Mamdani's Fuzzy Logic-Based Tapioca Optimal Production Amount Prediction System - Tiara Safitrah; Divo Wibowo Adi; Irfan Tigranaufal Nugraha; Antonio Banggas Gregory Sinaga; Zaki Rafi Athallah; Mohammad Akbar Alfa Dirk Steyer; Sesar Husen Santosa; Muhammad Danang Mukti Darmawan; Nanda Octavia (Batrisya Education, 2024)
This research focuses on the imbalance between supply and demand in the tapioca industry, especially on the scale of Small and Medium Industries (SMI). Such challenges involve price fluctuations and lack of efficiency in determining the optimal production amount. Using fuzzy logic as an artificial intelligence method, this research aims to develop a system for predicting the optimal amount of tapioca production that can increase efficiency, reduce waste of raw materials and energy, and stabilize prices. The data used in this study was obtained through interviews with resource persons who are involved in the field of tapioca SMI. Furthermore, the data was processed using the Mamdani Fuzzy Inference System method. The results showed that in the case of production when demand is 400 kg and cassava availability is 2000 kg, the optimal production amount of tapioca is 630 kg. This value is also consistent when proven using the Matlab R2015a application. This shows that the model can be relied upon in determining the decision on the amount of tapioca production by considering demand factors and raw material availability.