Penetapan Kadar Formalin Pada Mie Basah Yang Beredar Di Pasar Peunayong Kota Banda Aceh Secara Spektrofotometri Visible

Syarifah Nadia; Debi Meilani; Dea Anggreini; Siti Rahmi Ningrum; Supran Hidayat Sihotang; Ernawaty Ginting

doi:10.55606/jurrike.v4i2.6296

Authors

Syarifah Nadia Universitas Tjut Nyak Dhien
Debi Meilani Insitut Kesehatan Medistra
Dea Anggreini Universitas Tjut Nyak Dhien
Siti Rahmi Ningrum Universitas Tjut Nyak Dhien
Supran Hidayat Sihotang Universitas Tjut Nyak Dhien
Ernawaty Ginting Universitas Tjut Nyak Dhien

DOI:

https://doi.org/10.55606/jurrike.v4i2.6296

Keywords:

formalin, food safety, spectrophotometry, visible method, wet noodles

Abstract

Formalin is a 37% formaldehyde solution in water, commonly misused as a food preservative despite its toxicity and prohibition in food products. This study aimed to determine the presence and levels of formalin in wet noodles sold at Peunayong Market, Banda Aceh, using visible spectrophotometry. A descriptive design was applied to three randomly selected samples. Qualitative tests using multiple reagents (Schiff, Schryver, KMnO₄, and a test kit) showed negative results for formalin presence. However, quantitative analysis with Spectroquant Prove 300 revealed that all samples contained formalin levels exceeding the acceptable standard. The concentrations ranged from 0.7433 mg/L to 0.8766 mg/L, indicating that the products are unsafe for consumption. These findings underline the need for stricter monitoring and improved food safety awareness among producers and consumers in traditional markets.

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References

Ahmed, S., & Patel, K. (2022). Machine learning–based decision support in healthcare: Current trends and challenges. Journal of Healthcare Engineering, 2022, Article 7596174. https://doi.org/10.1155/2022/7596174

Baker, M. P., & Jones, D. N. (2023). The role of artificial intelligence in clinical decision support: A meta‐review. Artificial Intelligence in Medicine, 136, 102415. https://doi.org/10.1016/j.artmed.2023.102415

Choi, J. H., & Park, E. H. (2024). Human factors in hospital information systems: A socio-technical analysis. Applied Ergonomics, 106, 103800. https://doi.org/10.1016/j.apergo.2024.103800

Ewoh, A. I. E., & Vartiainen, M. (2024). Cybersecurity vulnerabilities in digital health systems: A socio-technical review. International Journal of Medical Informatics, 165, 105421. https://doi.org/10.1016/j.ijmedinf.2023.105421

Ferreira, F., & Silva, T. (2020). Evaluating the impact of dashboard-driven analytics on hospital management. Health Systems, 9(2), 110–123. https://doi.org/10.1080/20476965.2020.1763815

Grechuta, K., Adebayo, O., & Zhao, L. (2024). Impact of integrated CDSS on medication error reduction in tertiary hospitals. Health Informatics Journal, 30(2), 1098–1110. https://doi.org/10.1177/1460458223123456

Huang, M., Lee, C. H., & Wang, J. (2021). Assessing user acceptance of hospital information systems based on the technology acceptance model. Computers in Biology and Medicine, 134, 104460. https://doi.org/10.1016/j.compbiomed.2021.104460

Johnson, M. E., & Robinson, T. (2023). Quality of care and clinical decision support: A systematic exploration. Systematic Reviews, 12, 220. https://doi.org/10.1186/s13643-023-02203-4

Kumar, S., & Bhatt, R. (2023). Change management strategies in digital health transformation: A case study of Indian hospitals. International Journal of Healthcare Management, 16(4), 1233–1245. https://doi.org/10.1080/20479700.2023.1234567

Lee, K., & Park, J. (2021). Adoption of predictive decision support systems in clinical settings: A systematic scoping review. Computers in Biology and Medicine, 136, 104678. https://doi.org/10.1016/j.compbiomed.2021.104678

Lee, S. Y., & McCullough, J. S. (2020). Physicians’ experiences with electronic medical records: Implications for the adoption of health information technology. Health Services Research, 55(S1), 96–107. https://doi.org/10.1111/1475-6773.13552

Miller, A. J., Smith, L., & Garcia, R. (2021). Evaluating predictive analytics for patient flow management in acute care hospitals. Journal of Medical Systems, 45(7), 112. https://doi.org/10.1007/s10916-021-01739-2

Nguyen, P. Q., Tran, V. T., & Le, D. N. (2022). Implementing dashboard analytics in hospital management: Benefits and challenges. BMC Medical Informatics and Decision Making, 22(1), 167. https://doi.org/10.1186/s12911-022-01945-8

Putteeraj, V., Bhungee, C., Somanah, R., & Moty, D. (2021). Diffusion of innovation in e-health adoption: A study of Mauritian hospitals. BMC Medical Informatics and Decision Making, 21, 45. https://doi.org/10.1186/s12911-021-01345-2

Rodrigues, J. N., Silva, P. L., & Almeida, R. S. (2022). Real-time analytics for reducing patient wait times: A systematic evaluation. Journal of Medical Systems, 46(2), 34. https://doi.org/10.1007/s10916-021-01823-z

Saba, V. K., & McCormick, K. A. (2020). Essentials of computerized provider order entry (2nd ed.). Springer.

Tan, Y., & Chen, Z. (2021). Predictive analytics in hospital resource allocation: A review. International Journal of Medical Informatics, 152, 104447. https://doi.org/10.1016/j.ijmedinf.2021.104447

Wang, X., & Li, Y. (2023). User-friendly dashboards for hospital performance monitoring: Design principles and evaluation. Journal of Biomedical Informatics, 137, 104207. https://doi.org/10.1016/j.jbi.2023.104207

Wati, N., Santoso, H., & Cahyono, B. (2025). Perceived usefulness and risk in hospital information system adoption: A structural equation model. Journal of Healthcare Information Management, 39(2), 78–92. https://doi.org/10.1007/s10916-025-01850-1

Zhang, L., & Liu, Y. (2020). Integrating clinical decision support into electronic health records: A comprehensive review. Journal of the American Medical Informatics Association, 27(12), 1874–1884. https://doi.org/10.1093/jamia/ocaa236