Deteksi Penyakit Tanaman Cabai Menggunakan Algoritma YOLOv5 Dengan Variasi Pembagian Data

Laurenza Setiana Riva, Jayanta Jayanta


Rapid technological developments have resulted in various innovative techniques that help humans, including object detection which functions to identify each element in an image. Object detection is often used to overcome problems that occur because of its ability to identify each element in the image. One of the problems that is often encountered is a decrease in agricultural income due to disease in chili plants. The maintenance of chili plants has various obstacles including the impact of weather which causes the development of diseases and pests so that chili production has decreased. By implementing the object detection, farmers can easily identify diseases that attack chili plants through pictures so that chili disease can be treated more quickly. This study uses the YOLOv5 algorithm to test the performance of the model in identifying diseases in chili plants. Pictures were taken using a cellphone camera with dimensions of 3472x3472 pixels. The amount of image data used is 430 data. Image data is divided into 3 parts, namely train data, validation data, and test data. To get the best model, this study also conducted three experiments with different distribution of data. Experiment 1 with a division of 70:20:10, experiment 2 with a division of 75:15:10, and experiment 3 with a division of 80:10:10. From the experiments carried out, the best results were obtained, namely in experiment 3 with an average value obtained in the test of 0.947 with a translation of the precision, recall, and mAP values, namely 0.946, 0.936, and 0.959 respectively.


Object Detection;Penyakit Cabai;YOLOv5

Full Text:


S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” J Big Data, 2021,

Q. Aini, N. Lutfiani, H. Kusumah, and M. S. Zahran, “Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo,” CESS (Journal of Computer Engineering, System and Science), vol. 6, no. 2, p. 192, 2021, doi: 10.24114/cess.v6i2.25840.

Sahla Muhammed Ali, “Comparative Analysis of YOLOv3, YOLOv4 and YOLOv5 for Sign Language Detection,” IJARIIE, vol. 7, no. 4, pp. 2393–2398, 2021.

Xuewei Wang, Jun Liu, and Guoxu Liu, “Disease Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning,” Front. Plant Sci., vol. 12, 2021, doi:10.3389/fpls.2021.792244.

F. Arnia and K. Munadi, Pengantar Teknik Pengolahan Citra dan Visi Komputer. Yogyakarta: Ombak, 2018.

D. B. Lasfeto, Machine Learning Dalam Penelitian Bidang Pendidikan. 2021.

R. J. Gunawan, B. Irawan, “Pengenalan Ekspresi Wajah Berbasis Convolutional Neural Network Dengan Model Arsitektur VGG16,” eProceedings of Engineering, vol. 8, no. 5, 2021.

D. Kurniawan, Pengenalan Machine Learning dengan Python. Jakarta: PT Elex Media Komputindo, 2020.

GitHub, Inc., “Does validation data impact the model performance? #6023,” [2] GitHub, Inc. [Online]. Available: [Accessed: 01-Mar-2023].

C. S. Sriyano, E. B. Setiawan, “Pendeteksian Berita Hoax Menggunakan Naive Bayes Multinomial Pada Twitter Dengan Fitur Pembobotan Tf-idf,” e-Proceeding Eng. Vol.8, No.2, vol. 8, no. 2, pp. 3396–3405, 2021.

C. Geraldy, C. Lubis, “Pendeteksian Dan Pengenalan Jenis Mobil Menggunakan Algoritma You Only Look Once Dan Convolutional Neural Network,” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 8, no. 2, 2020.

Khairunnas, E. M. Yuniarno, A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” JURNAL TEKNIK ITS, vol. 10, no. 1, 2021.

V. Choudhari, M. Phadtare, S. Vartak, R. Pedram, “Comparison between YOLO and SSD MobileNet for Object Detection in a Surveillance Drone,” IJSREM, vol. 5, no. 10, 2021.

T. Delleji, Z. Chtourou, “An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones,” pp. 174-181, 2022.

U. Nepal, H. Eslamiat, “Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs,” Sensors, vol. 22, no. 2, 2022.

M. N Bramasta, M. Anshar, I. Nurtanio, “Prototipe Sistem Pengenalan Pelat Kendaraan Otomatis Berbasis YOLO pada Mekanisme Pintu Masuk Departemen Elektro UNHAS,” Seminar Nasional Elektroteknik dan Teknologi Informasi, 2022.

A. Bochkovskiy, C. Y. Wang, H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” 2020.

B. B. Dursa and K. K. Tune, “Developing Traffic Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road,” 2020



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Terindeks oleh :