Development of a Family Meal Plan Application with Voice Recognition, Siri Integration, and Nutrition Management Using Data-Driven Approach

Livanty Efatania Dendy; Celinka Eira Jove; Rinabi Tanamal

doi:10.24167/sisforma.v13i1.14489

Development of a Family Meal Plan Application with Voice Recognition, Siri Integration, and Nutrition Management Using Data-Driven Approach

Livanty Efatania Dendy, Celinka Eira Jove, Rinabi Tanamal

Abstract

Maintaining a healthy diet has become increasingly important as fast-food consumption rises and nutritional balance is often overlooked. This research proposes a technology-based Family Meal Plan Application equipped with voice recognition, Siri integration, nutrition management, and automated grocery lists to assist home cooks in planning healthier meals more efficiently. The application was designed using Figma, developed with Flutter, and integrated with the Siri API to provide a seamless hands-free experience during cooking. A nutrition prediction model was also developed using a Kaggle dataset and deployed locally to generate real-time nutritional analysis. The final results of this study show that the application prototype successfully meets user needs in simplifying weekly meal planning, providing accurate voice-based cooking guidance, and offering automated nutrition calculations for each family member. User testing involving home cooks indicated increased efficiency in meal preparation, reduced cognitive load during recipe execution, and improved awareness of daily nutritional intake. The voice command system operated with high accuracy and responsiveness, while the automated grocery list feature significantly streamlined weekly shopping activities. Overall, the application demonstrates strong potential to support healthier family eating habits through an intelligent, data-driven, and user-friendly solution.

Keywords

Application development; Voice recognition; Siri API integration; home cook app; Nutrition management

Full Text:

PDF

References

Asyati, M., et al. (2022). Manajemen penyelenggaraan makanan institusi di Pondok Pesantren Baitul Arqom Polinggona, Kecamatan Polinggona, Kabupaten Kolaka tahun 2022. Jurnal Gizi dan Kesehatan Indonesia. https://doi.org/10.37887/jgki.v3i3.29260

Zadeh, M. S. A. T. (2024). AI Approaches in Personalized Meal Planning for a Multi Criteria Problem (Doctoral dissertation, North Dakota State University).

Damayanty, A. E., Nuralita, N. S., & Thristy, I. (2021). Pentingnya Nutrisi Keluarga Di Masa Pandemi Dalam Upaya Pencegahan Penyakit Covid-19. JURNAL PRODIKMAS Hasil Pengabdian Kepada Masyarakat, 6(2), 133-138.

Chaerul, M., & Zatadini, N. (2020). Pengurangan limbah makanan melalui perencanaan makan yang baik. Jurnal Ilmu Gizi, 18(3), 455–466. https://doi.org/10.14710/jil.18.3.455-466

Jakob, D., Wilhelm, S., Gerl, A., Ahrens, D., & Wahl, F. (2025). Adapting voice assistant technology for older adults: A comprehensive study on usability, learning patterns, and acceptance. Digital, 5(1), 4. https://doi.org/10.3390/digital5010004

Swamilaksita, N., et al. (2024). Visualisasi rencana makan sebagai metode edukasi untuk mengurangi limbah makanan pada ibu rumah tangga. Jurnal Pendidikan Gizi, 3(2), 133–145. https://doi.org/10.35842/ilgi.v3i2.133

Li, J., Maharjan, B., Xie, B., & Tao, C. (2020). A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation. Journal of medical Internet research, 22(9), e19897. https://doi.org/10.2196/19897

Amiri, M., Li, J., & Hasan, W. (2023). Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study. JMIR formative research, 7, e46434. https://doi.org/10.2196/46434

Jayabhaduri, R., Prabanjan, U. V., Gunashekara Reddy, M. R., Santhosh, G., & Yashas, M. R. (2025, April). AI-Driven Nutritional Intelligence: A Predictive Data Analytics Framework for Personalized Dietary Optimization. In 2024 International Conference on IT Innovation and Knowledge Discovery (ITIKD) (pp. 1-6). IEEE.

Chikwetu, L., Daily, S., Mortazavi, B. J., & Dunn, J. (2023). Automated Diet Capture Using Voice Alerts and Speech Recognition on Smartphones: Pilot Usability and Acceptability Study. JMIR formative research, 7, e46659. https://doi.org/10.2196/46659

Wahyudi, P., & Tanamal, R. (2024, September). Design and Development a Web-Based System for Recording Transactions of Contracted Service for Industrial Machine Repair at CV. Elmectro Mandiri. In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA) (pp. 1-5). IEEE.

Samad, S., Ahmed, F., Naher, S., Kabir, M. A., Das, A., Amin, S., & Islam, S. M. S. (2022). Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps. Intelligent Systems with Applications, 15, 200103.

Liang, X., Batsis, J. A., Yuan, J., Zhu, Y., Driesse, T. M., & Schultz, J. (2022, June). Voice-assisted food recall using voice assistants. In International Conference on Human-Computer Interaction (pp. 92-107). Cham: Springer Nature Switzerland.

Sezgin, E., Oiler, B., Abbott, B., Noritz, G., & Huang, Y. (2022). “Hey Siri, Help Me Take Care of My Child”: A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management. Frontiers in public health, 10, 849322.

Akturk, H. K., Snell-Bergeon, J. K., & Shah, V. N. (2021). Continuous glucose monitor with Siri integration improves glycemic control in legally blind patients with diabetes. Diabetes Technology & Therapeutics, 23(1), 81-83.

Tashildar, A., Shah, N., Gala, R., Giri, T., & Chavhan, P. (2020). Application development using flutter. International Research Journal of Modernization in Engineering Technology and Science, 2(8), 1262-1266.

Tyagi, P. (2021). Pragmatic flutter: building cross-platform mobile apps for android, iOS, web & desktop. CRC Press.

Suparman, M., Rosada, M., Lutfi, M., Kamaliya, P., Sabaniah, F., Alfaro, I., & Rosdiana, M. (2023). Mengenal aplikasi figma untuk membuat content menjadi lebih interaktif di era society 5.0. Abdi Jurnal Publikasi, 1(6), 552-555.

Lubis, N., Siambaton, M. Z., & Aulia, R. (2024). Implementasi Algoritma Deep Learning pada Aplikasi Speech to Text Online dengan Metode Recurrent Neural Network (RNN). sudo Jurnal Teknik Informatika, 3(3), 113-126.

Nugroho, K. (2019). Implementasi Sistem Speech To Text Berbasis Android Menggunakan APP Inventor Speech Recognizer. Jurnal Ilmiah Infokam, 15(1).

Thakare, U. A., Thakare, P. B., & Dhawale, V. R. (2020). Siri-The Intelligent Personal Assistant. International Research Journal of Innovations in Engineering and Technology, (1), 17.

Rohman, A. T., & Panglipury, G. S. (2024). Implementasi metode sdlc dalam transformasi desa melalui inovasi aplikasi pengaduan masyarakat berbasis android. Jurnal Informatika Dan Teknik Elektro Terapan, 12(1).

Saputra, R., Ahmadi, D., Prastiyo, R., Hermawan, R., & Maulana, A. (2022). Aplikasi Media Pembelajaran Alat Musik Gitar Berbasis Android Menggunakan Metode SDLC: indonesia. Computer Science (CO-SCIENCE), 2(2), 90-99.

DOI: https://doi.org/10.24167/sisforma.v13i1.14489