NutriSee: A Rule-Based Mobile Application for Personalized Dietary Planning Using Indonesian Food Composition Data
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Abstract
Nutritional problems in Indonesia show a concerning trend, with adult overweight and obesity prevalences reaching 14.4% and 23.4% (2023 Indonesian Health Survey). However, existing mobile nutrition applications commonly rely on static macronutrient ratios, lack transparent decision-making mechanisms, and do not incorporate localized Indonesian food composition data, limiting their practical relevance for Indonesian users. This study aims to develop NutriSee, a mobile application for determining dietary patterns based on Body Mass Index (BMI) classification. The application was built using the Rapid Application Development (RAD) method, Flutter framework, and Firebase Firestore. The system implements BMI calculation (WHO Asia-Pacific standards) alongside Basal Metabolic Rate and Total Daily Energy Expenditure calculations utilizing the Mifflin-St Jeor equation. A rule-based system generates five daily meal recommendations based on users' caloric and macronutrient targets, using the Indonesian Food Composition Table (TKPI 2020) database. Black Box Testing with Boundary Value Analysis and Equivalence Partitioning passed all 33 scenarios. User Acceptance Testing involving 30 respondents yielded "Very Good" acceptance rates of 89.33% for functionality and 84.40% for usability. These findings indicate that NutriSee functions effectively and remains accessible for practical use, enabling Indonesian adults to independently plan and manage their daily dietary intake based on localized food composition data
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Fabian Rattu, R. C. ., Lengkong, S. P. ., & Joshua, S. R. (2026). NutriSee: A Rule-Based Mobile Application for Personalized Dietary Planning Using Indonesian Food Composition Data. Journal of Intelligent Decision Support System (IDSS), 9(2), 89-98. https://doi.org/10.35335/idss.v9i2.360
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Wu, Y., Li, D., & Vermund, S. H. (2024). Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity. In International Journal of Environmental Research and Public Health (Vol. 21, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/ijerph21060757
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Aliyah, Hartono, N., & Muin, A. A. (2025). Penggunaan User Acceptance Testing (UAT) Pada Pengujian Sistem Informasi Pengelolaan Keuangan Dan Inventaris Barang. 3. https://doi.org/https://doi.org/10.62951/switch.v3i1.330
Almoraie, N. M., Saqaan, R., Alharthi, R., Alamoudi, A., Badh, L., & Shatwan, I. M. (2021). Snacking patterns throughout the life span : potential implications on health. Nutrition Research, 91, 81–94. https://doi.org/10.1016/j.nutres.2021.05.001
Bermingham, K. M., May, A., Asnicar, F., Capdevila, J., Leeming, E. R., Franks, P. W., Valdes, A. M., Wolf, J., Hadjigeorgiou, G., Delahanty, L. M., Segata, N., Spector, T. D., & Berry, S. E. (2024). Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study. European Journal of Nutrition, 63(1), 121–133. https://doi.org/10.1007/s00394-023-03241-6
Çelik Ertuğrul, D., Toygar, Ö., & Foroutan, N. (2021). A rule-based decision support system for aiding iron deficiency management. Health Informatics Journal, 27(4). https://doi.org/10.1177/14604582211066054
Dennis, A., Wixom, B. H., & Roth, R. M. (2012). Systems Analysis & Design (5th ed.).
Fauziah. (2025). Standar Makanan Rumah Sakit. In L. O. Alifariki (Ed.), Dietetika (pp. 11–24). PT Media Pustaka Indo.
Gerdes, M., Bajpai, R., Chatterjee, A., & Pahari, N. (2022). Analyze the Effect of Healthy Behavior on Weight Change and Its Conceptual Use in Digital Behavioral Intervention. https://doi.org/10.13140/RG.2.2.29226.08647
Hermansah, L., Murhadi, & Saputro, W. T. (2025). User Acceptance Testing Guna mengetahui Reseptivitas Pengguna terhadap Sistem Informasi Pelatihan Softskill. 14, 2097–2112.
Joshua, S. R., Abbas, W., Lee, J.-H., & Kim, S. K. (2023). Trust Components: An Analysis in The Development of Type 2 Diabetic Mellitus Mobile Application. 13(3). https://doi.org/10.3390/app13031251
Joshua, S. R., Shin, S., Lee, J.-H., & Kim, S. K. (2023). Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type‑2 Diabetic Mellitus Patients’ Nutrition Control. 23(3). https://doi.org/10.3390/s23031656
Kementerian Kesehatan Republik Indonesia. (2020). Tabel Komposisi Pangan Indonesia.
Kementerian Kesehatan Republik Indonesia. (2023). Survei Kesehatan Indonesia (SKI) 2023. https://www.badankebijakan.kemkes.go.id/hasil-ski-2023/
Khanna, D., Peltzer, C., Kahar, P., & Parmar, M. S. (2022). Body Mass Index (BMI): A Screening Tool Analysis. Cureus. https://doi.org/10.7759/cureus.22119
Lengkong, S. P., Palilingan, K. Y., & Josua, S. R. (2026). Web-Based Body Mass Index Application Using Asia-Pasific Classification. 15(1).
Lopez-minguez, J., Gómez-Abellán, P., & Garaulet, M. (2019). Timing of Breakfast, Lunch, and Dinner. Effects on Obesity and Metabolic Risk. 1–15.
Mahrozi, N., & Yaqin, M. A. (2024). PENGUJIAN APLIKASI DENGAN METODE BLACKBOX TESTING: ANALISIS BOUNDARY VALUE DAN EQUIVALENCE PARTITIONING PADA APLIKASI SISTEM PAKAR KUCING. 2, 257–265.
Melani, V., Ronitawati, P., Swamilakista, P. D., Sitoayu, L., Dewanti, L. P., & Hayatunnufus, F. (2022). KONSUMSI MAKAN SIANG DAN JAJANAN KAITANNYA DENGAN PRODUKTIVITAS KERJA DAN STATUS GIZI GURU.
Mifflin, M. D., St Jeor, S. T., Hill, L. A., Scott, B. J., Daugherty, S. A., & Koh, Y. O. (1990). A new predictive equation for resting energy expenditure in healthy individuals. American Journal of Clinical Nutrition, 51(2). https://doi.org/10.1093/ajcn/51.2.241
Myers, G. J., Badgett, T., & Sandler, C. (2012). The Art of Software (3rd ed.).
Pressman, R. S. (2010). Software Engineering: A Praticioner’s Approach (7th ed.).
Qorirah, S., & Rahayu, L. S. (2024). The Relationship of Energy Intake, Dinner Proportion, Sleep Duration, and Physical Activity with The Event of Overweight in Teachers at Rawamangun Muhammadiyah School. 8, 1–17. https://doi.org/10.20884/1.jgipas.2024.8.1.10508
Rizaldy, A. H., Fachrie, M., & Ikrimach. (2024). Design of an Android-Based Mobile Application for Nutritional Consultation and Status Monitoring. International Journal Software Engineering and Computer Science (IJSECS), 4(2), 808–821. https://doi.org/10.35870/ijsecs.v4i2.2701
Scarry, A., Rice, J., O’Connor, E. M., & Tierney, A. C. (2022). Usage of Mobile Applications or Mobile Health Technology to Improve Diet Quality in Adults. Nutrients, 14(12), 2437. https://doi.org/10.3390/nu14122437
Shandi, Y. J., Huda, F. R., Setiawan, D., & Felisa, J. (2021). Rekomendasi Perencanaan Menu Makan Harian bagi Penderita Diabetes Melitus dengan Metode Rule-Based. 132–151.
Swarna, R. A., Pinky, L. Y., Hussain, M. I., & Iqbal, M. (2023). A Rule-Based Expert System for Efficient Food Planning and Consumption. International Journal For Multidisciplinary Research, 5(6). https://doi.org/10.36948/ijfmr.2023.v05i06.10024
Syauqy, A., Noer, R., Fajrani, A. M., Marfu’ah Kurniawati, D., Purwanti, R., Rahadiyanti, A., & Rahma, D. E. (2020). DIETARY PATTERNS WERE ASSOCIATED WITH OBESITY PARAMETERS AMONG HEALTHY WOMEN. https://doi.org/10.14710/jnc.v9i4.28674
World Health Organization. (2025). Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
World Health Organization. (2026). Healthy diet. https://www.who.int/news-room/fact-sheets/detail/healthy-diet
Wu, X., Zhang, C., Liang, Z., Liang, Y., Li, Y., & Qiu, J. (2024). Composition , Attenuates Muscle Mass Loss , and Regulates. https://doi.org/10.3390/sports12040091
Wu, Y., Li, D., & Vermund, S. H. (2024). Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity. In International Journal of Environmental Research and Public Health (Vol. 21, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/ijerph21060757
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