PERBANDINGAN METODE TRANSFER LEARNING DALAM KLASIFIKASI PENYAKIT DAUN PADI
DOI:
https://doi.org/10.35316/.v11i1.24-34Kata Kunci:
Rice leaf disease, Transfer learning, ResNet152, DenseNet121, Image ClassificationAbstrak
This study compares four transfer learning-based CNN models, namely VGG19, ResNet152, MobileNetV2, and DenseNet121, for the classification of 10 classes of rice leaf diseases. Evaluation results on the test dataset show that ResNet152 achieves the best performance with an accuracy of 0.9553, precision of 0.9589, recall of 0.9553, and F1-score of 0.9558, followed by DenseNet121 (accuracy 0.9433), MobileNetV2 (0.9353), and VGG19 (0.9247). ResNet152 excels in recognizing complex features through its skip connection mechanism, while DenseNet121 is more efficient with the lowest validation loss. MobileNetV2 is the lightest and fastest model, making it suitable for resource-limited devices. Based on the confusion matrix analysis, all models are able to classify the neck blast class perfectly; however, misclassifications still occur among visually similar classes such as brown spot, narrow brown spot, and leaf blast. Overall, transfer learning is proven effective for rice leaf disease classification, with ResNet152 and DenseNet121 being the most recommended models.
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Referensi
[1] N. Istiqomah and M. Murinto, “Klasifikasi Penyakit Tanaman Padi Berbasis Citra Daun Menggunakan Convolutional Neural Network (CNN),” JSTIE (Jurnal Sarjana Teknik Informatika) (E-Journal), vol. 12, no. 1, p. 18, Feb. 2024, doi: 10.12928/jstie.v12i1.27314.
[2] T. Y. Al Rajab and N. Nafiiyah, “Evaluasi Kinerja Model CNN Berbasis Transfer Learning dalam Klasifikasi Penyakit Daun Padi,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 7, no. 3, pp. 989–995, Mar. 2026, doi: 10.30865/json.v7i3.9539.
[3] M. F. A. Maulana, N. M. Anggadimas, and D. A. Sani, “Klasifikasi Citra Penyakit Daun Padi Dengan Metode CNN Menggunakan Arsitektur ResNet50V2,” Journal of Computer Engineering, System and Science, vol. 10, no. 2, pp. 517–529, Jul. 2025, doi: https://doi.org/10.24114/cess.v10i2.66960.
[4] A. Agustina, F. Yanto, E. Budianita, I. Iskandar, and F. Syafria, “Klasifikasi Penyakit Tanaman Padi Menggunakan Metode CNN Arsitektur DenseNet-121 dan Augmentasi Data,” JOISIE (Journal Of Information Systems And Informatics Engineering), vol. 8, no. 1, pp. 124–134, 2024, doi: 10.35145/joisie.v8i1.4256.
[5] F. H. Hawari, F. Fadillah, M. R. Alviandi, and T. Arifin, “Klasifikasi Penyakit Padi Menggunakan Algoritma CNN (Convolutional Neural Network),” Jurnal Responsif, vol. 4, no. 2, pp. 184–189, 2022, doi: https://doi.org/10.51977/jti.v4i2.856.
[6] R. Ardianto and S. K. Wibisono, “Analisis Deep Learning Metode Convolutional Neural Network Dalam Klasifikasi Varietas Gandum,” Jurnal Kolaboratif Sains, vol. 6, no. 12, pp. 2081–2092, 2023, doi: 10.56338/jks.v6i12.4938.
[7] M. Khoiruddin, A. Junaidi, and W. A. Saputra, “Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network,” Journal of Dinda, vol. 2, no. 1, pp. 37–45, 2022, doi: https://doi.org/10.20895/dinda.v2i1.341.
[8] R. Shinta, Jasrilb, M. Irsyad, F. Yanto, and S. Sanjaya, “Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN dengan Arsitektur VGG-19,” Jurnal Sains dan Informatika, vol. 9, no. 1, pp. 37–45, Apr. 2023, doi: 10.22216/jsi.v9i1.2175.
[9] R. Suciani, D. A. Anugra, E. Faisal, and D. Nuswantoro, “Deteksi Penyakit Daun Padi Menggunakan Deep Learning dengan Arsitektur CNN (Detection of Rice Leaf Disease Using Deep Learning with CNN Architecture),” Journal of Information Technology and Computer Science (INTECOMS), vol. 8, no. 5, 2025, doi: https://doi.org/10.31539/9112kc41.
[10] T. D. A. R. Putra and Y. F. Riti, “Implementasi Arsitektur MobileNetV2 untuk Klasifikasi Rumah Adat Berbasis Android,” Journal Sistem dan Teknologi Informasi, vol. 14, no. 2, pp. 217–222, 2026, doi: 10.26418/justin.v14i2.97977.
[11] M. M. Nugraha, M. D. F. Saputra, D. A. Fauzan, and E. Y. Puspaningrum, “Augmentasi Data Pengenalan Citra Batik Yogyakarta Menggunakan Pendekatan Random Crop, Rotate, Dan Mixup,” Prosiding Seminar Nasional Informatika Bela Negara, vol. 5, no. 1, pp. 152–158, 2025, doi: 10.33005/santika.v5i1.680.
[12] A. Banerjee and A. Chandra, “A comparative study on 2-D convolutional neural network architectures for the detection of cognitive impairment from structural MRI,” Discover Electronics, vol. 3, no. 1, p. 8, Jan. 2026, doi: 10.1007/s44291-026-00156-4.
[13] T. H. Nguyen, T. N. Nguyen, and B. V. Ngo, “A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease,” AgriEngineering, vol. 4, no. 4, pp. 871–887, Dec. 2022, doi: 10.3390/agriengineering4040056.
[14] I. Santoso, A. M. Manurung, and E. R. Subhiyakto, “Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification,” Journal of Applied Informatics and Computing, vol. 9, no. 2, pp. 284–294, 2025, doi: 10.30871/jaic.v9i2.8968.
[15] J. Wang, H. Li, G. Huo, C. Li, and Y. Wei, “Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition,” Electronics (Switzerland), vol. 12, no. 4, Feb. 2023, doi: 10.3390/electronics12040791.
[16] A. Z. Hibatullaha, M. F. Rahmanb, and A. P. Saric, “Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah,” ALINIER: Journal of Artificial Intelligence & Applications, vol. 5, no. 2, pp. 129–139, Nov. 2024, doi: https://doi.org/10.36040/alinier.v5i2.11061.
[17] I. S. Lestari, J. Jumadi, and N. Lukman, “Implementasi Convolutional Neural Network dengan Pre-Trained Model MobileNetV2 untuk Deteksi Kolesterol,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 2, pp. 173–183, Jul. 2024, doi: 10.36341/rabit.v9i2.4732.
[18] P. Enkvetchakul and O. Surinta, “Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition,” Applied Science and Engineering Progress, vol. 15, no. 3, Jul. 2022, doi: 10.14416/j.asep.2021.01.003.
[19] S. A. S. Mola, B. O. D. K. Wadu, A. N. Kenlopo, and V. C. K. Tungga, “Perbandingan Arsitektur ResNet50V2, InceptionV3, dan DenseNet121 dalam Klasifikasi Pengenalan Ekspresi Wajah,” JIKO (Jurnal Informatika dan Komputer), vol. 9, no. 2, pp. 285–294, Jun. 2025, doi: 10.26798/jiko.v9i2.1584.
[20] T. Zhou, X. Ye, H. Lu, X. Zheng, S. Qiu, and Y. Liu, “Dense Convolutional Network and Its Application in Medical Image Analysis,” Biomed Res. Int., vol. 2022, pp. 1–22, 2022, doi: 10.1155/2022/2384830.
[21] M. S. I. Khan et al., “Accurate brain tumor detection using deep convolutional neural network,” Comput. Struct. Biotechnol. J., vol. 20, pp. 4733–4745, Jan. 2022, doi: 10.1016/j.csbj.2022.08.039.
[22] Hanisa, A. Kartika, D. Pakpahan, W. Novitasari, Q. R. Ismail, and N. Hayaty, “Penggunaan Convolutional Neural Network (CNN) untuk Menentukan Kesegaran Ikan Bawal Putih,” Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan, vol. 13, no. 1, pp. 16–22, 2024, doi: https://doi.org/10.31629/sustainable.v13i1.
[23] O. Harris and M. Andrews, “Effects and results of dropout layer in reducing overfitting with convolutional Neural Networks (CNN),” World Journal of Advanced Engineering Technology and Sciences, vol. 13, no. 2, pp. 836–853, 2024, doi: 10.30574/wjaets.2024.13.2.0584.
[24] A. H. Nasrullah, “Convolutional Neural Network untuk Deteksi Penyakit Paru-Paru Menggunakan Citra Radiografi,” Journal of Computers, Informatics, and Vocational Education, vol. 1, no. 2, pp. 37–42, 2024.
[25] F. Ramadhan and J. Hernadi, “Evaluasi Optimizer Adam dan RMSProp pada Arsitektur VGG-19 Klasifikasi Ekspresi Wajah Manusia,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 2, pp. 1414–1426, Mar. 2025, doi: 10.29100/jipi.v10i2.6197.
[26] M. M. Chinthia, E. P. Cynthia, M. Eka, and F. Nursalisah, “Pengaruh Parameter Learning Rate terhadap Konvergensi Model Neural Network dalam Proses Pelatihan,” Jurnal Ilmu Komputer dan Teknik Informatika, vol. 1, no. 1, pp. 21–28, 2025, doi: https://doi.org/10.64803/juikti.v1i1.45.
[27] F. R. Valerian, M. Syarief, and D. A. Fatah, “Klasifikasi tingkat obesitas menggunakan metode gbm dan confusion matrix,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 2, pp. 2242–2249, 2025, doi: https://doi.org/10.36040/jati.v9i2.13062.
[28] A. Indrajaya, “Perbandingan Klasifikasi Kacang Kering dengan Algoritma KNN dan SVM Linear,” Jurnal Komputer dan Informatika, vol. 20, no. 1, pp. 62–70, 2025.
[29] N. Safitri, D. Kusnandar, and S. Martha, “Implementasi Algoritma K-Nearest Neighbor Dengan Normalisasi Z-Score Dalam Klasifikasi Penerima Bantuan Sosial Desa Serunai,” Buletin Ilmiah Mat. Stat. dan Terapannya (Bimaster), vol. 13, no. 1, pp. 99–106, 2024, doi: https://doi.org/10.26418/bbimst.v13i1.74063.
[30] N. I. Chaerunnisa and M. Y. T. Sulistyono, “Machine Learning-Based Teacher Performance Classification Using Administrative and Credit Point Assessment ( PAK ) Data : A Comparative Study of Decision Tree and Naive Bayes,” Journal of Applied Informatics and Computing (JAIC), vol. 10, no. 2, pp. 1853–1863, 2026, doi: https://doi.org/10.30871/jaic.v10i2.12377.
[31] Rahayu, B. Irawan, and O. S. Bachri, “Klasifikasi Sentimen Ulasan Produk Samsung pada Platform E-Commerce Tokopedia Menggunakan Algoritma Naive Bayes,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 11, no. 1, pp. 1904–1921, 2026, doi: https://doi.org/10.36341/rabit.v11i1.7557.
[32] L. M. Ni’mah and D. Kurniawan, “Model Klasifikasi Cerdas Gangguan Tidur Berbasis Machine Learning Random Forest pada Data Kesehatan dan Perilaku Harian,” Technology and Science (BITS), vol. 7, no. 3, pp. 1717–1729, 2025, doi: 10.47065/bits.v7i3.8631.
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