE MUSHROOM AGAINST," Scholar : National School of Leadership, vol. 8, 2019.
[8] D. A and Omondiagbe, "Machine Learning Classification Techniques for Breast Cancer," IOP Conf. Series: Materials Science and Engineering 495, pp. 1-3, 2019 .
[9] P. Mishra and A. Biancolillo, "New data preprocessing trends based on ensemble of multiple Preproccesing techniques," Trends in Analytical Chemistry, vol. 132, pp. 1-2, 2020.
[10] K. Maharana and S. Mondal, "A review: Data pre-processing and data augmentation techniques,"Global Transitions Proceedings, vol. 3, no. 1, pp. 91-92, 2022
[11] A. Rácz and D. Bajusz, "Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification," vol. 26, no. 4, 2021.
[12] S. Zhang, "KNN Classification With One-Step Computation," IEEE Transactions on Knowledge and Data Engineering, vol. 35, pp. 2711-2723, 2020.
[13] S. Klidbary and A. Arabameri, "A Novel Density-Based KNN in Pattern Recognition," 13th International Conference on Computer and Knowledge Engineering, pp. 185-190, 2023.
[14] B. F and S. E, "Non-linear Multiclass SVM Classification Optimization using Large Datasets of Geometric Motif Image," International Journal of Advanced Computer Science and Applications, 2021.
[15] T. Thomas and A. Vijayaraghavan, "Applications of Decision Trees," Machine Learning Approaches in Cyber Security Analytics, pp. 157-184, 2019.
[16] M. Denuit and D. Hainaut, "Gradient Boosting with Neural Networks.," Springer Actuarial, p. 167, 2019
[17] M. H. K. A. T. F. N. T. a. A. A. T. Alyas, "method for thyroid disease classification using a machine learning approach," BioMed Research International, 2022.
[18] J. &. L. J. Graffelman, "Improved Approximation and Visualization of the Correlation Matrix," The American Statistician, vol. 77, p. 432, 2022.
[19] M. I. T. S. S. a. Y. A. Fikri, "Perbandingan metode naïve bayes dan support vector machine pada analisis sentimen twitter," STIKI Informatika Jurnal, vol. 2, p. 71, 2020.
[20] M. Patwary and X. Wang, "Sensitivity analysis on initial classifier accuracy in fuzziness based semi-supervised learning," inf Sci, p. 93, 2019.