Usability Evaluation for Mobile-Based Nutritional Food Recommender System
DOI:
https://doi.org/10.25126/jitecs.202381385Abstract
COVID-19 was designated as a global pandemic by WHO and Government Regulation of the Republic of Indonesia Number 21 of 2020 concerning Large-Scale Social Restrictions for the handling of Corona Virus Disease 2019 (COVID-19). Eating healthy foods with balanced nutrition can increase the body's immunity during a COVID-19 pandemic. Since many foods that sold freely are not guaranteed in nutritional ingredients, cooking becomes a better alternative. system is a mobile application that provides a recommendation of healthy food recipe by using the Simple Additive Weighting (SAW) algorithm, which has been studied in the previous research of recipe recommendation and gives significant result. Previous research has not examined the appropriate variables and the data are still general food. We conducted a user study to measure the usability of the proposed system. The result of usability testing shows that there is a significant increase in SUS score from 63.5 to 87.
References
Hammami, Amri, et al. "Physical activity and coronavirus disease 2019 (COVID-19): specific recommendations for home-based physical training." Managing Sport and Leisure (2020): 1-6.
Tiksnadi, B. B., Sylviana, N., Cahyadi, A. I. & Undarsa, A. C., 2020. Olahraga Rutin untuk Meningkatkan Imunitas Pasien Hipertensi Selama Masa Pandemi COVID-19. Indonesian Journal of Cardiology, 41(2), pp. 112-119.
Rahayu, N. & Munastiwi, E., 2018. Healthy Food Management in PAUD. Golden Age Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini, 3(2), pp. 65-80.
Hamm, Michael W. "Principles for framing a healthy food system." Journal of hunger & environmental nutrition 4.3-4 (2009): 241-250.
Sari, R., Tursina & Sukamto, A. S., 2019. Pemilihan Resep Masakan Berdasarkan Ketersediaan Bahan Masakan dengan Metode Simple Matching Coefficient (SMC). Jurnal Edukasi dan Penelitian Informatika, 5(1), pp. 32-29.
Ge, Mouzhi, Francesco Ricci, and David Massimo. "Health-aware food recommender system." Proceedings of the 9th ACM Conference on Recommender Systems. 2015.
Ge, Mouzhi, et al. "Using tags and latent factors in a food recommender system." Proceedings of the 5th International Conference on Digital Health 2015. 2015.
Elahi, Mehdi, et al. "Interaction design in a mobile food recommender system." IntRS 2015 Interfaces and Human Decision Making for Recommender Systems: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2015). Vol. 1438. CEUR-WS, 2015.
Leipold, Nadja, et al. "Nutrilize a Personalized Nutrition Recommender System: an Enable Study." HealthRecSys@ RecSys 2216 (2018): 24-29.
Pecune, Florian, Lucile Callebert, and Stacy Marsella. "A Recommender System for Healthy and Personalized Recipes Recommendations." HealthRecSys@ RecSys. 2020.
De Pessemier, Toon, Simon Dooms, and Luc Martens. "Design and evaluation of a group recommender system." Proceedings of the sixth ACM conference on Recommender systems. 2012.
Frieyadie. Application of the Simple Additive Weight (SAW) Method in Promotional Promotion Decision Support Systems. Jurnal Pilar Nusa Mandiri , 7(1), pp. 37 - 45. 2016.
Yoon, K. Paul, and Ching-Lai Hwang. Multiple attribute decision making: an introduction. Vol. 104. Sage publications, 1995.
Sahir, Syafrida Hafni, R. Rosmawati, and Kresna Minan. "Simple additive weighting method to determining employee salary increase rate." Int. J. Sci. Res. Sci. Technol 3.8 (2017): 42-48.
Adela, Hana, et al. "Selection of dancer member using simple additive weighting." International Journal of Engineering & Technology 7.3 (2018): 1096-1107.
Nurmalini, N., and Robbi Rahim. "Study Approach of Simple Additive Weighting For Decision Support System." Int. J. Sci. Res. Sci. Technol 3.3 (2017): 541-544.
Setiawan, Nashrudin, et al. "Simple additive weighting as decision support system for determining employees salary." Int. J. Eng. Technol 7.2.14 (2018): 309-313.
Dewi, R. K., Brata, K. C. & Nabila, N., 2019. Konsistensi Ranking pada Sistem Rekomendasi Resep Masakan dengan Simple Additive Weighting. JNTETI, 8(3), pp. 235 - 240.
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189(194), 4-7.
Gutiérrez-Carreón, G., Daradoumis, T., and Jorba, J. (2015). Integrating learning services in the cloud: An approach that benefits both systems and learning. Journal of Educational Technology & Society, 18(1), 145-157.
Nielsen, Jacobs. How Many Test Users in a Usability Study? Article can be accessed in https://www.nngroup.com/articles/how-many-test-users/ at August, 9th 2022.
Bangor, Aaron, Philip Kortum, and James Miller. "Determining what individual SUS scores mean: Adding an adjective rating scale." Journal of usability studies 4.3 (2009): 114-123.
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
