Study 0n Identification Of Poisonous and Non-Toxic Mushrooms Using the Cart-Logitboost Algorithm

moch anjas  aprihartha; Zulhandi  Putrawan; Dicky  Zulhan; Fatma Ahardika  Nurfaizal

doi:10.20956/j.v21i1.35072

Authors

moch anjas aprihartha universitas dian nuswantoro
Zulhandi Putrawan Program Studi PJJ Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Dicky Zulhan Program Studi PJJ Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Fatma Ahardika Nurfaizal Program Studi PJJ Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

DOI:

https://doi.org/10.20956/j.v21i1.35072

Keywords:

CART, Mushroom, LogitBoost, Poisonous, Non-Toxic

Abstract

Mushrooms are one of the groups of living organisms in the fungal regnum which have umbrella-like body characteristics. The body consists of an upright part that functions as a rod to support the hood as well as a hood that is horizontal and rounded with different color variations. There are types of mushrooms that can be a food source for humans. Some types of mushrooms can be eaten or processed like other foods. Apart from that, some types of mushrooms are dangerous if consumed by humans because they are poisonous. Based on these problems, this study offers a new contribution in identifying types of poisonous and non-toxic mushrooms based on mushroom characteristics using the CART algorithm combined with the LogitBoost boosting algorithm. The aim of this research can be used as material for further studies in making tools that can effectively and accurately differentiate between poisonous and non-toxic types of mushrooms. This can help reduce cases of poisoning due to consumption of poisonous mushrooms. The data used is secondary data from public sources UCI Machine Learning Repository. Evaluation of model performance resulted in an accuracy of 98.79%; recall 98.70%; specificity 98.85%; precision 98.56%; F1-Score 98.63%, and AUC 0.9876. These results show that the model is very effective in detecting poisonous mushrooms and has minimal errors in classification.

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References

Achmad, I., Mugiono, S. P., Tias Arlianti, S. P., & Chotimatul Azmi, S. P., 2011. Panduan Lengkap Jamur. Penebar Swadaya Grup.

Ahalya, R. K., Snekhalatha, U., & Dhanraj, V., 2023. Automated Segmentation and Classification of Hand Thermal Images in Rheumatoid Arthritis Using Machine Learning Algorithms: A Comparison with Quantum Machine Learning Technique. Journal of Thermal Biology, Vol. 111, 103404. https://doi.org/10.1016/j.jtherbio.2022.103404

Aprihartha, M. A., Astutik, F., & Sulistianingsih, N., 2024. Comparison of Naïve Bayes, CART, dan CART Adaboost Methods in Predicting Tire Product Sales. Jurnal Matematika, Statistika dan Komputasi, Vol. 20, No. 3, 596-605. https://doi.org/10.20956/j.v20i3.33187

Aprihartha, M. A. Implementasi CART-Real Adaboost dalam Memprediksi Minat Pelanggan Membeli Sepatu. Jurnal EurekaMatika, 12(1), 35-46. https://doi.org/10.17509/jem.v12i1.67808

Aprihartha, M. A., Alam, T. N., & Husniyadi, M. (2024). Perbandingan Metrik Euclidean dan Metrik Manhattan untuk K-Nearest Neighbors dalam Klasifikasi Kismis. Jurnal Ilmu Komputer dan Informatika, 4(1), 21-30. https://doi.org/10.54082/jiki.126

Batubara, G. M. C., Desiani, A., & Amran, A., 2023. Klasifikasi Jamur Beracun Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbors. Jurnal Ilmu Komputer dan Informatika, Vol. 3, No. 1, 33-42. https://doi.org/10.54082/jiki.68

Cao, M. T., Nguyen, N. M., Chang, K. T., Tran, X. L., & Hoang, N. D., 2021. Automatic Recognition of Concrete Spall Using Image Processing and Metaheuristic Optimized LogitBoost Classification Tree. Advances in Engineering Software, Vol. 159, 103031. https://doi.org/10.1016/j.advengsoft.2021.103031

Chacón, A. M. P., Ramírez, I. S., & Márquez, F. P. G., 2023. K-nearest neighbour and K-fold cross-validation used in wind turbines for false alarm detection. Sustainable Futures, Vol. 6, 100132. https://doi.org/10.1016/j.advengsoft.2021.103031

Chu, J., Lee, T. H., & Ullah, H., 2018. Component-wise AdaBoost Algorithms for High-dimensional Binary Classification and Class Probability Prediction. Department of Economics, University of California. https://doi.org/10.1016/bs.host.2018.10.003

Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random forests. Ensemble machine learning: Methods and applications, 157-175. https://doi.org/10.1007/978-1-4419-9326-7_5

Fauzi, A., Supriyadi, R., & Maulidah, N., 2020. Deteksi Penyakit Kanker Payudara dengan Seleksi Fitur berbasis Principal Component Analysis dan Random Forest. Jurnal Infortech, Vol. 2, No. 1, 96-101. https://doi.org/10.31294/infortech.v2i1.8079

Friedman, J., Hastie, T., & Tibshirani, R., 2000. Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics, Vol. 28, No. 2, 337-374. DOI: 10.1214/aos/1016218223

Gorunescu, F., 2011. Data Mining: Concepts, Models and Techniques. Springer Science & Business Media, Berlin.

Hasanuddin, H., 2018. Jenis Jamur Kayu Makroskopis Sebagai Media Pembelajaran Biologi (Studi di TNGL Blangjerango Kabupaten Gayo Lues). BIOTIK: Jurnal Ilmiah Biologi Teknologi dan Kependidikan, Vol. 2, No. 1, 38-52. http://dx.doi.org/10.22373/biotik.v2i1.234

Hu, G., Yin, C., Wan, M., Zhang, Y., & Fang, Y, 2020. Recognition of diseased pinus trees in UAV images using deep learning and AdaBoost classifier. Biosystems Engineering, 194, 138-151.

Kuswanto, H., & Mubarok, R., 2019. Classification of Cancer Drug Compounds for Radiation Protection Optimization Using CART. Procedia Computer Science, 161, 458-465. https://doi.org/10.1016/j.procs.2019.11.145

Prasetya, J., Fallo, S. I., & Aprihartha, M. A., 2024. Stacking Machine Learning Model for Predict Hotel Booking Cancellations. Jurnal Matematika, Statistika dan Komputasi, 20(3), 525-537. https://doi.org/10.20956/j.v20i3.32619

Sahu, R., Pandey, S., Verma, R., & Pandey, P., 2024. Ensemble Learning based Classification of Edible and Poisonous Agaricus Mushrooms. In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-7). IEEE. DOI: 10.1109/ICAECT60202.2024.10469539

Sarkar, S. K., Rudra, R. R., Talukdar, S., Das, P. C., Nur, M. S., Alam, E., ... & Islam, A. R. M. T., 2024. Future Groundwater Potential Mapping Using Machine Learning Algorithms and Climate Change Scenarios in Bangladesh. Scientific Reports, Vol. 14, No. 1, 10328. https://doi.org/10.1038/s41598-024-60560-2

Tutuncu, K., Cinar, I., Kursun, R., & Koklu, M., 2022. Edible and poisonous mushrooms classification by machine learning algorithms. In 2022 11th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE. DOI: 10.1109/MECO55406.2022.9797212

Wagner, Dennis, Heider, D., and Hattab, Georges, 2023. Secondary Mushroom. UCI Machine Learning Repository. https://doi.org/10.24432/C5FP5Q.

Wahdini, M. G., 2020. Implementasi Metode Bayesian Network untuk Sistem Rekomendasi Klasifikasi Jamur Beracun dan Tidak Beracun. Disertasi Doktoral, Universitas Hasanuddin.

Zandi, F., Mansouri, P., & Goodarzi, M, 2023. Global Protein-Protein Interaction Networks in Yeast Saccharomyces Cerevisiae and Helicobacter Pylori. Talanta, 265, 124836. https://doi.org/10.1016/j.talanta.2023.124836