PAPAYA TYPE CLASSIFICATION USING YOLOv8

Egi Verdiansyah; Firman Nurdiyansyah; Istiadi Istiadi

doi:10.52436/1.jutif.2024.5.5.2336

Egi Verdiansyah Department of Informatics, Faculty of Engineering, Widya Gama Malang University, Indonesia
Firman Nurdiyansyah Master of Management, Human Resource Management, Islamic University of Malang, Indonesia
Istiadi Electrical Engineering, Faculty of Engineering, Gadjah Mada University, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2336

Keywords: classification, papaya, You Only Look Once

Abstract

Papaya (Carica papaya L) is a fruit that is easily found in subtropical and tropical regions, including Indonesia. With many varieties of papaya, the manual method used in distinguishing papaya types by humans depends on individual knowledge which can cause inaccuracies in the classification process. The manual classification process also takes a very long time if production is done on a large scale. Therefore, a technology for sorting automation is needed, especially in the industrial world. This research aims to classify papaya classes based on their type. The classification is divided into four classes, namely bangkok papaya, california papaya, hawai papaya, and red lady papaya. The classification process in this study uses the YOLOv8 model, where the total dataset is 1200 papaya images with a training data division of 88% (1050 images), 8% validation data (100 images), and 4% test data (50 images). The dataset is separated according to papaya fruit class. Data training was conducted with 300 epochs. The results show that bangkok papaya has a mAP value of 96%, california papaya 97%, hawai papaya 95%, and red lady papaya has 95% mAP. The average class has a precision value of 99.6%, and recall 100.0%. It can be concluded that the YOLOv8 classification model is able to achieve a high level of accuracy.

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References

P. Barat et al., “Jurnal Inovasi Penelitian,” vol. 1, no. 4, 2020.

W. E. Sari, “JOINTECS,” vol. 3, no. 28, pp. 4–8, 2022.

A. D. Setiawan, “PENGEMBANGAN BUDIDAYA PEPAYA JENIS CALIFORNIA,” vol. 1, no. 1, pp. 51–59, 2023.

K. Magetan, D. Membuat, A. E. Cahyani, N. Fadillah, P. Oktaviana, and L. Yuhanna, “OLAHAN PEPAYA CALIFORNIA EMPOWERMENT OF THE KERIK VILLAGE COMMUNITY , MAGETAN REGENCY IN MAKING CALIFORNIA PAPAYA PRODUCTS,” pp. 12–19, 2022, doi: 10.25105/jamin.v4i1.10344.

A. Firlansyah, “Klasifikasi Tingkat Kematangan Buah Pepaya Berdasarkan Fitur Warna Menggunakan Jaringan Syaraf Tiruan,” vol. 6, no. 2, pp. 55–60, 2021.

S. V. Widyasari, M. I. Muttaqin, T. P. Ananda, and A. Stefanie, “IMPLEMENTASI INTERNET OF THINGS PADA SISTEM MONITORING KEMATANGAN BUAH PEPAYA CALIFORNIA DENGAN METODE DEEP LEARNING,” vol. 7, no. 3, pp. 1946–1952, 2023.

M. Ezar et al., “PENENTUAN KUALITAS BUAH PEPAYA CALIFORNIA MENGGUNAKAN METODE K-NN,” vol. 6, no. 1, pp. 1–8, 2021.

M. Ezar, A. Rivan, and G. R. Sung, “Identifikasi Mutu Buah Pepaya California ( Carica Papaya L .) Menggunakan Metode Jaringan Syaraf Tiruan,” vol. 10, pp. 113–119, 2021.

A. Jayadi and D. Meilinda, “KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA BERDASARKAN WARNA KULIT MENGGUNAKAN SENSOR WARNA TCS3200,” vol. 3, no. 2, pp. 1–13.

F. Bimantoro, P. Studi, T. Informatika, F. Teknik, and U. Mataram, “KLASIFIKASI JENIS DAN TINGKAT KEMATANGAN BUAH PEPAYA BERDASARKAN FITUR WARNA , TEKSTUR DAN BENTUK,” vol. 4, no. 1, pp. 75–87, 2022.

A. Kusuma, A. Rangga, S. Nurrohman, K. T. Anggoro, and R. Susun, “Implementasi Algoritma Yolo Dalam Pendeteksian Tingkat Kematangan Pada Buah Pepaya,” vol. 1, no. 1, pp. 74–77, 2023.

A. Helsaputra, A. L. Prasasti, R. R. Septiawan, and U. Telkom, “IMPLEMENTASI DEEP LEARNING UNTUK PREDIKSI TINGKAT KEMATANGAN DAN BOBOT BUAH PEPAYA DEEP LEARNING IMPLEMENTATION FOR WEIGHT AND RIPENESS PREDICTION OF,” vol. 8, no. 6, pp. 11993–11998, 2021.

F. Aziz, U. Bina, S. Informatika, U. N. Mandiri, and M. Wajah, “YOLO-V8 PENINGKATAN ALGORITMA UNTUK DETEKSI,” vol. 7, no. 3, pp. 1437–1444, 2023.

A. Setiyadi, E. Utami, and D. Ariatmanto, “Analisa Kemampuan Algoritma YOLOv8 Dalam Deteksi Objek Manusia Dengan Metode Modifikasi Arsitektur,” vol. 7, no. September, pp. 891–901, 2023.

L. Suroiyah, Y. Rahmawati, and R. Dijaya, “FACEMASK DETECTION USING YOLO V5 DETEKSI MASKER WAJAH MENGGUNAKAN METODE YOLO V5,” vol. 4, no. 6, pp. 1277–1286, 2023.

I. Maulana, N. Rahaningsih, T. Suprapti, T. Informatika, K. Cirebon, and O. Detection, “ANALISIS PENGGUNAAN MODEL YOLOV8 ( YOU ONLY LOOK ONCE ),” vol. 7, no. 6, pp. 3621–3627, 2023.

M. Algoritma, A. Beregerak, G. I. Andaru, and D. H. Fudholi, “Pengembangan Sistem Deteksi On-Shelf Availability Produk Abstrak,” vol. 5, no. 2, pp. 1980–1988, 2024.