Assessment of Chicken Egg Appearance Using Mamdani Fuzzy Logic with Weight and Sphericity Inputs
DOI:
https://doi.org/10.62535/sbwr9205Keywords:
Egg quality, Fuzzy logic, Mamdani method, Sphericity, WeighAbstract
This study aims to design an egg appearance assessment system using fuzzy logic with input parameters of weight and sphericity. Manual egg sorting based on physical characteristics such as size and shape is often time-consuming and subjective, necessitating an automated classification system. The Mamdani fuzzy inference system was implemented using MATLAB Fuzzy Logic Toolbox to simulate the decision-making process. Triangular and trapezoidal membership functions were used for the input variables (weight and sphericity) and output variables (appearance quality), with linguistic terms such as light, normal, heavy, and abnormal, normal, round. The rule base consisted of nine if–then statements, with the defuzzification process using the centroid method. The system produced a crisp output value of 31.67, which falls into the “good” category, indicating that eggs weighing 55 g and with a roundness of 0.85 are classified as good quality eggs. Visualization through Surface Viewer and Rule Viewer shows that this model is capable of capturing nonlinear relationships between variables and providing adaptive classification results. The results of this study indicate that Mamdani fuzzy logic can assess egg quality accurately and efficiently, and serve as a reliable basis for the development of intelligent automatic sorting systems in the poultry industry.
References
Athiyah, U., Handayani, A. P., Aldean, M. Y., Putra, N. P., & Ramadhani, R. (2021). Fuzzy Inference System: Definition, Application, and Benefits. Journal of Dinda: Data Science, Information Technology, and Data Analytics, 1(2), 73-76.
Badan Pusat Statistik. (2025, October 15). Egg production of laying hens by province (tons), 2024. Badan Pusat Statistik. https://www.bps.go.id.
Chowdhury, S., & Kar, R. (2020). Evaluation of approximate fuzzy membership function using linguistic input-an approached based on cubic spline. JINAV: Journal of Information and Visualization, 1(2), 53-59.
Dalvand, A., Abbasianjahromi, H., Ravanshadnia, M., & Zeighami, E. (2022). Designing a mamdani fuzzy inference expert system for evaluating human resources in the iranian construction industry. Interdisciplinary Journal of Management Studies, 15(4), 851-873.
Kastina, M. & Silalahi, M. (2016). Fuzzy Logic Mamdani Method in Fuzzy Production Decision System Using Matlab. Journal Ilmu Komputer. 1(2):171–181.
Kusumadewi, S., Rosita, L., & Wahyuni, E. G. (2022). Selection of aggregation function in fuzzy inference system for metabolic syndrome. Int. J. Advanced Sci. Engrg. Inform. Technol, 12(5), 2140
Lima, J. F., Patiño-León, A., Orellana, M., & Zambrano-Martinez, J. L. (2025). Evaluating the impact of membership functions and defuzzification methods in a fuzzy system: Case of air quality levels. Journal of Applied Sciences, 15(4), 1934.
Luo, B., Xuan, L., Jin, H., Shi, Y., Lai, J., Wang, R., & Zheng, J. (2025). Non-destructive measurement of egg yolk weight and percentage based on magnetic resonance imaging. Poultry Science, 104 (12) 2-11..
Mahesa, I., Putrada, A. G., & Abdurohman, M. (2019). Egg Quality Detection System Using Fuzzy Logic Method. Journal of Dinda: Data Science, Information technology, and Data Analytics. 4(3), 207-216.
Prastika, R,, Nirmala, I,, Rismawan, T. (2024). Application of Fuzzy Mamdani on Arduino-based Duck Egg Quality Classification System. Journal of Computing Engineering, System and Science, 9(1):51–63.
Rancapan, J. G. C., Arboleda, E. R., Dioses, J. L., & Dellosa, R. M. (2019). Egg Fertility Detection Using Image Processing and Fuzzy Logic. Int. J. Sci. Technol. Res, 8(10), 3228-3230.
Rizandi, F., Zulqisthi, D., Trianawati, M. L., Fathon, F. G., Ramadani, V. F., Handayani, S. N., Ramadani, V. F., Fathom, F. G., Zulqisthi, D., Larasati, D., & Assariy, M. F. (2025). Fuzzy Logic Implementation in Determining Sponge Cake Characteristics Based on Temperature and Baking Time. Journal of Applied Science, Technology & Humanities, 2(3), 453-467.
Saatchi, R. (2024). Fuzzy logic concepts, developments, and implementation. Information, 15(10), 656. https//doi.org/10.3390/info15100656
Sinaga, Y., Sirait, D. E., & Sinaga, J. A. (2023). Penerapan Metode Fuzzy Mamdani Dalam Menentukan Persediaan Beras Tahun 2023. Jurnal Ilmu Pendidikan dan Sosial, 2(3), 358-370.
Sipan, M., & Pramuyanti, R. K. (2020). Detection of Free-Range Chicken Egg Yolk Quality Using First-Order Statistical Feature Extraction Based on Fuzzy Logic. Journal of Elektrika, 12(2), 73-79.
Siregar, T. M., Anshari, F., Pratiwi, B., Pelawi, D. C., & Dameria, J. (2023). Fuzzy Set Optimization Model to Determine Optimal Selling Price of Beef. Innovative: Journal Of Social Science Research, 3(2), 3263-3275.
So, J. H., Joe, S. Y., Hwang, S. H., Hong, S. J., & Lee, S. H. (2022). Current advances in detection of abnormal egg: a review. Journal of Animal Science and Technology, 64(5), 813.
Wahyudi, A. T., Giyanti, I., & Kritiana, B. V. (2023). Studi Penentuan Jumlah Produksi Botol Kemasan Minuman Yang Optimal Dengan Fuzzy Time Series Markov Chain Dan Fuzzy Inference System. JISI: Journal of Integrasi Sistem Industri, 10(2), 99-110.
Widayat, I. W., Arsyad, A. A., Mantau, A. J., Adhitya, Y., & Köppen, M. (2024). Fuzzy Methods in Smart Farming: A Systematic Review. Journal of Informatica, 36(2), 453-489.
Widyantara, P.R.A., Dewi, G.A.M.K. and Ariana, I.N.T. (2017). The Effect of Storage Length on Quality. Journal of Ilmiah Peternakan, 20(1), 5-11.
