Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management


  • Widi Sayyd Fadhil Muhammad School of computing, Telkom University Bandung, Indonesia
  • Z. K. A Baizal Telkom University
  • Ramanti Dharayani School of computing, Telkom University Bandung, Indonesia




Chatbot, Ontology, Recommender System, SWRL


In Indonesia, obesity is a serious health issue, and rates have risen recently because of sedentary lifestyles and poor eating practices. We suggest a proactive self-care suggestion system specifically created for Indonesians who are dealing with obesity to address this problem. Our recommender system attempts to give customers individualized suggestions for healthy lifestyle modifications that will make it easier for them to manage their weight. Because social media is so widely used in Indonesia, we created our system as a Telegram Chatbot. Our system may provide personalized suggestions based on a particular gender, activity level, fat mass, and difficulty of exercise that are relevant to Indonesians by fusing the user's ontological profile with generic clinical guidelines and standards for the management of obesity. Ontologies with Semantic Web Rule Language (SWRL) were used in the development of our system since SWRL ontologies are thought to perform better. Evaluations carried out using case studies and expert verification illustrate the usefulness of our suggested method, and the validated result of 88.8 percent demonstrates that our system can deliver good suggestion results for the user.


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Author Biographies

Widi Sayyd Fadhil Muhammad, School of computing, Telkom University Bandung, Indonesia



Ramanti Dharayani, School of computing, Telkom University Bandung, Indonesia




Aditya, N., Baizal, Z. K. A., & Dharayani, R. (2023). Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language. Building of Informatics, Technology and Science (BITS), 4(4). https://doi.org/10.47065/bits.v4i4.3005

Alian, S., Li, J., & Pandey, V. (2018). A Personalized Recommendation System to Support Diabetes Self- Management for American Indians. IEEE Access, 6, 73041–73051. https://doi.org/10.1109/ACCESS.2018.2882138

Basnayake, C., Peiris, C., Wickramarathna, H., & Jayathunga, P. (2021). Recommender System based on Food and Exercise Ontologies to Find the Suitable Fitness Exercise Plan with the Aid of Python. 2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI), 1–6. IEEE. https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664742

Casari, S., Di Paola, M., Banci, E., Diallo, S., Scarallo, L., Renzo, S., … Lionetti, P. (2022). Changing Dietary Habits: The Impact of Urbanization and Rising Socio-Economic Status in Families from Burkina Faso in Sub-Saharan Africa. Nutrients, 14(9). https://doi.org/10.3390/nu14091782

Chatterjee, A., & Prinz, A. (2022). Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling. JMIR Medical Informatics, 10(6). https://doi.org/10.2196/33847

Chatterjee, A., Prinz, A., Gerdes, M., & Martinez, S. (2021). An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study. Journal of Medical Internet Research, 23(4), e24656. https://doi.org/10.2196/24656

Chi, Y.-L., Chen, T.-Y., & Tsai, W.-T. (2015). A chronic disease dietary consultation system using OWL-based ontologies and semantic rules. Journal of Biomedical Informatics, 53, 208–219. https://doi.org/10.1016/j.jbi.2014.11.001

Dewi, N. U., Tanziha, I., Solechah, S. A., & Bohari. (2020). Obesity determinants and the policy implications for the prevention and management of obesity in Indonesia. Current Research in Nutrition and Food Science, 8(3), 942–955. https://doi.org/10.12944/CRNFSJ.8.3.22

FactSheet_Obesitas_Kit_Informasi_Obesitas. (n.d.). Freeletics. (n.d.).

Hassan, S. M., Malek, N. F. A., Khan, T. K. A., Ishak, A., Hashim, H. A., & Chen, C. K. (2022). THE EFFECT OF 12-WEEK CALISTHENICS EXERCISE ON PHYSICAL FITNESS AMONG OBESE FEMALE STUDENTS. Physical Education Theory and Methodology, 22(3), S45–S50. https://doi.org/10.17309/tmfv.2022.3s.06

HHS. (n.d.). Physical Activity Guidelines for Americans 2 nd edition.

Jean-Baptiste, L. (2021). Ontologies with Python. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842- 6552-9

Kim, H.-Y., Park, H.-A., Min, Y. H., & Jeon, E. (2013). Development of an Obesity Management Ontology Based on the Nursing Process for the Mobile-Device Domain. Journal of Medical Internet Research, 15(7), e130. https://doi.org/10.2196/jmir.2512

MadMuscles. (n.d.).

Nam, Y., & Kim, Y. (2015). Individualized exercise and diet recommendations: An expert system for monitoring physical activity and lifestyle interventions in obesity. Journal of Electrical Engineering and Technology, 10(6), 2434–2441. https://doi.org/10.5370/JEET.2015.10.6.2434

Petridou, A., Siopi, A., & Mougios, V. (2019, March 1). Exercise in the management of obesity. Metabolism: Clinical and Experimental, Vol. 92, pp. 163–169. W.B. Saunders. https://doi.org/10.1016/j.metabol.2018.10.009

Prasetyo, C., & Istiono, W. (2021). Fitness Exercise Recommendation System Using Weighted Products. International Journal of Emerging Trends in Engineering Research, 9(9), 1234–1238. https://doi.org/10.30534/ijeter/2021/05992021

Rachmi, C. N., Li, M., & Alison Baur, L. (2017, June 1). Overweight and obesity in Indonesia: prevalence and risk factors—a literature review. Public Health, Vol. 147, pp. 20–29. Elsevier B.V. https://doi.org/10.1016/j.puhe.2017.02.002

Sojic, A., Terkaj, W., Contini, G., & Sacco, M. (2016). Modularising ontology and designing inference patterns to personalise health condition assessment: the case of obesity. Journal of Biomedical Semantics, 7(1), 12. https://doi.org/10.1186/s13326-016-0049-1

Susilo, M. T., Kurnia, A. R., Handayani, O. W. K., Rahayu, S. R., Fauzi, L., Irawan, F. A., … Chiao Yu, Y. (2022). Obesity in Indonesian and Taiwanese Adolescents Related to Self Perception, Diet, Exercise, and Body Image. Jurnal Kesehatan Masyarakat, 17(3), 453–461. https://doi.org/10.15294/kemas.v17i3.34396

Taçyıldız, Ö., & Çelik Ertuğrul, D. (2020). A decision support system on the obesity management and consultation during childhood and adolescence using ontology and semantic rules. Journal of Biomedical Informatics, 110. https://doi.org/10.1016/j.jbi.2020.103554

Widya Hapsari, P. (n.d.). The Application of UNICEF’S 2020 Conceptual Framework of Maternal and Child Nutrition in Indonesia. Retrieved from https://www.researchgate.net/publication/349161215

Woolcott, O. O., & Bergman, R. N. (2019). Relative Fat Mass as an estimator of whole-body fat percentage among children and adolescents: A cross-sectional study using NHANES. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-51701-z

Woolcott, O. O., & Bergman, R. N. (2020). Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014. International Journal of Obesity, 44(6), 1301–1310. https://doi.org/10.1038/s41366-019-0516-8

Zhang, Y., Guo, Q., Gao, X., Yang, J., Ye, J., Wang, W., … Zeng, Q. (2018). Exercise and Dietary Intervention in the Management of Overweight and Obese People. Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018, 804–807. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ITME.2018.00181


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How to Cite

Muhammad, W. S. F. ., Baizal, Z. K. A., & Dharayani, R. . (2023). Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1699-1708. https://doi.org/10.33395/sinkron.v8i3.12689