Web Application Using Fuzzy Logic to Assess Recommended Intake Frequency of Extruded Chiki Snacks
DOI:
https://doi.org/10.62535/nt94t493Keywords:
fuzzy logic, Mamdani inference, extruded snacksAbstract
Excessive consumption of extruded snacks, particularly Chiki products high in sugar, salt, and fat, increases the risk of obesity, hypertension, and metabolic disorders. This study aims to develop a web-based fuzzy logic application using the Mamdani inference system to determine the recommended frequency of consumption based on nutrient composition. The research employed a quantitative descriptive approach integrating three main input variables: sugar, salt, and fat, each represented by fuzzy sets of low, medium, and high levels. The output variable, consumption frequency, was classified into three linguistic terms: daily safe, moderate, and limited. A total of 27 fuzzy rules were constructed and simulated using MATLAB. The results showed that the model effectively translated quantitative nutritional data into qualitative recommendations, with higher nutrient concentrations corresponding to lower consumption frequency. The fuzzy Mamdani model provided smooth decision boundaries, demonstrating high interpretability and potential as a nutritional decision support system for consumer health guidance.
References
Adlini, M. N., Dinda, A. H., Yulinda, S., Chotimah, O., & Merliyana, S. J. (2022). Metode Penelitian Kualitatif Studi Pustaka. Jurnal Pendidikan, 6(1), 974–980.
Budiman, Wulandari, Z., & Suryati, T. (2009). Suplementasi Tepung Putih Telur untuk Memperbaiki Nilai Nutrisi Snack Ekstrusi Berbahan Grits Jagung. Media Peternakan, 32(3), 179–184.
Çunkaş, M., & Aydoğdu, O. (2010). Realization of Fuzzy Logic Controlled Brushless Dc Motor Drives Using Matlab/Simulink. Mathematical and Computational Applications, 15(2), 218–229. https://doi.org/https://doi.org/10.3390/mca15020218
Dash, K. K., & Das, S. K. (2019). Puffing Quality and Sensory Evaluation of Reduced Sodium Puffed Rice. Quality Assurance and Safety of Crops & Foods, 11(8), 757–768. https://doi.org/10.3920/QAS2018.1414
Dey, D., Gu, B.-J., Ek, P., Ross, C. F., Saunders, S. R., & Ganjyal, G. M. (2024). Sugar Inclusion Influences The Expansion Characteristics of Corn Starch Extrudates. Journal of Food Science, 89(11), 7760–7775. https://doi.org/https://doi.org/10.1111/1750-3841.17403
El-atik, A. A., Abu-gdairi, R., Nasef, A. A., Jafari, S., & Badr, M. (2023). Fuzzy Soft Sets and Decision Making in Ideal Nutrition Abdelfattah. Symmetry, 15(8), 1–10. https://doi.org/https://doi.org/10.3390/sym15081523
Fauziyyah, H., Diana, F. M., & Femelia, W. (2022). Hubungan Konsumsi Ultraprocessed Food, Kebiasaan Tidur, dan Praktik Pemesanan Makanan Online dengan Obesitas pada Orang Dewasa. JGMI : The Journal of Indonesian Community Nutrition, 11(2). https://doi.org/https://doi.org/10.30597/jgmi.v11i2.23129
Fu, H., Chi Hyun Lee, Nolden, A. A., & Kinchla, A. J. (2024). Nutrient Density, Added Sugar, and Fiber Content of Commercially Available Fruit Snacks in the United States from 2017 to 2022. Nutrients, 16(2), 292. https://doi.org/https://doi.org/10.3390/nu16020292
He, F. J., Tan, M., Ma, Y., & MacGregor, G. A. (2020). Salt Reduction to Prevent Hypertension and Cardiovascular Disease. Journal of the American College of Cardiology, 75(6), 632–647. https://doi.org/https://doi.org/10.1016/j.jacc.2019.11.055
Huang, H., Xu, H., Chen, F., Zhang, C., & Mohammadzadeh, A. (2023). An Applied Type-3 Fuzzy Logic System: Practical Matlab Simulink and M-Files for Robotic, Control, and Modeling Applications. Symmetry, 15(2), 475. https://doi.org/https://doi.org/10.3390/sym15020475
Kılınç, B., Kılınç, I., Takma, Ç. dem, & Gevrekçi, Y. (2024). Review: The Applications of Fuzzy Logic Models on Food and Marine Food Products. Journal of Food Safety and Food Quality, 75, 52–63. https://doi.org/10.31083/0003-925X-75-52
Kumar, C. S., Ali, A., & Manickavasagan, A. (2020). Health Benefits of Substituting Added Sugars with Fruits in Developing Value-Added Food Products : A Review. International Journal of Nutrition, Pharmacology, Neurological Diseases, 10(3), 75–90. https://doi.org/10.4103/ijnpnd.ijnpnd_34_20
Kurniadi, D., Nuraeni, F., & Jaelani, D. (2022). Implementasi Logika Fuzzy Mamdani Pada Sistem Prediksi Calon Penerima Program Keluarga Harapan. Jurnal Algoritma, 19(1), 160–171. https://doi.org/https://doi.org/10.33364/algoritma/v.19-1.1016
Lumanlan, J. C., Fernando, W. M. A. D. B., & Jayasena1, V. (2020). Mechanisms of Oil Uptake During Deep Frying and Applications of Predrying and Hydrocolloids in Reducing Fat Content of Chips. International Journal of Food Science and Technology, 55, 1661–1670. https://doi.org/10.1111/ijfs.14435
Monge-Rojas, R., Rulamán Vargas-Quesada, Uriyoán Colón-Ramos, & Chinnock, A. (2022). Dietary Intake and Sources of Added Sugars in Various Food. Nutrients, 14(5), 959. https://doi.org/https://doi.org/10.3390/nu14050959
Pavani, M., Singha, P., Rajamanickam, D. T., & Singh, S. K. (2023). Exploration of Foods and Foodomics Application of Fuzzy Logic Techniques for Sensory Evaluation of Plant-Based Extrudates Fortified with Bioactive Compounds. Explor Foods Foodomics., 1, 272–287. https://doi.org/https://doi.org/10.37349/eff.2023.00021
Pramudito, A. (2025). Analisis dan Simulasi Sistem Kontrol Suhu Otomatis Berbasis Fuzzy Logic Aditya. Jurnal Teknik Elektro, 14(1), 43–47. https://doi.org/https://doi.org/10.26740/jte.v14n1.p43-47
Putri, A. D., & Maulana, A. (2023). Penerapan Metode Mamdani Fuzzy Logic untuk Menentukan Pembelian Alat Berat dalam Proyek Migas di PT SMOE Indonesia. Jurnal Desain Dan Analisis Teknologi, 2(2), 138–149. https://doi.org/https://doi.org/10.58520/jddat.v2i2.32
Rahmawati, S. (2017). Perancangan Aplikasi Fuzzy Logic dalam Menentukan Volume Produksi dengan Menggunakan Metode Mamdani. Jurnal Teknologi Informasi & Pendidikan, 10(1).
Rauber, F., Chang, K., Vamos, E. P., Laura, M., Monteiro, C. A., Millett, C., & Levy, R. B. (2021). Ultra Processed Food Consumption and Risk of Obesity : A Prospective Cohort Study of UK Biobank. European Journal of Nutrition, 60(4), 2169–2180. https://doi.org/https://doi.org/10.1007/s00394-020-02367-1
Tašić, J., Nagy-perjési, Z., & Takács, M. (2024). Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes Multilevel Fuzzy Inference System for Estimating Risk. Mathematics, 12, 1167. https://doi.org/https://doi.org/10.3390/math12081167
Wang, D., Xiao, H., Lyu, X., Chen, H., & Wei, F. (2023). Lipid Oxidation in Food Science and Nutritional Health: A Comprehensive Review. Oil Crop Science, 8(1), 35–44.
Yadav, U., Singh, R. R. B., & Arora, S. (2018). Evaluation of Quality Changes in Nutritionally Enriched Extruded Snacks During Storage. Journal of Food Science and Technology, 55(10), 3939–3948. https://doi.org/https://doi.org/10.1007/s13197-018-3319-3
Yang, S., Zheng, B., Huang, L., Zhang, Y., & Zeng, H. (2024). Saltiness Perception Mechanism and Salt Reduction Strategies in Food. Trends in Food Science & Technology, 148, 104521. https://doi.org/https://doi.org/10.1016/j.tifs.2024.104521
