PENERAPAN YOU ONLY LOOK ONCE VERSI 8 (YOLOv8) DALAM MENDETEKSI CITRA JENIS MAKANAN UNTUK MENAMPILKAN INFORMASI NUTRISI BERBASIS WEB
Abstract
Food nutrition information plays an important role in helping individuals monitor their daily dietary intake. However, manually identifying food types is often impractical and prone to errors. Therefore, this study aims to develop an image-based food detection system capable of automatically displaying nutritional information. The method employed in this research is You Only Look Once version 8 Nano (YOLOv8n), which was selected due to its high inference speed and relatively low computational requirements. The dataset was collected from two sources, namely the internet and direct image acquisition, with a ratio of 2:1, resulting in a total of 2,000 images across 13 food classes. The experimental results show that the YOLOv8n model achieved an [email protected] score of 89.3% on the test dataset. Several food classes obtained mAP values above 90%, while the remaining classes achieved scores ranging from 70% to 80%. In addition, the system successfully integrated the FatSecret API to retrieve and display nutritional information based on the detected food items, as well as to monitor users’ daily nutritional intake. These findings demonstrate that YOLOv8n can be effectively utilized for food type detection and can support the automated provision of nutritional information.
Downloads
References
serta kontribusi selama pelaksanaan penelitian ini. Berbagai bentuk arahan, masukan, dan kerja sama yang diberikan memiliki peran penting dalam mendukung kelancaran setiap tahapan penelitian hingga penyusunan laporan ini dapat diselesaikan dengan baik dan tepat waktu.
DAFTAR PUSTAKA
G. C. Utami, C. R. Widiawati, and P. Subarkah, “Detection of Indonesian Food to Estimate Nutritional Information Using YOLOv5,” Teknika, vol. 12, no. 2, pp. 158–165, Jun. 2023, doi: 10.34148/teknika.v12i2.636.
N. K. Hamzidah et al., “Performance Evaluation of Food Calorie Counter Mobile Application Based on CNN-YOLO Algorithms,” Jambura Journal of Electrical and Electronics Engineering, vol. 7, no. 2, pp. 253–263, 2025.
R. Supriyadi et al., “PENGEMBANGAN APLIKASI ESTIMASI KALORI MAKANAN BERBASIS CITRA DENGAN PENDEKATAN DETEKSI OBJEK MENGGUNAKAN YOLO,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 14, no. 1, pp. 1393–1404, Jan. 2026, doi: 10.23960/jitet.v14i1.8545.
H. A. Pratiwi, M. Cahyanti, and M. Lamsani, “IMPLEMENTASI DEEP LEARNING FLOWER SCANNER MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK,” Sebatik, vol. 25, no. 1, pp. 124–130, Jun. 2021, doi: 10.46984/sebatik.v25i1.1297.
Y. K. Bintang and H. Imaduddin, “PENGEMBANGAN MODEL DEEP LEARNING UNTUK DETEKSI RETINOPATI DIABETIK MENGGUNAKAN METODE TRANSFER LEARNING,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1442–1455, Aug. 2024, doi: 10.29100/jipi.v9i3.5588.
F. M. Syakir and M. Syani, “APLIKASI TAMU WAJIB LAPOR BERBASIS MOBILE (STUDI KASUS KP. PASIR PEUNDEUY CIHAMPELAS BANDUNG BARAT),” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, pp. 3867–3873, Oct. 2024, doi: 10.23960/jitet.v12i3s1.5211.
W. Djurianto, E. Maulana, and C. Brilianto, “Aplikasi Pendeteksi Objek Kendaraan Menggunakan Intel Movidius Neural Compute Stick dengan Algoritma MobileNet-YOLO,” Jurnal EECCIS2021,vol. 15, no. 3, pp. 92–97, 2021, [Online]. Available: https://jurnaleeccis.ub.ac.id/
I. M. D. Maleh, R. Teguh, A. S. Sahay, S. Okta, and M. P. Pratama, “Implementasi Algoritma You Only Look Once (YOLO) Untuk Object Detection Sarang Orang Utan Di Taman Nasional Sebangau,” Jurnal Informatika, vol. 10, no. 1, pp. 19–27, Mar. 2023, doi: 10.31294/inf.v10i1.13922.
I. Inayatul Arifah, F. Nur Fajri, and G. Qorik Oktagalu Pratamasunu, “Deteksi Tangan Otomatis Pada Video Percakapan Bahasa Isyarat Indonesia Menggunakan Metode YOLO Dan CNN,” Journal of Applied Informatics and Computing (JAIC), vol. 6, no. 2, pp. 2548–6861, 2022, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
Khairunnas, Yuniarno Mulyanto E, and Zaini Ahmad, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” JURNAL TEKNIK ITS, vol. 10, no. 1, pp. 50–55, 2021.
F. Alfaridzi, J. Dedy Irawan, and M. Orisa, “PERANCANGAN SISTEM MANAJEMEN USER HOTSPOT BERBASIS WEB MENGGUNAKAN APPLICATION PROGRAMMING INTERFACE (API) MIKROTIK,” Jurnal Mahasiswa Teknik Informatika), vol. 6, no. 2, pp. 974–981, 2022.
A. Triawan, A. Ramot, and Y. Siboro, “Penerapan Application Programming Interface (API) Pada Push Notification Untuk Informasi Monitoring Stok Barang Minim,” Jurnal Ilmiah Teknologi-Informasi&Sains, vol. 11, no. 2, pp. 107–114, 2021, doi: 10.36350/jbs.v11i2.
H. Y. Hermansyah and M. Maryam, “IMPLEMENTASI TEKNOLOGI APPLICATION PROGRAMMING INTERFACE PADA PERANCANGAN APLIKASI ABSENSI PEGAWAI,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 3, pp. 744–754, Aug. 2023, doi: 10.29100/jipi.v8i3.3890.
D. B. Avatara, Y. Sholva, and R. D. Nyoto, “Backend Service untuk Otomatisasi Validasi Format Pas Foto dengan Pengolahan Citra menggunakan Library OpenCV,” Jurnal Ilmiah Sains dan Teknologi, vol. 4, no. 2, pp. 406–420, 2026.
R. K. Ngantung and M. A. I. Pakereng, “Model Pengembangan Sistem Informasi Akademik Berbasis User Centered Design Menerapkan Framework Flask Python,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 3, pp. 1052–1062, Jul. 2021, doi: 10.30865/mib.v5i3.3054.
Y. Tamariska Bota and N. Setiyawati, “Pengembangan Sistem Informasi Perantara Bisnis Menggunakan Framework Flask,” Journal of Information Technology Ampera, vol. 3, no. 2, pp. 2774–2121, 2022, [Online]. Available: https://journal-computing.org/index.php/journal-ita/index
R. Andarsyah and A. Yanuar, “SENTIMEN ANALISIS APLIKASI POSAJA PADA GOOGLE PLAYSTORE UNTUK PENINGKATAN POSPAY SUPERAPP MENGGUNAKAN SUPPORT VECTOR MEACHINE,” Jurnal Teknik Informatika, vol. 16, no. 2, pp. 1–7, 2024.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
