Main Article Content
Abstract
The rapid development of the industry has led to increasingly fierce competition among companies, driving the need for operational efficiency and maximum profit. One of the main challenges faced by companies is determining the optimal production quantity to meet market demand and manage inventory efficiently. Inaccuracies in production planning, such as excess or insufficient stock, can reduce cost efficiency and customer satisfaction. The production decision-making process is often faced with uncertainty caused by limited information and incomplete data, making traditional approaches such as statistical calculations not always effective. As a solution, the Fuzzy logic method, particularly the Sugeno method, offers a flexible approach to managing uncertainty. This method uses human logic-based rules to model the relationship between demand, inventory, and production quantity adaptively. This research aims to explore the application of the Fuzzy Sugeno method in determining the optimal production quantity based on demand and supply data. Based on the analysis of tests conducted on the production quantity calculation application using the Fuzzy Sugeno method, a truth value of 81.63% was obtained. This high truth level indicates that the implementation of the Fuzzy Sugeno method is effective in determining the production quantity.
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Copyright (c) 2025 Destaria Br Sembiring, Zulfia Memi Mayasari

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References
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[4] M. Arigo, A. Hafid, S. Berliana, and R. Zainu, “Pendekatan Fuzzy Logic dalam Rancangan Otomatisasi Penggunaan Energi Listrik pada Sistem Pendingin Udara,” J. Elkolind, vol. 11, 2024.
[5] A. I. Lubis, S. Saniman, and M. Yetri, “Sistem Kendali Lampu Ruangan Menggunakan Metode Fuzzy Logic Dan Android Berbasis Mikrokontroler,” J. Sist. Komput. Triguna Dharma (JURSIK TGD), vol. 1, no. 1, pp. 1–9, 2022, doi: 10.53513/jursik.v1i1.4800.
[6] M. R. R. Isworo, M. F. Aldama, P. D. Agnesya, and A. Puspita, “Penerapan Fuzzy Logic Menggunakan Metode Sugeno Dan Tsukamoto Untuk Mengontrol Suhu Ac,” vol. 3, pp. 117–121, 2023.
[7] R. P. Prasetya, “Implementasi Fuzzy Mamdani Pada Lampu Lalu Lintas Secara Adaptif Untuk Meminimalkan Waktu Tunggu Pengguna Jalan,” J. Mnemon., vol. 3, no. 1, pp. 24–29, 2020, doi: 10.36040/mnemonic.v3i1.2526.
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[9] S. Hajar, M. Badawi, Y. D. Setiawan, M. Noor, and H. Siregar, “200-410-1-Sm_3,” vol. 4, pp. 210–219, 2020.
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[15] M. Y. Simargolang, Y. H. Siregar, and H. S. Tamba, “Sistem Pendukung Keputusan Menggunakan Metode Fuzzy Universitas Asahan,” vol. 2, no. 2, pp. 122–128, 2018.
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