Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming

Suwarno Suwarno, Anderson Arvando, Davina Davina, Brain Gantoro, Hendi Sama, Deli Deli
Pages 849-865
Abstract Views 848
PDF Downloads 957
Crossref Citations -
DOI https://doi.org/10.47738/jads.v7i2.1141

Abstract


Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.

Keywords


integer linear programming; dietary variety; chrononutrition; Harris-Benedict; nutrition recommendation

Full Text:

PDF

References


J. Chen et al., “New Insights Into The Mechanisms of High-Fat Diet Mediated Gut Microbiota in Chronic Diseases,” Feb. 01, 2023, John Wiley and Sons Inc. doi: 10.1002/imt2.69.

P. M. Kris-Etherton et al., “Strategies for Promotion of A Healthy Lifestyle in Clinical Settings: Pillars of Ideal Cardiovascular Health: A Science Advisory from the American Heart Association,” Dec. 14, 2021, Lippincott Williams and Wilkins. doi: 10.1161/CIR.0000000000001018.

M. Maltarić et al., “Adherence to the Mediterranean Diet Related to the Health Related and Well-Being Outcomes of European Mature Adults and Elderly, with an Additional Reference to Croatia,” Int J Environ Res Public Health, vol. 20, no. 6, Mar. 2023, doi: 10.3390/ijerph20064893.

B. Zhao et al., “Plant and Animal Fat Intake and Overall and Cardiovascular Disease Mortality,” JAMA Intern Med, vol. 184, no. 10, pp. 1234–1245, 2024, doi: 10.1001/jamainternmed.2024.3799.

D. Suciyanti, R. Kolopaking, A. Mustafa, S. Iwan, F. Witjaksono, and U. Fahmida, “Effect of Optimized Food-Based Recommendations on Nutrient Intakes, Hemoglobin Levels, and Memory Performance of Adolescent Girls in East Java, Indonesia,” Nutr J, vol. 24, no. 1, 2025, doi: 10.1186/s12937-024-01061-w.

E. Robinson, I. McFarland-Lesser, Z. Patel, and A. Jones, “Downsizing Food: A Systematic Review and Meta-Analysis Examining the Effect of Reducing Served Food Portion Sizes on Daily Energy Intake and Body Weight,” 2023. doi: 10.1017/S0007114522000903.

S. Lisciani et al., “Effects of Household Cooking on Mineral Composition and Retention in Widespread Italian Vegetables,” Nutrients , vol. 17, no. 3, pp. 1–16, 2025, doi: 10.3390/nu17030423.

M. A. Ali, I. A. Macdonald, and M. A. Taylor, “A Systematic Review of Associations Between Day-to-Day Variability in Meal Pattern and Body Weight, Components of the Metabolic Syndrome and Cognitive Function,” Feb. 01, 2024, John Wiley and Sons Inc. doi: 10.1111/jhn.13260.

L. A. Kaminsky, C. German, M. Imboden, C. Ozemek, J. E. Peterman, and P. H. Brubaker, “The Importance of Healthy Lifestyle Behaviors in the Prevention of Cardiovascular Disease,” Jan. 01, 2022, W.B. Saunders. doi: 10.1016/j.pcad.2021.12.001.

S. A. Nadimin, Zakaria, “The Effect of Nutrition Education Through Whatsapp to the Nutritional Knowledge of Mothers About Food Children Picky Eater,” Urban Health, vol. 2, no. 1, 2020.

U. Sholihah, Sarwanto, and Fatma Sukmawati, “Development of a Whatsapp Chatbot as a Nutrition Education Media for Mothers of Toddlers,” Attanwir : Jurnal Kajian Keislaman dan Pendidikan, vol. 15, no. 1, pp. 80–101, Mar. 2024.

Y. Christian et al., “Perancangan Chatbot Akreditasi UIB Untuk Kemudahan Akreditasi Menggunakan Metode Natural Language Processing,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 7, no. 1, pp. 233–238, Feb. 2025, doi: 10.47233/jteksis.v7i1.1849.

A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI,” Information Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/j.inffus.2019.12.012.

I. H. Sarker, “AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems,” 2022. doi: 10.1007/s42979-022-01043-x.

M. Côté and B. Lamarche, “Artificial Intelligence in Nutrition Research: Perspectives on Current and Future Applications,” 2022, Canadian Science Publishing. doi: 10.1139/apnm-2021-0448.

V. Knights, M. Kolak, G. Markovikj, and J. Gajdoš Kljusurić, “Modeling and Optimization with Artificial Intelligence in Nutrition,” Applied Sciences (Switzerland), vol. 13, no. 13, Jul. 2023, doi: 10.3390/app13137835.

Suwarno, Y. Christian, K. Yoputra, and Y. Estrada, “Exploring Machine Learning to Support Software Managers’ Insights on Software Developer Productivity,” Journal of Applied Data Sciences, vol. 6, no. 3, pp. 1504–1524, Sep. 2025, doi: 10.47738/jads.v6i3.661.

M. S. S. Putri, H. Mawengkang, S. Suwilo, and Tulus, “A Data-Driven Mixed Integer Nonlinear Programming Model for Cost-Optimal Scheduling of Perishable Production and Workforce,” Journal of Applied Data Sciences, vol. 6, no. 4, pp. 2449–2460, Dec. 2025, doi: 10.47738/jads.v6i4.1019.

N. Azmi, D. Richasdy, and Hasmawati, “Recommendation System in the Form of an Ontology-Based Chatbot for Healthy Food Recommendations for Teenagers,” Jurnal Penelitian Pendidikan IPA, vol. 9, no. 7, pp. 5085–5091, Jul. 2023, doi: 10.29303/jppipa.v9i7.4401.

J. C. Gerdessen and K. J. Borgonjen-van den Berg, “A Linear Programming Based Method for Designing Menus for Controlled Feeding Trials,” American Journal of Clinical Nutrition, vol. 117, no. 2, 2023, doi: 10.1016/j.ajcnut.2022.11.006.

R. Lopes, R. Alves, A. Ledent, R. L. T. Santos, and M. Kloft, “Recommendations with Minimum Exposure Guarantees: A Post-Processing Framework,” Expert Syst Appl, vol. 236, 2024, doi: 10.1016/j.eswa.2023.121164.

L. de Carvalho Souza, M. E., Weigang, “Grok, Gemini, ChatGPT and Deepseek: Comparison and Applications in Conversational Artificial Intelligence.,” Inteligencia Artificial, vol. 2, no. 1, pp. 1–7, 2025, doi: 10.5281/zenodo.14885243.

W. J. Waldock, J. Zhang, A. Guni, A. Nabeel, A. Darzi, and H. Ashrafian, “The Accuracy and Capability of Artificial Intelligence Solutions in Health Care Examinations and Certificates: Systematic Review and Meta-Analysis,” J Med Internet Res, vol. 26, 2024, doi: 10.2196/56532.

RS Awal Bros, “Brosur Daftar Bahan Makanan Penukar.” Accessed: Oct. 04, 2025. [Online]. Available: https://drive.google.com/file/d/1GLuuncro_qsZ7_yHLTOhh2-UsB64HsKr/view?usp=drive_link

I. Bendavid et al., “The Centenary of The Harris–Benedict Equations: How to Assess Energy Requirements Best? Recommendations from The ESPEN Expert Group,” 2021. doi: 10.1016/j.clnu.2020.11.012.

N. H. Phelps et al., “Worldwide Trends in Underweight and Obesity from 1990 to 2022: A Pooled Analysis of 3663 Population-Representative Studies with 222 Million Children, Adolescents, and Adults,” The Lancet, vol. 403, no. 10431, 2024, doi: 10.1016/S0140-6736(23)02750-2.

M. de L. Macena et al., “Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations in Adults with Overweight and Obesity: A Systematic Review with Meta-Analysis,” Nutr Rev, vol. 80, no. 11, pp. 2113–2135, Nov. 2022, doi: 10.1093/nutrit/nuac031.

A. M. Aas et al., “Evidence-based European Recommendations for The Dietary Management of Diabetes,” Diabetologia, vol. 66, no. 6, 2023, doi: 10.1007/s00125-023-05894-8.

Harvard Health Publishing, “How Much Protein do You Need Every Day?” Accessed: Aug. 27, 2025. [Online]. Available: https://www.health.harvard.edu/blog/how-much-protein-do-you-need-every-day-201506188096

Mayo Clinic Staff, “Carbohydrates: How Carbs Fit Into a Healthy Diet.” Accessed: Oct. 05, 2025. [Online]. Available: https://www.mayoclinic.org/healthy-lifestyle/nutrition-and-healthy-eating/in-depth/carbohydrates/art-20045705

I. Vasquez, “Understanding Your Daily Fat Needs for Healthier Living.” Accessed: Oct. 05, 2025. [Online]. Available: https://www.health.com/how-many-grams-of-fat-per-day-8575275

K. Murakami et al., “Characterisation of Breakfast, Lunch, Dinner and Snacks in The Japanese Context: An Exploratory Cross-Sectional Analysis,” Public Health Nutr, vol. 25, no. 3, 2022, doi: 10.1017/S1368980020004310.

A. Jayedi et al., “Dose-Dependent Effect of Carbohydrate Restriction For Type 2 Diabetes Management: A Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials,” American Journal of Clinical Nutrition, vol. 116, no. 1, 2022, doi: 10.1093/ajcn/nqac066.

C. Cornejo, “How Much Fat Can People with Diabetes Have Each Day?” Accessed: Aug. 27, 2025. [Online]. Available: https://www.healthline.com/health/diabetes/how-much-fat-can-a-diabetic-have-a-day

L. Schwingshackl et al., “A Scoping Review of Current Guidelines on Dietary Fat and Fat Quality,” Aug. 01, 2021, S. Karger AG. doi: 10.1159/000515671.

Kementerian Kesehatan Republik Indonesia, “Isi Piringku, Panduan Kebutuhan Gizi Seimbang Harian.” Accessed: Oct. 11, 2025. [Online]. Available: https://ayosehat.kemkes.go.id/isi-piringku-kebutuhan-gizi-harian-seimbang?utm_source=chatgpt.com

K. Adolphus, A. Hoyland, J. Walton, F. Quadt, C. L. Lawton, and L. Dye, “Ready-to-Eat Cereal and Milk for Breakfast Compared with No Breakfast Has a Positive Acute Effect on Cognitive Function and Subjective State in 11–13-Year-Olds: a School-Based, Randomised, Controlled, Parallel Groups Trial,” Eur J Nutr, vol. 60, no. 6, 2021, doi: 10.1007/s00394-021-02506-2.

OpenAI, “ChatGPT (GPT-5) [Large language model].” Accessed: Oct. 13, 2025. [Online]. Available: https://chat.openai.com

Google Gemini, “Gemini 2.5 Flash [Large language model].” Accessed: Oct. 13, 2025. [Online]. Available: https://gemini.google.com

DeepSeek, “DeepSeek V3.2 [Large language model].” Accessed: Oct. 13, 2025. [Online]. Available: https://chat.deepseek.com/

C. Nangin, “Nutrition-IndonesianFoodDataset.” Accessed: Sep. 22, 2025. [Online]. Available: https://www.kaggle.com/datasets/christiannangin/nutrition-indonesianfooddataset

nichsedge, “panganku-scraper.” Accessed: Sep. 22, 2025. [Online]. Available: https://github.com/nichsedge/panganku-scraper?utm_source=chatgpt.com

P. Jackson et al., “Artificial Intelligence in Medical Education - Perception Among Medical Students,” BMC Med Educ, vol. 24, no. 1, pp. 1–9, 2024, doi: 10.1186/s12909-024-05760-0.




DOI: https://doi.org/10.47738/jads.v7i2.1141

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0