Wet Seaweed Inventory Using the Fuzzy Mamdani Method in the Food Industry
Persediaan Rumput Laut Basah Menggunakan Metode Fuzzy Mamdani pada Industri Makanan
DOI:
https://doi.org/10.21070/ups.9535Keywords:
Decision Support System, Inventory Control, , Food Industry, Fuzzy LogicAbstract
In the digital era, accurate decision-making is vital, particularly in inventory management to balance production and market demand. Problems like overstock (15%) and shortage (4%) often occur due to unpredictable demand. Hence, reliable control and forecasting systems are essential. This study employs the Mamdani Fuzzy Logic approach to manage uncertainty and enhance intelligent inventory management. The goal is to analyze raw material inventory, evaluate shortage risks, and provide data-driven recommendations for efficiency improvement. Mamdani Fuzzy Logic is selected for its strength in processing uncertain data and generating intelligent control systems capable of precise assessments. The research findings indicate an inventory prediction of 524 tons for 26 tons of production and 28 tons of demand in October, with a MAPE of 41.475% (“fair” accuracy). Overall, the Mamdani Fuzzy Logic method effectively supports decision-making in inventory management.
Downloads
References
D. Rifai and F. Fitriyadi, “Penerapan Logika Fuzzy Sugeno dalam Keputusan Jumlah Produksi Berbasis Website,” Hello World J. Ilmu Komput., vol. 2, no. 2, pp. 102–109, 2023, doi: 10.56211/helloworld.v2i2.297.
Herdy Alfiansyah, Zeny Fatimah Hunusalela, and Theresia Evy Yulianty Nadeak, “Pengoptimalan Persediaan Bahan Baku Natur E DN Revitalizing dengan Metode Fuzzy Mamdani dan Algoritma Within Wagner pada PT Darya Varia Laboratoria,” J. Tek. Ind., vol. 13, no. 2, pp. 152–158, 2023, doi: 10.25105/jti.v13i2.17557.
J. A. P. de Oliveira, P. Wanke, J. Antunes, and Y. Tan, “Unveiling the impact of information vagueness on carbon emission inventories using fuzzy sets,” Energy Econ., vol. 148, no. March 2024, p. 108672, 2025, doi: 10.1016/j.eneco.2025.108672.
S. Sukardi, A. Prasetyo, and A. Nugroho, “Analisis Kebutuhan Tenaga Kerja Menggunakan Metode Time and Motion Study pada Industri Makanan Ringan,” J. Tek. Ind., vol. 21, no. 1, pp. 45–52, 2020.
I. Dwi Antoni and Y. Findawati, “Implementasi Logika Fuzzy Untuk Menentukan Jumlah Produksi Roti Menggunakan Metode Tsukamoto,” Smatika J., vol. 14, no. 01, pp. 61–70, 2024, doi: 10.32664/smatika.v14i01.1168.
A. Saputra, S. Rahayu, and H. Windyatri, “Analisis Peningkatan Overall Equipment Effectiveness ( OEE ) Mesin Surface Mount Technology ( SMT ) Melalui Failure Mode And Effect Analysis ( FMEA ) ( Studi Kasus : PT . Sharp Eleketronik Indonesia ) Pendahuluan Metode Penelitian,” vol. 4, no. 3, pp. 537–547, 2025.
S. Agrawal, B. K. Patle, and S. Sanap, “Navigation control of unmanned aerial vehicles in dynamic collaborative indoor environment using probability fuzzy logic approach,” Cogn. Robot., vol. 5, no. March, pp. 86–113, 2025, doi: 10.1016/j.cogr.2025.02.002.
M. Esmaeili, A. Anjomshoae, N. Shahsavari-Pour, P. Srisurin, and R. Banomyong, “An optimization similarity fuzzy inference method for traffic signal control at an isolated intersection,” Multimodal Transp., vol. 4, no. 4, p. 100234, 2025, doi: 10.1016/j.multra.2025.100234.
H. C. Alam and N. Baldah, “Evaluasi Strategi Pengendalian Persediaan Buah Menggunakan Min-Max dan SWOT Analysis,” J. Teknol. dan Manaj. Ind. Terap., vol. 4, no. 2, pp. 457–465, 2025, doi: 10.55826/jtmit.v4i2.678.
M. Rofih, S. Rahayu, and T. N. Wiyatno, “Postural Risk Analysis Based on Rapid Upper Limb Assessment ( RULA ) Method for Manual Handling Workers in the Food Industry,” vol. 4, no. 3, pp. 652–660, 2025.
V. Kumara and E. Ganesan, “A novel approach to wastewater treatment control: a self-organizing fuzzy sliding mode controller,” IAES Int. J. Artif. Intell., vol. 13, no. 3, pp. 2796–2807, 2024, doi: 10.11591/ijai.v13.i3.pp2796-2807.
Fauzan Ahmad, N. Syauqi B N, D. Kurniawan, and T. A. Pamungkas, “Analisis Pemilihan Supplier Menggunakan Metode Analytical Hierarchy Process (AHP) pada Industri Ritel Sepatu,” J. Teknol. dan Manaj. Ind. Terap., vol. 4, no. I, pp. 45–51, 2025, doi: 10.55826/jtmit.v4ii.469.
R. K. Pratama, A. A. Karim, and C. D. P. Hertadi, “Perencanaan Stok Pengaman dan Titik Pemesanan Ulang dengan Metode Time Series pada Perusahaan Furniture Di Kalimantan,” J. Teknol. dan Manaj. Ind. Terap., vol. 2, no. 3, pp. 200–211, 2023, doi: 10.55826/tmit.v2i3.256.
Wahyu Syaputra, N. Fakhri G, S. R. Ardian, and A. J. Nugroho, “Integrasi Metode FMEA Dan FTA Dalam Analisis Risiko Keselamatan Dan Kesehatan Kerja Di Bengkel Bubut,” J. Teknol. dan Manaj. Ind. Terap., vol. 3, no. I, pp. 47–56, 2024, doi: 10.55826/tmit.v3ii.254.
E. Bottani, M. Di Nardo, L. Monferdini, and T. Murino, “Mapping LARGS criteria and relationships for supplier selection using a fuzzy hybrid approach,” Comput. Ind. Eng., vol. 206, no. May, 2025, doi: 10.1016/j.cie.2025.111252.
S. Sarbaini, D. Yanti, and Nazaruddin, “Prediksi Harga Beras Belida Di Kota Pekanbaru Menggunakan Fuzzy Time Series Cheng,” J. Teknol. dan Manaj. Ind. Terap., vol. 2, no. 3, pp. 234–241, 2023, doi: 10.55826/tmit.v2i3.183.
J. Khan et al., “Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system,” Alexandria Eng. J., vol. 119, no. February, pp. 598–608, 2025, doi: 10.1016/j.aej.2025.01.116.
K. Pujaru, S. Adak, T. K. Kar, S. Patra, and S. Jana, “A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production,” Decis. Anal. J., vol. 11, no. May, p. 100481, 2024, doi: 10.1016/j.dajour.2024.100481.
E. Khalil and M. Akter, “Prediction of seam strength of cotton canvas fabric using fuzzy logic,” Results Control Optim., vol. 17, no. October, p. 100502, 2024, doi: 10.1016/j.rico.2024.100502.
M. R. Lumban Batu, “Analisis Pengendalian Stock Untuk Menentukan Efektivitas Biaya Menggunakan Metode Aktual, Eoq, Poq, Dan Min-Max,” Primanomics J. Ekon. Bisnis, vol. 21, no. 1, pp. 102–111, 2023, doi: 10.31253/pe.v21i1.1766.
W. Kim, J. Lee, J. Shim, S. Cho, and H. Cho, “Fuzzy logic-based filtering for defect detection in automated X-ray inspection during the production of lithium-ion battery pouch cells,” Nucl. Eng. Technol., vol. 57, no. 10, p. 103739, 2025, doi: 10.1016/j.net.2025.103739.
C. Napole, O. Barambones, M. Derbeli, and I. Calvo, “Design and experimental validation of a piezoelectric actuator tracking control based on fuzzy logic and neural compensation,” Fuzzy Sets Syst., vol. 464, p. 108449, 2023, doi: 10.1016/j.fss.2022.12.005.
O. G. L. Vivian, O. G. L. Alessandra, C. B. N. Esther, L. R. Danny, and S. Q. J. Jesús, “Prioritization of Incident Management Process using ITIL-Fuzzy for Informatics Development Sector of Private Universities,” Int. J. Fuzzy Log. Intell. Syst., vol. 25, no. 1, pp. 37–54, 2025, doi: 10.5391/IJFIS.2025.25.1.37.
A. Kareem and V. Kumara, “A Novel Fuzzy Logic Based Operating System Scheduling Scheme,” Int. J. Fuzzy Log. Intell. Syst., vol. 24, no. 1, pp. 30–42, 2024, doi: 10.5391/IJFIS.2024.24.1.30.
A. Saelan, “Logika Fuzzy,” Makal. If2091 Strukt. Disk. Tahun 2009, vol. 1, no. 13508029, pp. 1–5, 2009.
M. K. Timur, “Fuzzy Inference System Metode Tsukamoto,” Jiko (Jurnal Inform. dan Komputer), vol. 7, no. 1, pp. 23–29, 2023.
J. Huang, M. Abadi, and J. Yeow, “Policy framework for facilitating reverse logistics in circular construction: an integrated Fuzzy-DEMATEL and system dynamics approach,” Waste Manag., vol. 204, no. April, p. 114963, 2025, doi: 10.1016/j.wasman.2025.114963.
N. Gupta, P. Garg, and N. Ahuja, “An integrated pythagorean fuzzy delphi-AHP-CoCoSo approach for exploring barriers and mitigation strategies for sustainable supply chain in the food industry,” Supply Chain Anal., vol. 10, no. August 2024, p. 100105, 2025, doi: 10.1016/j.sca.2025.100105.
Downloads
Additional Files
Posted
License
Copyright (c) 2025 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
