Comparison of CNN Transfer Learning in Detecting Superior Local Fruit Types in Bali
DOI:
https://doi.org/10.52985/insyst.v6i1.389Keywords:
CNN, Local Bali Fruits, ResNet152, VGG16Abstract
Bali Province is an island that has unique geographical conditions, as well as the diversity of fruit it has. The specialty of local fruit is not only of economic value for food needs but also for religious ceremonial needs. Bali provincial government is currently actively promoting local fruit so that it can be used as consumption for Bali's increasingly rapid tourism. Several superior fruits were developed as an effort to raise the potential of local fruit in the tourism sector. Some of the superior fruits are Balinese snake fruit and sapodilla. However, snake fruit is one of the superior local fruits in Bali which has not experienced degradation over time. This research aims to detect the types of snake fruit in Indonesia. This fruit is not popular compared to imported fruit. Therefore, an application is needed to recognize this type of snake fruit automatically. This research uses a deep learning method with the CNN (Convolutional Neural Network) algorithm. This algorithm is able to recognize and classify an image well. The fruit images used were 400 fruits for 4 types of snake fruit. Where the training data for snake fruit is special because it has different skin and fruit contents. In this research, 2 transfer learning models from the CNN algorithm were also compared, namely mobilenetv2 and ResNet152. Based on the test results, it was found that the best level of accuracy was obtained using the ResNet152 model with an accuracy value of 92% in identifying images of Balinese snake fruit.
References
N. Adiputra, “Fungsi Buah Dan Daun Tanaman Dalam Budaya Bali Sebuah Kajian Terhadap Tanaman Upacara.”
N. Widya Utami, N. Purnama, I. Putu, And R. Prajna, “Klasifikasi Tanaman Upakara Adat Hindu Di Kebun Raya Eka Karya Bali Menggunakan Algoritma Convolutional Neural Network,” 2023.
I. M. Tamba, “Kajian Buah-Buahan Lokal Unggulan Provinsi Bali dan Potensi Dinamisnya,” JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian, vol. 9, no. 2, pp. 126–132, Apr. 2023, doi: 10.37149/jia.v9i2.1117.
F. Fitra Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network”.
N. E. A. Mimma, S. Ahmed, T. Rahman, and R. Khan, “Fruits Classification and Detection Application Using Deep Learning,” Sci Program, vol. 2022, 2022, doi: 10.1155/2022/4194874.
S. Juliansyah et al., “Klasifikasi Citra Buah Pir Menggunakan Convolutional Neural Networks,” Jurnal Infra Petra, vol. 7, no. 1, pp. 489–495, 2021, doi: 10.22441/incomtech.v11i1.10185.
R. Pathak, “CLASSIFICATION OF FRUITS USING CONVOLUTIONAL NEURAL NETWORK AND TRANSFER LEARNING MODELS.” [Online]. Available: https://www.researchgate.net/publication/364254116
Y. Miftahuddin and F. Zaelani, “Perbandingan Metode Efficientnet-B3 dan Mobilenet-V2 Untuk Identifikasi Jenis Buah-buahan Menggunakan Fitur Daun,” 2022.
V. Gadre, S. Sashte, and A. Sarnaik, “WASTE CLASSIFICATION USING RESNET-152,” INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, vol. 07, no. 01, Jan. 2023, doi: 10.55041/ijsrem17421.
T. Saputra, M. Ezar, and A. Rivan, “STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Analisis Performa Resnet-152 Dan Alexnet Dalam Klasifikasi Jenis Kanker Kulit.” [Online]. Available: https://challenge.isic-
M. C. Wujaya and L. W. Santoso, “Klasifikasi Pakaian Berdasarkan Gambar Menggunakan Metode YOLOv3 dan CNN.”
“Fruit Classification using Convolutional Neural Network (CNN),” Precision Agriculture Science and Technology, vol. 3, no. 1, 2021, doi: 10.12972/pastj.20210001.
W. Setiawan, “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus,” Jurnal Simantec, vol. 7, no. 2, pp. 48–53, 2020, doi: 10.21107/simantec.v7i2.6551.
D. Ramayanti, D. Asri, and L. Lionie, “Implementasi Model Arsitektur VGG16 dan MobileNetV2 Untuk Klasifikasi Citra Kupu-Kupu Article Info ABSTRAK,” JSAI: Journal Scientific and Applied Informatics, vol. 5, no. 3, 2022, doi: 10.36085.
I. P. W. Prasetia and I Made Gede Sunarya, “Image Classification of Balinese Seasoning Base Genep Based on Deep Learning,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 13, no. 1, Mar. 2024, doi: 10.23887/janapati.v13i1.67967.
S. A. Maulana et al., “Penerapan Metode CNN (Convolutional Neural Network) Dalam Mengklasifikasi Jenis Ubur-Ubur,” Jurnal Penelitian Rumpun Ilmu Teknik (JUPRIT), vol. 2, no. 4, pp. 122–130, 2023, doi: 10.55606/juprit.v2i4.3084.
A. Prima, “Rancang Bangun Sistem Pendeteksi Aneka Ragam Buah Menggunakan MobileNetv2,” Jurnal Sistim Informasi dan Teknologi, pp. 208–215, Jul. 2023, doi: 10.60083/jsisfotek.v5i2.217.
M. Sanjaya and E. Nurraharjo, “Deteksi Jenis Rempah-Rempah Menggunakan Metode Convolutional Neural Network Secara Real Time,” 2023.
A. Eka et al., “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Klasifikasi Jenis Rempah Menggunakan Convolutional Neural Network dan Transfer Learning,” 2023.
C. Z. Basha, B. N. L. Pravallika, and E. B. Shankar, “An efficient face mask detector with pytorch and deep learning,” EAI Endorsed Trans Pervasive Health Technol, vol. 7, no. 25, pp. 1–8, 2021, doi: 10.4108/eai.8-1-2021.167843.
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