PENERAPAN METODE FUZZY DALAM SISTEM PENENTUAN PEMBELIAN BAHAN BAKU

Abstract

PT Cubic Indonesia is a company operating in the field of cubic and printing technology services with original designs. In its operational processes, the company heavily relies on the availability of raw materials to meet customer demand. However, the manual raw material purchasing system creates several problems. One of these is suboptimal inventory recording, which often leads to an overstock due to excessive ordering or a stock-out when sudden demand cannot be met. This not only increases storage costs but also potentially causes losses from production delays and loss of customer trust. To solve these problems, this study aims to develop an information technology-based raw material purchasing decision system that can help the company make decisions faster, more accurately, and more effectively. The method used is Fuzzy Tsukamoto, chosen for its ability to handle uncertainty and provide more flexible decision results. The system design was created with a Unified Modeling Language (UML) approach to illustrate the system's functional requirements and workflow. Furthermore, the system was implemented using a web-based programming language so it can be easily accessed in real time and integrated with the company's operational processes.  This research resulted in a decision support system for determining raw material purchases at PT Cubic Indonesia by applying the Fuzzy Tsukamoto method. This web-based system, built with a UML modeling approach, can accommodate user needs in managing raw material inventory more effectively. The test results show that the developed system can provide more accurate purchasing recommendations based on stock conditions and demand levels. Implementing this system is able to reduce the risk of both overstock and stock-out, allowing the company to reduce potential losses, increase inventory management efficiency, and maintain the smooth flow of production.  Thus, the proposed Fuzzy Tsukamoto-based decision support system is proven to help the company make raw material purchasing decisions more accurately and efficiently. In the future, this research can be further developed by integrating historical sales data, demand prediction methods, and the automation of raw material ordering so the system becomes more adaptable to market changes.