Analisis Arsitektur Convolutional Neural Network Untuk Klasifikasi Citra Bunga
Abstract
Characteristic recognition of biological sciences is increasingly popular in scientific research, especially by utilizing computation.Indonesia is one of the countries with the largest biodiversity in the world, currently, Indonesia has identified and named 19,232 species of flowering plants.Flower image classification has attracted the interest of many researchers to investigate new methods.One of the widely used methods is deep learning and neural network methods.Convolutional Neural Network (CNN) is one of the most commonly used deep learning methods in image processing. This study aims to analyze the performance of flower image classification using VGG16 and NasNetMobile architecture with fine tune and without fine tune.The NasNetMobile architecture model with fine tune achieved the best accuracy of 99.15%, while the NasNetMobile architecture model without fine tune achieved the lowest accuracy of 97.45%.
Downloads
References
B. Falahkhi, E. F. Achmal, M. Rizaldi, R. Rizki, and N. Yudistira, “Perbandingan Model AlexNet dan ResNet dalam Klasifikasi Citra Bunga Memanfaatkan Transfer Learning Comparison of AlexNet and ResNet Models in Flower Image Classification Utilizing Transfer Learning,” 2022, [Online]. Available: http://journal.ipb.ac.id/index.php/jika
D. J. Middleton et al., “Progress on Southeast Asia’s Flora projects,” Gardens’ Bulletin Singapore, vol. 71, no. 2, pp. 267–319, Dec. 2019, doi: 10.26492/gbs71(2).2019-02.
M. Hafizh, T. Novita, D. Guswandi, H. Syahputra, and L. Mayola, “Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 3, pp. 242–249, Jul. 2023, doi: 10.47233/jteksis.v5i3.847.
H. Kurnia, V. Ariandi, and A. I. Jamhur, “Decicion Suport System Pemilihan Pestisida Terbaik Pada UD. Pupuk Jasa Tani Menggunakan Metode Weighted Product,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 1, pp. 1–6, Jan. 2023, doi: 10.47233/jteksis.v5i1.659.
A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” 2021.
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.
Fitriani, “FITRIANI-KLASIFIKASI JENIS BUNGA DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) KLASIFIKASI JENIS BUNGA DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) (CLASSIFICATION OF INTEREST TYPES USING CONVOLUTIONAL NEURAL NETWORK (CNN)),” 2021.
N. FatihahSahidan, A. K. Juha, N. Mohammad, and Z. Ibrahim, “Flower and leaf recognition for plant identification using convolutional neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 2, pp. 737–743, 2019, doi: 10.11591/ijeecs.v16.i2.pp737-743.
G. Wega Intyanto, “Klasifikasi Citra Bunga dengan Menggunakan Deep Learning: CNN (Convolution Neural Network),” 2021.
F. Farhan, C. A. Sari, E. H. Rachmawanto, and N. R. D. Cahyo, “Mangrove Tree Species Classification Based on Leaf, Stem, and Seed Characteristics Using Convolutional Neural Networks with K-Folds Cross Validation Optimalization,” Advance Sustainable Science Engineering and Technology, vol. 5, no. 3, p. 02303011, Oct. 2023, doi: 10.26877/asset.v5i3.17188.
M. Baihaqy, A. Toto WIbowo, and D. Qori Utama, “Klasifikasi Tanaman Anggrek jenis Phalaenopsis berdasarkan Citra Labellum Bunga Menggunakan Metode Convolutinal Neural Network (CNN),” 2022.
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.
M. I. Syahputra and A. T. Wibowo, “Klasifikasi Genus Tanaman Anggrek berdasarkan Citra Kuntum Bunga Menggunakan Metode Convolutional Neural Network (CNN),” 2020. [Online]. Available: https://www.programmersought.com/article/3724355693/
B. Setiyono et al., “IDENTIFIKASI TANAMAN OBAT INDONESIA MELALUI CITRA DAUN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)”, doi: 10.25126/jtiik.2023106809.
K. Rega Prilianti, H. Setiawan, and P. Mimboro, “METODE DETEKSI POKOK POHON SECARA AUTOMATIS PADA CITRA PERKEBUNAN SAWIT MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK (CNN) PADA PERANGKAT LUNAK SISTEM INFORMASI GEOGRAFIS,” vol. 9, no. 7, pp. 1689–1698, 2022, doi: 10.25126/jtiik.202296772.
A. Toto Wibowo, “Klasifikasi Bunga Anggrek untuk Genus Grammatophyllum Menggunakan Metode Convolutional Neural Network (CNN),” vol. 8, no. 5, p. 10527, 2021.
H. Noprisson, “Fine-Tuning Model Transfer Learning VGG16 Untuk Klasifikasi Citra Penyakit Tanaman Padi,” JSAI: Journal Scientific and Applied Informatics, vol. 5, no. 3, 2022, doi: 10.36085.
U. Sri Rahmadhani and N. Lysbetti Marpaung, “Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN,” vol. 8, no. 2, 2023.
U. Kulsum and A. Cherid, “Penerapan Convolutional Neural Network Pada Klasifikasi Tanaman Menggunakan ResNet50,” SIMKOM, vol. 8, no. 2, pp. 221–228, Jul. 2023, doi: 10.51717/simkom.v8i2.191.
S. U. Masruroh, F. Munthaha, A. T. Muharram, and M. F. Syahid, “ANALISA KINERJA MODEL FINE-TUNING DEEP LEARNING UNTUK KLASIFIKASI TULISAN TANGAN BAHASA ARAB,” 2022.
H. Ahmed, M. A. Hossain, I. Hossain, S. S. Akhi, and I. J. Lima, “Detection and classification of plant diseases in leaves through machine learning,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 28, no. 3, pp. 1676–1683, Dec. 2022, doi: 10.11591/ijeecs.v28.i3.pp1676-1683.
A. Fadilah, A. T. Wibowo, and E. Rachmawati, “Klasifikasi Spesies Anggrek Genus Phalaenopsis Berdasarkan Citra Sepal-Petal Menggunakan Metode Convolutional Neural Network (CNN),” 2022.
A. Rajbongshi, T. Khan, M. M. Rahman, A. Pramanik, S. M. T. Siddiquee, and N. R. Chakraborty, “Recognition of mango leaf disease using convolutional neural network models: A transfer learning approach,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 3, pp. 1681–1688, Sep. 2021, doi: 10.11591/ijeecs.v23.i3.pp1681-1688.
M. R. R. Allaam, “KLASIFIKASI GENUS TANAMAN ANGGREK MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN),” 2021.
A. H. Nasrullah and H. Annur, “JURNAL MEDIA INFORMATIKA BUDIDARMA Implementasi Metode Convolutional Neural Network Untuk Identifikasi Citra Digital Daun,” 2023, doi: 10.30865/mib.v7i2.5962.
T. Bayu Sasongko and A. Amrullah, “ANALISIS EFEK AUGMENTASI DATASET DAN FINE TUNE PADA ALGORITMA PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK (CNN),” vol. 10, no. 4, pp. 763–768, 2023, doi: 10.25126/jtiik.2023106583.
R. Kia Sandi Cahaya Putri, “IMPLEMENTASI DEEP LEARNING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI,” 2018.
S. Ummi Masruroh, F. Munthaha, A. Taufik Muharram, and M. Fikri Syahid, “ANALISA KINERJA MODEL FINE-TUNING DEEP LEARNING UNTUK KLASIFIKASI TULISAN TANGAN BAHASA ARAB,” 2022.
T. Sahro Winanto, C. Rozikin, and A. Jamaludin, “Analisa Performa Arsitektur Transfer Learning Untuk Mengindentifikasi Penyakit Daun Pada Tanaman Pangan,” 2023. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution 4.0 International (CC BY 4.0) that allows others to share — copy and redistribute the material in any medium or format and adapt — remix, transform, and build upon the material for any purpose, even commercially with an acknowledgment of the work's authorship and initial publication in this journal.
