Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN

Ummi Sri Rahmadhani; Noveri Lysbetti Marpaung

doi:10.30591/jpit.v8i2.5229

Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN

Ummi Sri Rahmadhani, Noveri Lysbetti Marpaung

Abstract

Mushrooms are plants that do not have true roots and leaves. There are many types of mushrooms that have been identified worldwide, with various shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy, health, and others. One of the benefits of mushrooms is as a food source in Indonesia, but not all types can be consumed. To identify mushroom species, the concepts of Genus and species can be used. The concept of Genus is considered easier because it groups mushroom types based on similar morphological characteristics. Therefore, a model is needed to classify mushrooms based on consumable and toxic genera. The method used in this research is Convolution Neural Network (CNN) due to its good predictive results in image recognition. The model in the research utilizes three convolution layers, three MaxPooling layers, and two dropout layers. The use of dropout aims to reduce overfitting in the model. The research uses a dataset of 1200 images with a training and testing data ratio of 70:30, resulting in 840 training data and 360 testing data. The best accuracy achieved by this model is 89% for training and 82% for validation. Therefore, it can be concluded that the model is able to classify mushrooms based on Genus using the CNN method

Keywords

Jamur; Genus; CNN; Beracun; Konsumsi

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References

Oktavia N. Putri, “Implementasi Metode CNN Dalam Klasifikasi Gambar Jamur Pada Analisis Image Processing (Studi Kasus: Gambar Jamur Dengan Genus Agaricus Dan Amanita),” pp. 1–80, 2020, [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/23677/16611103 Ocktavia Nurima Putri.pdf?sequence=1&isAllowed=y

V. Vanitha, “Classification of Mushrooms to Detect their Edibility Based on Key Attributes,” Biosci. Biotechnol. Res. Commun., vol. 13, no. 11, pp. 37–41, 2020, doi: 10.21786/bbrc/13.11/9.

I. P. Ihsan and F. Yusuf, “Analisis Jamur Beracun Berdasarkan Ciri Menggunakan Algoritma AdaBoost,” Pros. Konf. Ilmu Komput. Nas. 2013, no. ISSN 2338-2899, pp. 1–6, 2013.

I. P. Putra, “Kasus keracunan Inocybe sp. di Indonesia,” Pros. Semin. Nas. Biol., no. September, pp. 148–153, 2020.

H. F. Jessar, A. Toto Wibowo, and E. Rachmawati, “Klasifikasi Genus Tanaman Sukulen Menggunakan Convolutional Neural Network,” Proceeding Eng., vol. 8, no. 2, pp. 3180–3195, 2021.

F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” J. Informatics Comput. Sci., vol. 1, no. 02, pp. 104–108, 2020, doi: 10.26740/jinacs.v1n02.p104-108.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

S. R. Suartika E. P, I Wayan, Wijaya Arya Yudhi, “Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, p. 76, 2016, [Online]. Available: http://repository.its.ac.id/48842/

M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” J. Algoritm., vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.

S. R. Dewi, “Deep Learning Object Detection Pada Video,” Deep Learn. Object Detect. Pada Video Menggunakan Tensorflow Dan Convolutional Neural Netw., pp. 1–60, 2018, [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/7762/14611242_Syarifah Rosita Dewi_Statistika.pdf?sequence=1

A. Kholik, “Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram,” Jdmsi, vol. 2, no. 2, pp. 10–20, 2021.

M. R. R. Allaam and A. T. Wibowo, “KLASIFIKASI GENUS TANAMAN ANGGREK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ( CNN ) Program Studi Sarjana Informatika Fakultas Informatika Universitas Telkom Bandung,” vol. 8, no. 2, pp. 3147–3179, 2021.

I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.

A. Nasuha, T. A. Sardjono, and M. H. Purnomo, “Pengenalan Viseme Dinamis Bahasa Indonesia Menggunakan Convolutional Neural Network,” vol. 7, no. 3, pp. 258–265, 2018.

Lianah, Budidaya Jamur Pangan: Konsumsi Lokal. 2020.

DOI: https://doi.org/10.30591/jpit.v8i2.5229