<![CDATA[In Bad Stock (BS) control at PT Sari Roti Semarang, it is crucial to reduce the company's significant losses. However, the selection of rejected and returned bread with poor quality is still prevalent. Based on production data and the amount of bad stock (a collection of returned bread from stores and rejected production), a decision support system for controlling bad stock using Fuzzy Tsukamoto is needed. In this decision support system, three variables are modeled: production, store returns, and production rejects. The production variable consists of two fuzzy sets: increase, remain the same, and decrease. The store returns variable consists of two fuzzy sets: decrease, remain the same, and increase. The production rejects variable consists of three fuzzy sets: few, normal, and many. The membership values are only two values, 0 and 1, while the membership values in the fuzzy sets are closed intervals [0,1]. By combining all these fuzzy sets, three fuzzy rules are obtained, which are then used in the inference stage. In the inference stage, the membership value of the antecedent (α) and the estimated amount of production (z) are found for each rule. The amount of rejected production (Z) is obtained by centered average defuzzification. The research results based on expert validation testing indicate that the obtained values are in the range of 2.51-3.25, which is categorized as valid or good. Meanwhile, the validation testing from users indicates that the obtained value of 3.4 is in the range of 3.26-4.00, which is categorized as highly valid. The effectiveness testing of the system's performance results in an average value of 89.5%.]]>
