Detection of Indonesian Food to Estimate Nutritional Information Using YOLOv5

  • Gina Cahya Utami Department of Informatics, Universitas Amikom Purwokerto, Purwokerto, Central Java
  • Chyntia Raras Widiawati Department of Information Technology, Universitas Amikom Purwokerto, Purwokerto, Central Java
  • Pungkas Subarkah Department of Informatics, Universitas Amikom Purwokerto, Purwokerto, Central Java
Keywords: YOLOv5, Object Detection, Nutritions

Abstract

Currently, the development of online food delivery service applications is very popular. The application offers convenience in finding and fulfilling food needs. That circumstance has an impact such as not controlling the type and amount of food consumed. Therefore, to maintain a healthy lifestyle, people need to eat healthy and nutritious food. The goal of this research is to build a model using the YOLOv5 model that can detect images of Indonesian food so that nutritional estimation can then be carried out by taking information per serving data sourced from the FatSecret Indonesia website. The methods of this research include data collection, data pre-processing, training, testing, evaluation, image detection, and model export. The outcome of this research is an object detection model that is ready to be implemented in android applications or websites to detect images of Indonesian food which can be estimated for each nutrient. Based on the detection results, 98.6% for an average of a curacy, 95% for precision, 95.3% for recall, and 95% for F1-Score were obtained. The results of the detection are then used to estimate nutrition by taking information per portion from the FatSecret Indonesia website. From the experiments that were carried out on seven pictures of Indonesian food, the estimation was carried out well by displaying various nutritional information including energy, protein, fat, and carbohydrates.

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References

M. A. Assuja, S. Saniati, and Y. Nurhasanah, “Artificial Intelligence (AI) Fruit Detection for Nutrition Information - using Convolutional Neural Network,” thesis, Universitas Teknokrat Indonesia, Bandar Lampung, 2019 .

R. Kurniasari, L. R. Sefrina, and S. Sabrina, “Edukasi Penggunaan aplikasi Pesan Antar Makanan Online Dengan Bijak Untuk Menciptakan status gizi optimal,” SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan, vol. 6, no. 1, pp. 230–234, 2022. doi:10.31764/jpmb.v6i1.7208.

A. Poerna Wardhanie and Ahmad Nizar Yogatama, “Peran E-Service Quality Dalam Meningkatkan Penjualan Makanan Melalui Penggunaan Aplikasi ShopeeFood,” JoTI, vol. 3, no. 2, pp. 67–77, 2022, doi: 10.37802/joti.v3i2.225.

Y. M. Fristanti and A. Ruhana, “Hubungan Intensitas Penggunaan Smartphone Terhadap Aktivitas Fisik dan Tingkat Konsumsi Pangan Pada Mahasiswa Gizi Unesa Di Era Pandemi Covid–19,” Jurnal Gizi Unesa, vol. 1, no. 2, pp. 89–94, 2021.

Menteri Kesehatan, Jakarta: Direktur Jenderal Peraturan Perundang-Undangan Kementerian Hukum dan Hak Asasi Manusia Republik Indonesia, 2019.

M. Dandi, H. Fauzi Tsp, and S. Rizal, “Perancangan Aplikasi Perhitungan Nutrisi Pada Makanan Berbasis Android Dengan Metode Convolutional Neural Network (Cnn),” e-Proceeding Eng., vol. 8, no. No.5, pp. 5000–5008, 2021.

M. L. Chiang, C. A. Wu, J. K. Feng, C. Y. Fang, and S. W. Chen, “Food Calorie and Nutrition Analysis System based on Mask R-CNN,” 2019 IEEE 5th Int. Conf. Comput. Commun. ICCC 2019, pp. 1721–1728, 2019, doi: 10.1109/ICCC47050.2019.9064257.

R. E. Jayaputra, “Estimasi Kalori pada makanan melalui Citra Makanan menggunakan Model SSD (Single Shot Detector) pada Mobile Device,” thesis, Universitas Islam Indonesia, Yogyakarta, 2020.

F. Jubayer et al., “Detection of mold on the food surface using YOLOv5,” Curr. Res. Food Sci., vol. 4, pp. 724–728, 2021, doi: 10.1016/j.crfs.2021.10.003.

P. Adarsh, P. Rathi, and M. Kumar, “YOLO v3-Tiny: Object Detection and Recognition using one stage improved model,” 2020 6th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2020, no. August, pp. 687–694, 2020, doi: 10.1109/ICACCS48705.2020.9074315.

M. E. Salman, G. Çakirsoy Çakar, J. Azimjonov, M. Kösem, and İ. H. Cedi̇moğlu, “Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm,” Expert Syst. Appl., vol. 201, no. April, 2022, doi: 10.1016/j.eswa.2022.117148.

G. Liu, J. C. Nouaze, P. L. T. Mbouembe, and J. H. Kim, “YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3,” Sensors (Switzerland), vol. 20, no. 7, pp. 1–20, 2020, doi: 10.3390/s20072145.

J. Yu and W. Zhang, “Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4,” Eng. Lett., vol. 30, no. 4, pp. 1493–1503, 2022, doi: 10.3390/s21093263.

A. Casado-García et al., “LabelStoma: A tool for stomata detection based on the YOLO algorithm,” Comput. Electron. Agric., vol. 178, no. August, p. 105751, 2020, doi: 10.1016/j.compag.2020.105751.

X. Zhou, L. Jiang, C. Hu, S. Lei, T. Zhang, and X. Mou, “YOLO-SASE: An Improved YOLO Algorithm for the Small Targets in Complex Backgrounds,” Sensors, vol. 22, no. 12, pp. 1–14, 2022, doi: 10.3390/s22124600.

D. Thuan, “Evolution of YOLO Algorithm and YOLOv5: The State-of-the-art Object Detection Algorithm,” thesis, Oulu University of Applied Sciences, Finland, 2021.

F. Ramasari, F. Firdaus, S. Nita, and K. Kartika, “Penggunaan Metode You Only Look Once dalam Penentu Pindah Tanaman Cabai Besar Ternotifikasi Telegram ,” Elektron : Jurnal Ilmiah, vol. 13, no. 2, pp. 45–52, 2021. doi:10.30630/eji.13.2.229.

A. Amwin, “Deteksi dan Klasifikasi Kendaraan Berbasis Algoritma You Only Look Once (YOLO),” thesis, Universitas Islam Indonesia, Yogyakarta, 2021.

M. Y. A. Thoriq, I. A. Siradjuddin, and K. E. Permana, “Deteksi Wajah Manusia berbasis One Stage Detector Menggunakan Metode You Only Look Once (YOLO),” J. Teknoinfo, vol. 17, no. 1, p. 66, 2023, doi: 10.33365/jti.v17i1.1884.

M. T. Stefanus Christian Adi Pradhana, Untari Novia Wisesty S.T.,M.T., Febryanthi Sthevanie S.T., “Pengenalan Aksara Jawa dengan Menggunakan Metode Convolutional Neural Network,” e-Proceeding Eng., vol. 7, no. 1, pp. 2558–2567, 2020.

O. Rochmawanti, F. Utaminingrum, and F. A. Bachtiar, “Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 4, p. 805, 2021, doi: 10.25126/jtiik.2021844441.

I. W. Prastika and E. Zuliarso, “Deteksi penyakit kulit wajah menggunakan tensorflow dengan metode convolutional neural network,” J. Manaj. Inform. dan Sist. Inf., vol. 4, no. 2, pp. 84–91, 2021, doi: 10.36595/misi.v4i2.418.

B. Putra, G. Pamungkas, B. Nugroho, and F. Anggraeny, “Deteksi dan Menghitung Manusia Menggunakan YOLO-CNN,” J. Inform. dan Sist. Inf., vol. 02, no. 1, pp. 67–76, 2021.

A. Rosadi et al., “Analisis Sentimen Berdasarkan Opini Pengguna pada Media Twitter Terhadap BPJS Menggunakan Metode Lexicon Based dan Naïve Bayes Classifier,” J. Ilm. Komputasi, vol. 20, no. 1, pp. 39–52, 2021, doi: 10.32409/jikstik.20.1.401.

N. Hadianto, H. B. Novitasari, and A. Rahmawati, “Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 163–170, 2019, doi: 10.33480/pilar.v15i2.658.

K. A. Shianto, K. Gunadi, and E. Setyati, “Deteksi Jenis Mobil Menggunakan Metode YOLO Dan Faster R-CNN,” J. Infra, vol. 7, no. 1, pp. 157–163, 2019, [Online]. Available: http://publication.petra.ac.id/index.php/teknik-informatika/article/view/8065

M. Meiriyama, S. Devella, and S. M. Adelfi, “Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2573–2584, 2022, doi: 10.35957/jatisi.v9i3.2974.

A. Malta, M. Mendes, and T. Farinha, “Augmented reality maintenance assistant using yolov5,” Appl. Sci., vol. 11, no. 11, pp. 1–14, 2021, doi: 10.3390/app11114758.

Published
2023-06-22
How to Cite
Utami, G. C., Widiawati, C. R., & Subarkah, P. (2023). Detection of Indonesian Food to Estimate Nutritional Information Using YOLOv5. Teknika, 12(2), 158-165. https://doi.org/10.34148/teknika.v12i2.636
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Articles