Deteksi Jamur Beracun dan Tidak Beracun Menggunakan CNN dan YOLO

  • Nurul Fajria Politeknik Negeri Padang
  • Lifwarda Lifwarda Politeknik Negeri Padang
  • Ramiati Ramiati Politeknik Negeri Padang
  • Yulindon Yulindon Politeknik Negeri Padang
Keywords: Convolutional Neural Network, Poisonous Mushrooms, Edible Mushrooms, Training, YOLO


In Indonesia, which has a tropical climate there is a lot of wood which is very suitable for mushroom growth. However, some of these mushrooms are poisonous and should be avoided. By examining the morphological characteristics of the mushroom, such as the shape of the mushroom cap, color, smell, and other characteristics it is possible to identify between poisonous and non-poisonous mushrooms. However, some poisonous mushrooms have the same morphological characteristics as non-poisonous mushrooms, making it difficult for us to differentiate them when seen with the naked eye. As a solution to this problem, machine learning is needed to identify which mushrooms are poisonous and which are not. As in this research, Convolutional Neural Network (CNN) and You Only Look Once (YOLO) were used as methods for identification. The stages in this research include collecting datasets, modeling and training data, and the final process, namely deployment. CNN Method got an accuracy value of 55% and succeeded in identifying 5 correct and 5 incorrect images from the 10 images given. Meanwhile, using YOLOv5, we got a high accuracy value of 91% and succeeded in identifying 9 correct and 1 incorrect image out of the 10 images provided. From a comparison of the 2 methods, it was found that detecting poisonous and non-toxic mushrooms was better using the YOLO method than the CNN method


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[1] Y. Suryani, O. Taupiqurrahman, and Y. Kulsum, Buku Mikologi Dr. Yani Suryani_Lengkap, 2020.

[2] N. L. Chusna, M. I. Shalahudin, U. Riyanto, and A. D. Alexander, Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine, Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, Jun. 2022, doi: 10.47065/bits.v4i1.1624.

[3] K. Nurfitri, A. D. Pradana, and I. Widaningrum, Jurnal Rekayasa Teknologi dan Komputasi Penerapan Algoritma Principal Component Analysis (PCA) Dan K-Nearest Neighbors (KNN) Pada Klasifikasi, 2021.

[4] Y. Yohannes, D. Udjulawa, and T. Ivan Sariyo, Klasifikasi Jenis Jamur Menggunakan SVM dengan Fitur HSV dan HOG, PETIR, vol. 15, no. 1, pp. 113–120, Dec. 2021, doi: 10.33322/petir.v15i1.1101.

[5] E. Iedfitra Haksoro and A. Setiawan, Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada Convolutional Neural Network, Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, Online, 2021.

[6] S. Enggari, A. Ramadhanu, and H. Marfalino, Peningkatan Digital Image Processing Dalam Mendeskripsikan Tumbuhan Jamur Dengan Segmentasi Warna, Deteksi Tepi Dan Kontur, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 4, no. 1, pp. 70–75, Jan. 2022, doi: 10.47233/jteksis.v4i1.358.

[7] Li, W., Zhao, X., Li, S., & Niu, D. Real-time recognition of edible and poisonous mushrooms based on deep learning. Computers and Electronics in Agriculture, 174, 105450. 2020. doi: 10.1016/j.compag.2020.105450

[8] Khairunnas, E. Mulyanto Yuniarno, and A. Zaininas, Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot, 2021.

[9] R. Gelar Guntara, Pemanfaatan Google Colab Untuk Aplikasi Pendeteksian Masker Wajah Menggunakan Algoritma Deep Learning YOLOv7, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 1, pp. 55–60, Feb. 2023, doi: 10.47233/jteksis.v5i1.750.

[10] Mengenal Unstructured Data. 2022. [Online] Tersedia: [30 Juli 2023]

[11] A. F. Fandisyah, N. Irawan, and W. S. Winahju, Deteksi Kapal di Laut Indonesia Menggunakan YOLOv3, 2021.

[12] A. K. E. Lapian, S. R. U. A. , Sompie, and P. D. K. Manembu, You Only Look Once (YOLO) Implementation For Signature Pattern Classification, 2021.

[13] Shrivastava, H. (2021). Symptomatic Assistance. International Journal for Research in Applied Science and Engineering Technology,9(VII).

[14] Tan, M., & Le, Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 2019

[15] Bochkovskiy, A., theNM22, & AlexeyAB. YOLOv4: Optimal Speed and Accuracy of Object Detection. 2020
How to Cite
Fajria, N., Lifwarda, L., Ramiati, R., & Yulindon, Y. (2024, June 28). Deteksi Jamur Beracun dan Tidak Beracun Menggunakan CNN dan YOLO. Elektron : Jurnal Ilmiah, 14-21.