Pengembangan SandoAI Berbasis Budaya Lokal untuk Pencegahan Stunting pada Ibu Hamil
DOI:
https://doi.org/10.57218/jkj.Vol5.Iss2.2672Kata Kunci:
artificial intelligence, gizi ibu hamil, ketahanan pangan, platform digital, stuntingAbstrak
Ketahanan pangan dan pemenuhan gizi ibu hamil merupakan faktor penting dalam pencegahan stunting, terutama di Kabupaten Bima, Nusa Tenggara Barat, yang masih memiliki prevalensi stunting sebesar 29,5%. Penelitian ini bertujuan mengembangkan dan mengevaluasi efektivitas SandoAI, platform edukasi gizi berbasis kecerdasan buatan (AI) yang mengintegrasikan kearifan pangan lokal untuk meningkatkan literasi gizi dan ketahanan pangan ibu hamil. Penelitian menggunakan pendekatan Research and Development (R&D) berdasarkan model Borg & Gall. Tahapan penelitian meliputi analisis kebutuhan, pengembangan produk, validasi ahli, uji coba terbatas, revisi, dan uji efektivitas dengan desain one-group pretest-posttest pada 200 ibu hamil. Data dikumpulkan menggunakan tes literasi gizi, angket ketahanan pangan, wawancara, dan observasi, kemudian dianalisis menggunakan statistik deskriptif, uji paired t-test, dan N-Gain. Hasil validasi ahli menunjukkan kategori sangat layak (4,20), sedangkan uji coba terbatas memperoleh kategori sangat baik (4,25). Penggunaan SandoAI meningkatkan literasi gizi dan ketahanan pangan secara signifikan (p < 0,05) dengan N-Gain 0,62–0,65 (kategori sedang). SandoAI dinyatakan layak, praktis, dan efektif sebagai media edukasi gizi berbasis AI yang mengintegrasikan pangan lokal untuk mendukung pencegahan stunting.
Referensi
Al-Smadi, M. O. (2023). Examining the relationship between digital finance and financial inclusion: Evidence from MENA countries. Borsa Istanbul Review, 23(2), 464–472. https://doi.org/10.1016/j.bir.2022.11.016
Ameyaw, E. K., Amoah, P. A., & Ezezika, O. (2024). Effectiveness of mHealth apps for maternal health care delivery: Systematic review of systematic reviews. Journal of Medical Internet Research, 26, e49510. https://doi.org/10.2196/49510
Bastian, A., Parks, C., Yaroch, A., McKay, F. H., Stern, K., van der Pligt, P., McNaughton, S. A., & Lindberg, R. (2022). Factors associated with food insecurity among pregnant women and caregivers of children aged 0–6 years: A scoping review. Nutrients, 14(12), 2407. https://doi.org/10.3390/nu14122407
Beyeler, M., Légeret, C., Kiwitz, F., & Van der Horst, K. (2023). Usability and overall perception of a health bot for nutrition-related questions for patients receiving bariatric care: Mixed methods study. JMIR Human Factors, 10(1), e47913. https://doi.org/10.2196/47913
Champlin, S., Mackert, M., Glowacki, E. M., & Donovan, E. E. (2017). Toward a better understanding of patient health literacy: A focus on the skills patients need to find health information. Qualitative Health Research, 27(8), 1160–1176. https://doi.org/10.1177/1049732317695039
Chatelan, A., Clerc, A., & Fonta, P.-A. (2023). ChatGPT and future artificial intelligence chatbots: What may be the influence on credentialed nutrition and dietetics practitioners? Journal of the Academy of Nutrition and Dietetics, 123(11), 1525–1531. https://doi.org/10.1016/j.jand.2023.07.007
Clapp, J., Moseley, W. G., Burlingame, B., & Termine, P. (2022). The case for a six-dimensional food security framework. Food Policy, 106, 102164. https://doi.org/10.1016/j.foodpol.2021.102164
Dickson, A., McKay, F. H., Zinga, J., & van der Pligt, P. (2024). Antenatal healthcare providers’ knowledge, attitudes and practices regarding food insecurity in pregnancy: A qualitative investigation based at a specialist antenatal hospital in Melbourne, Australia. Journal of Human Nutrition and Dietetics, 37(1), 94–104. https://doi.org/10.1111/jhn.13231
El Bilali, H., Callenius, C., Strassner, C., & Probst, L. (2019). Food and nutrition security and sustainability transitions in food systems. Food and Energy Security, 8(2), e154. https://doi.org/10.1002/fes3.154
Guiné, R. P. F., Pato, M. L. J., Costa, C. A., Costa, D. V. T. A., Silva, P. B. C., & Martinho, V. J. P. D. (2021). Food security and sustainability: Discussing the four pillars to encompass other dimensions. Foods, 10(11), 2732. https://doi.org/10.3390/foods10112732
Haddad, S. M., Souza, R. T., Cecatti, J. G., Pacagnella, R. C., Surita, F. G., Parpinelli, M. A., ... & Brazilian Multicenter Study on Preterm Birth Study Group. (2019). Mobile technology in health (mHealth) and antenatal care—Searching for apps and available solutions: A systematic review. International Journal of Medical Informatics, 127, 1–8. https://doi.org/10.1016/j.ijmedinf.2019.04.008
Haddon, L., Silverstone, R., & Hirsch, E. (1992). Consuming technologies: Media and information in domestic spaces. Routledge.
Inaoka, K., Octawijaya, I. H., Mamahit, C. G., Karundeng, J. F., Wariki, W. M. V., & Ota, E. (2023). Effects of a comic booklet intervention aimed at preventing second-hand smoke exposure for pregnant women in Indonesia: A randomised controlled trial. Healthcare, 11(23), 3061. https://doi.org/10.3390/healthcare11233061
Jordan, J. E., Buchbinder, R., Briggs, A. M., Elsworth, G. R., Busija, L., Batterham, R., & Osborne, R. H. (2013). The health literacy management scale (HeLMS): A measure of an individual's capacity to seek, understand and use health information within the healthcare setting. Patient Education and Counseling, 91(2), 228–235. https://doi.org/10.1016/j.pec.2013.01.013
Kementerian Kesehatan Republik Indonesia. (2022). Buku saku hasil Survei Status Gizi Indonesia (SSGI) 2022. Badan Kebijakan Pembangunan Kesehatan.
Kusyanti, T., Wirakusumah, F. F., Rinawan, F. R., Muhith, A., Purbasari, A., Mawardi, F., Puspitasari, I. W., Faza, A., & Stellata, A. G. (2022). Technology-based (mHealth) and standard/traditional maternal care for pregnant women: A systematic literature review. Healthcare, 10(7), 1287. https://doi.org/10.3390/healthcare10071287
Leroy, J. L., Olney, D. K., Bliznashka, L., & Ruel, M. T. (2020). Tubaramure, a food-assisted maternal and child health and nutrition program in Burundi, increased household food security and energy and micronutrient consumption, and maternal and child dietary diversity: A cluster-randomized controlled trial. The Journal of Nutrition, 150(4), 945–957. https://doi.org/10.1093/jn/nxz304
Limketkai, B. N., Mauldin, K., Manitius, N., Jalilian, L., & Salonen, B. R. (2021). The age of artificial intelligence: Use of digital technology in clinical nutrition. Current Surgery Reports, 9(7), Article 20. https://doi.org/10.1007/s40137-021-00297-2
McKay, F. H., Spiteri, S., Zinga, J., Sulemani, K., Jacobs, S. E., Ranjan, N., Ralph, L., Raeburn, E., Threlfall, S., & Bergmeier, M. L. (2022). Systematic review of interventions addressing food insecurity in pregnant women and new mothers. Current Nutrition Reports, 11(3), 486–499. https://doi.org/10.1007/s13668-022-00420-1
Moafi, F., Kazemi, F., Samiei Siboni, F., & Alimoradi, Z. (2018). The relationship between food security and quality of life among pregnant women. BMC Pregnancy and Childbirth, 18(1), Article 319. https://doi.org/10.1186/s12884-018-1955-2
Muhammad, R. (2024). The effectiveness of technology to improve educational counseling services: A systematic literature review. Journal of Teaching and Learning, 18(2), 111–127. https://doi.org/10.22329/jtl.v18i2.8709
Nakayama, K., Yonekura, Y., Danya, H., & Hagiwara, K. (2022). Associations between health literacy and information-evaluation and decision-making skills in Japanese adults. BMC Public Health, 22(1), Article 1473. https://doi.org/10.1186/s12889-022-13825-x
Nunnery, D. L., Labban, J. D., & Dharod, J. M. (2018). Interrelationship between food security status, home availability of variety of fruits and vegetables, and their dietary intake among low-income pregnant women. Public Health Nutrition, 21(4), 807–815. https://doi.org/10.1017/S1368980017002942
Ormond, K. E., Banuvar, S., Daly, A., Iris, M., Minogue, J., & Elias, S. (2009). Information preferences of high literacy pregnant women regarding informed consent models for genetic carrier screening. Patient Education and Counseling, 75(2), 244–250. https://doi.org/10.1016/j.pec.2008.11.008
Pérez-Escamilla, R. (2017). Food security and the 2015–2030 sustainable development goals: From human to planetary health. Current Developments in Nutrition, 1(7), e000513. https://doi.org/10.3945/cdn.117.000513
Rainford, M., Barbour, L. A., Birch, D., Catalano, P., Daniels, E., Gremont, C., Marshall, N. E., Wharton, K., & Thornburg, K. L. (2024). Barriers to implementing good nutrition in pregnancy and early childhood: Creating equitable national solutions. Annals of the New York Academy of Sciences, 1534(1), 94–105. https://doi.org/10.1111/nyas.15118
Schulz, P. J., Pessina, A., Hartung, U., & Petrocchi, S. (2021). Effects of objective and subjective health literacy on patients' accurate judgment of health information and decision-making ability: Survey study. Journal of Medical Internet Research, 23(1), e20457. https://doi.org/10.2196/20457
Simelane, K. S., & Worth, S. (2020). Food and nutrition security theory. Food and Nutrition Bulletin, 41(3), 367–379. https://doi.org/10.1177/0379572120925181
Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024). Applications of artificial intelligence, machine learning, and deep learning in nutrition: A systematic review. Nutrients, 16(7), 1073. https://doi.org/10.3390/nu16071073
Tsolakidis, D., Gymnopoulos, L. P., & Dimitropoulos, K. (2024). Artificial intelligence and machine learning technologies for personalized nutrition: A review. Informatics, 11(3), 62. https://doi.org/10.3390/informatics11030062.











