Optimasi Data Preprocessing dan Hyperparameter Tuning pada Klasifikasi Penyakit Daun Apel menggunakan DenseNet169


  • Gilang Satria Putra Ramadhan Universitas Muhammadiyah Magelang, Magelang, Indonesia
  • Maimunah Maimunah * Mail Universitas Muhammadiyah Magelang, Magelang, Indonesia
  • Setiya Nugroho Universitas Muhammadiyah Magelang, Magelang, Indonesia
  • (*) Corresponding Author
Keywords: CNN; DenseNet169; Apple Leaf Disease; Data Preprocessing; Hyperparameter

Abstract

Apples are one of the horticultural commodities in Indonesia with production reaching 5,235,955 quintals in 2022, but decreasing to 3,925,628 quintals in 2023. One of the causes of this decline is diseases in apple plants that occur on the leaves, such as scab, black rot, and cedar rust, which can result in a decrease in the quality and quantity of production. Therefore, technology is needed for fast and accurate classification of diseases on apple leaves. This study uses a CNN model with DenseNet169 with optimization on data preprocessing and hyperparameter tuning to improve the accuracy of the apple leaf disease classification model. A total of 36 combinations of data preprocessing and hyperparameter tuning scenarios were tested on the apple leaf image dataset consisting of 4 classes: scab, black rot, cedar rust, and healthy. The optimal scenario is obtained from a combination of RGB + CLAHE with RMSprop optimizer and a learning rate of 0.0001 (P6 + H4), which results in 99.39% accuracy, 99.4% precision, 99.39% recall, and 99.39% f1-score. The results of this study show that the selection of the right preprocessing data and hyperparameter tuning greatly affects the performance of the apple leaf disease classification model.

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References

R. Prastiyaningtiyas and N. I. Hidayati, “Analisis Kelayakan Usaha Minuman Sari Buah Apel Di Desa Wonosari Nongkojajar Pasurua,” J. Ekon. Manaj. dan Akunt., vol. 2, no. 4, pp. 680–690, 2023.

M. N. Naben and E. Pani, “Pengaruh Berbagai Konsentrasi Gula terhadap Aktivitas Antioksidan pada Sari Buah Apel (Malus Sylvestris),” J. Pendidik. Tambusai, vol. 7, pp. 20410–20414, 2023.

BPS, “Produksi Buah-buahan dan Sayuran Menurut Jenis Tanaman Menurut Provinsi, 2023,” www.bps.go.id, 2024. https://www.bps.go.id/id/statistics-table/3/U0dKc1owczVSalJ5VFdOMWVETnlVRVJ6YlRJMFp6MDkjMw==/produksi-buah-buahan-menurut-jenis-tanaman-menurut-provinsi--2023.html?year=2023

L. R. Farida, Tazkia; Susilowati, Dwi; Maula, “Fenomena Peralihan Usahatani Apel Ke Komoditas Lain Di Kecamatan Bumiaji Kota Batu,” J. Sos. Ekon. Pertan. dan Agribisnis, vol. 1, no. 12, pp. 2439–2450, 2023, [Online]. Available: https://jim.unisma.ac.id/index.php/SEAGRI/article/view/20860

D. Husen, K. Kusrini, and K. Kusnawi, “Deteksi Hama Pada Daun Apel Menggunakan Algoritma Convolutional Neural Network,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2103, 2022, doi: 10.30865/mib.v6i4.4667.

V. K. Vishnoi, K. Kumar, B. Kumar, S. Mohan, and A. A. Khan, “Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network,” IEEE Access, vol. 11, no. November 2022, pp. 6594–6609, 2023, doi: 10.1109/ACCESS.2022.3232917.

A. M. Lesmana, R. P. Fadhillah, and C. Rozikin, “Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN),” J. Sains dan Inform., vol. 8, no. 1, pp. 21–30, 2022, doi: 10.34128/jsi.v8i1.377.

Q. N. Azizah and Andreyestha, “Klasifikasi Penyakit Daun Apel Menggunakan Convolutional Neural Network,” J. Ilmu Komput. dan Inform., vol. 3, no. 1, 2020.

H. P. Hadi and E. H. Rachmawanto, “Ekstraksi Fitur Warna Dan Glcm Pada Algoritma Knn Untuk Klasifikasi Kematangan Rambutan,” J. Inform. Polinema, vol. 8, no. 3, pp. 63–68, 2022, doi: 10.33795/jip.v8i3.949.

M. Wahid Islahfari, A. Lawi, and A. M. A. Siddik, “Perbandingan Kinerja Model Ensembled Transfer Learning Pada Klasifikasi Penyakit Daun Tomat,” Semin. Nas. Tek. Elektro dan Inform., vol. 8, no. 1, pp. 286–291, 2022, [Online]. Available: http://118.98.121.208/index.php/sntei/article/view/3630

S. Sahibu and I. Taufik, “Implementation of the Convolutional Neural Network Algorithm for Classifying Types of Organic and Non-Organic Waste Implementasi Algoritma Convolutional Neural Network untuk Klasifikasi Jenis Sampah Organik dan Non Organik,” vol. 4, no. July, pp. 840–852, 2024.

A. P. Syahputra, A. C. Siregar, and R. W. S. Insani, “Comparison of CNN Models With Transfer Learning in the Classification of Insect Pests,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 17, no. 1, p. 103, 2023, doi: 10.22146/ijccs.80956.

F. A. Breve, “COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles[Formula presented],” Expert Syst. Appl., vol. 204, no. May, p. 117549, 2022, doi: 10.1016/j.eswa.2022.117549.

A. Solihin, D. I. Mulyana, and M. B. Yel, “Klasifikasi Jenis Alat Musik Tradisional Papua menggunakan Metode Transfer Learning dan Data Augmentasi,” J. SISKOM-KB (Sistem Komput. dan Kecerdasan Buatan), vol. 5, no. 2, pp. 36–44, 2022, doi: 10.47970/siskom-kb.v5i2.279.

N. Pratama, M. Liebenlito, and Y. Irene, “Perbandingan Model Klasifikasi Transfer Learning Convolutional Neural Network Tumor Otak menggunakan Citra Magnetic Resonance Imaging,” J. Sehat Indones., vol. 6, no. 01, pp. 308–318, 2024, doi: 10.59141/jsi.v6i01.81.

Ulfah Nur Oktaviana and Yufis Azhar, “Garbage Classification Using Ensemble DenseNet169,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1207–1215, 2021, doi: 10.29207/resti.v5i6.3673.

K. R. R. Wardani, H. Suryalim, V. J. L. Engel, and H. Christian, “Analisis Pemilihan Optimizer dalam Arsitektur Convolution Neural Network VGG16 dan Inception untuk Sistem Pengenalan Wajah,” J. Edukasi dan Penelit. Inform., vol. 9, no. 2, p. 186, 2023, doi: 10.26418/jp.v9i2.60432.

A. K. Putri and A. S. Handayani, “Penerapan Arsitektur EfficientNet Untuk Pembuatan Model Algoritma Convolutional Neural Network Pada Klasifikasi Bahasa Isyarat,” vol. 6, no. 2, pp. 758–766, 2024, doi: 10.47065/bits.v6i2.5592.

H. Abbaszadeh, “Apple diseases 4 Classes (PlantVillage),” Kaggle.com, 2023. https://www.kaggle.com/datasets/hamidabbaszadeh/apple-leave-diseases-dataset-without-augmentation

A. Arjun, “Klasifikasi Citra Pada Tingkat Kematangan Buah Pisang Menggunakan Algoritma Deep Learning,” J. Ekon. Manaj. Sist. Inf., vol. 5, no. 3, pp. 203–208, 2024, doi: 10.31933/jemsi.v5i3.1786.

Nurul Chamidah, Mayanda Mega Santoni, and Nurhafifah Matondang, “Oversampling Method on Classifying Hypertension Using Naive Bayes, Decision Tree, and Artificial Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 4, pp. 635–641, 2020, doi: 10.29207/resti.v4i4.2015.

M. Resa Arif Yudianto, P. Sukmasetya, R. Abul Hasani, and D. Sasongko, “Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1367–1375, 2022, doi: 10.47065/bits.v4i3.2575.

N. P. Ekananda and D. Riminarsih, “Identifikasi Penyakit Pneumonia Berdasarkan Citra Chest X-Ray Menggunakan Convolutional Neural Network,” J. Ilm. Inform. Komput., vol. 27, no. 1, pp. 79–94, 2022, doi: 10.35760/ik.2022.v27i1.6487.

A. Desiani, D. A. Zayanti, R. Primartha, F. Efriliyanti, and N. A. C. Andriani, “Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina,” J. Edukasi dan Penelit. Inform., vol. 7, no. 2, p. 255, 2021, doi: 10.26418/jp.v7i2.47205.

S. A. Widiarto, W. A. Saputra, and A. R. Dewi, “Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 2, pp. 348–359, 2021, doi: 10.29100/jipi.v6i2.2102.

S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, 2020, doi: 10.29080/systemic.v5i2.798.

J. E. Widyaya and S. Budi, “The Effect of Preprocessing on the Classification of Diabetic Retinopathy with the Transfer Learning Convolutional Neural Network Approach,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 1, pp. 110–124, 2021.

M. Ikhsan and A. Wiranda Hakiki, “Analisis Perbandingan Metode Histogram Equalization Dan Gaussian Filter Untuk Perbaikan Kualitas Citra,” J. Sci. Soc. Res., vol. 4307, no. 2, pp. 487–492, 2024, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR

M. M. Sugiman and H. D. Purnomo, “Prediksi Kegagalan Transformator Daya dengan Metode DGA (Dissolved Gas Analysis) Menggunakan Random Forest Berbasis TDCG,” J. Media …, vol. 8, pp. 441–449, 2024, doi: 10.30865/mib.v8i1.7036.


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Article History
Submitted: 2024-10-27
Published: 2024-12-03
Abstract View: 135 times
PDF Download: 119 times
How to Cite
Ramadhan, G. S., Maimunah, M., & Nugroho, S. (2024). Optimasi Data Preprocessing dan Hyperparameter Tuning pada Klasifikasi Penyakit Daun Apel menggunakan DenseNet169. Building of Informatics, Technology and Science (BITS), 6(3), 1352-1362. https://doi.org/10.47065/bits.v6i3.6134
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