Implementasi Metode Faster R-CNN Untuk Deteksi Penyakit Tanaman Mentimun Melalui Citra Daun
DOI:
https://doi.org/10.29407/nqms7824Abstract
Mentimun (Cucumis Sativus) merupakan salah satu tanaman hortikultura penting di Indonesia dengan nilai ekonomi tinggi, namun rentan terhadap serangan hama dan penyakit yang dapat menurunkan kualitas dan kuantitas hasil panen. Penelitian ini bertujuan untuk menerapkan metode Faster R-CNN dan arsitektur ResNet50 dalam mendeteksi penyakit pada tanaman mentimun. Alur proses penelitian ini terdiri dari pengumpulan dataset, preprocessing data, pelatihan, dan evaluasi. Dataset yang digunakan berjumlah 799 citra daun mentimun, yang terdiri dari lima kelas: Fresh Leaf, Anthracnose, Bacterial Wilt, Downy Mildew, dan Gummy Stem Blight. Pelatihan model menggunakan K-Fold Cross Validation sebanyak 5 fold (K=5) dengan optimizer SGD, batch size 2, learning rate 0,007, dan 20 epoch. Hasil menunjukan menghasilkan rata-rata akurasi sebesar 0.86%, precision 0.87%, recall 0.88%, dan F1-score 0.87%. Hal ini membuktikan bahwa model mampu melakukan generalisasi dengan baik terhadap variasi data.
Keywords:
Deteksi, Faster R-CNN, Mentimun, Resnet-50##plugins.themes.default.displayStats.downloads##
References
[1] Ratna Indah Juwita Harahap, Sumi Khairani, and Rismayanti, “Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun,” J. Ilmu Komput. dan Sist. Inf., vol. 3, no. 2, pp. 135–145, 2024, doi: 10.70340/jirsi.v3i2.123.
[2] F. A. Junior and M. R. N. Majiid, “Cucumber Disease Classification with Ensemble Learning Method for Complex Datasets,” J. Tek. Inform., vol. 16, no. 2, pp. 161–169, 2023, doi: 10.15408/jti.v16i2.34618.
[3] M. Arifin, A. Yuniarti, and N. Suciati, “Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features,” Int. J. Robot. Control Syst., vol. 4, no. 3, pp. 1429–1445, 2024, doi: 10.31763/ijrcs.v4i3.1529.
[4] A. C. Milano, “Klasifikasi Penyakit Daun Padi Menggunakan Model Deep Learning Efficientnet-B6,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 1, 2024, doi: 10.23960/jitet.v12i1.3855.
[5] D. Iswantoro and D. Handayani UN, “Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode Convolutional Neural Network (CNN),” J. Ilm. Univ. Batanghari Jambi, vol. 22, no. 2, p. 900, 2022, doi: 10.33087/jiubj.v22i2.2065.
[6] M. Farij Amrulloh et al., “Klasifikasi Penyakit Daun Bawang Menggunakan Algoritma CNN Xception,” Pros. SEMNAS INOTEK (Seminar Nas. Inov. Teknol., vol. 8, pp. 617-623., 2024, doi: https://doi.org/10.29407/inotek.v8i1.5011.
[7] E. D. Ratnasari, D. A. Rudira, and A. Surya Buana, “Klasifikasi Penyakit Daun Sawi Hijau Dengan Metode Cnn,” Pros. Semin. Nas. Teknol. Dan Sains, vol. 3, pp. 338–393, 2024, doi: https://doi.org/10.29407/8gmc9b37.
[8] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.
[9] H. Harmiansyah, E. T. Oviana, R. Alpaizon, D. P. Khalifah, and P. Dwirotama, “Sistem Deteksi Hama dan Penyakit Tanaman Mangga (Mangifera indica L.) Berbasis Deep Learning Menggunakan Model Pra Latih YOLOv5,” Agrikultura, vol. 35, no. 1, p. 151, 2024, doi: 10.24198/agrikultura.v35i1.53834.
[10] Y. Rizki, R. Medikawati Taufiq, H. Mukhtar, and D. Putri, “Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN,” IT J. Res. Dev., vol. 5, no. 2, pp. 215–225, 2021, doi: 10.25299/itjrd.2021.vol5(2).5831.
[11] T. Berliani, E. Rahardja, and L. Septiana, “Perbandingan Kemampuan Klasifikasi Citra X-ray Paru-paru menggunakan Transfer Learning ResNet-50 dan VGG-16,” J. Med. Heal., vol. 5, no. 2, pp. 123–135, 2023, doi: 10.28932/jmh.v5i2.6116.
[12] P. I. Nainggolan, D. S. Prasvita, and D. S. Bukit, “Klasifikasi Informasi Kesehatan Pada Data Media Sosial Menggunakan Support Vector Machine dan K-Fold Cross Validation,” Malikussaleh J. Mech. Sci. Technol., vol. 5, no. 2, p. 34, 2021, doi: 10.29103/mjmst.v5i2.6317.
[13] D. Wahyudi Rustanto, F. Liantoni, and N. Pradana Taufik Prakisya, “Identifikasi Penyakit Daun pada Tanaman Padi Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) dan Metode K-Nearest Neighbour (KNN),” J. Sist. dan Teknol. Inf., vol. 12, no. 1, pp. 100–106, 2024, doi: 10.26418/justin.v12i1.69752.
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