One of the tofu-producing companies in Kupang City is Bintang Oesapa. With the Covid-19 pandemic,the factory needs to reconsider the amount of production by taking into account the unpredictability ofdemand and resources to minimize losses due to excessive accumulation or shortages of supplies. Indetermining the amount of production, Mamdani’s Fuzzy Inference System (FIS) can be used, whichis a method for the analysis of an uncertain system. This method has three stages in the process ofdecision making, namely fuzzification, inferencing and defuzzification. In the defuzzification stage,the FIS Mamdani has five methods, namely Centroid, Bisector, Mean of Maximum (MOM), Smallestof Maximum (SOM), and Largest of Maximum (LOM). This study discusses an application of FISMamdani with five defuzzification methods for determining daily tofu production. The purpose of thisstudy is to offer a solution by first comparing the five defuzzification methods in assessing the amount oftofu production at the Bintang Oesapa factory and then determining that which is most appropriate. Theinput variables used in this research are the amount of demand and the amount of available stock, whilethe amount of production is our variable of interest. The results showed that the best defuzzificationmethod was the MOM method with an accuracy level of 94.73% and a small error value, 5.27%. TheMOM defuzzification is expected to aid decision makers in determining the best amount of productionduring the pandemic.

Smartphones nowadays have become a basic need for everyone because they provide many benefits and conveniences for users. People always want to have a smartphone with good quality. However, due to the lack of information along with the many types of smartphones in circulation, it often makes it difficult for users to choose a smartphone that suits their needs. To overcome these problems, it is necessary to have a method that can provide recommendations for appropriate decision-making for users. This study aims to apply the Analytic Hierarchy Process (AHP) method and sensitivity analysis in determining the priority order of smartphone selection by comparing one smartphone to another. The criteria for consideration are Facilities, Price, Battery, and RAM with alternative choices in the form of Xiaomi, Oppo, and Vivo brand smartphones. Data collection in this study was carried out by distributing questionnaires to 100 students of the Mathematics Study Program. The data were processed using the AHP method and sensitivity analysis. AHP is used to produce a more consistent ranking order of each alternative, while the sensitivity test is carried out to measure the stability of the calculation results if there is a change in decision-making. From the results of the analysis with AHP, it was found that Xiaomi was the first priority of the respondent's choice, followed by Vivo, and the last priority was Oppo with an inconsistency level of 0.02. Meanwhile, sensitivity testing shows that RAM is the most influential criterion for changing the order of alternative priorities, where Xiaomi remains the first priority, followed by Oppo, and Vivo is the last priority.

The problems of linear programming are developing from time to time, and its complexity is constantly growing. Various problems can be viewed as a multi-objective fuzzy linear programming, multi-objective stochastic linear programming or a combination of both. This research is focused on examining Multi-Objective Fuzzy Stochastic Linear Programming (MOFSLP) with each of the objective functions has a different level of importance to decision makers, or better known as the nonsymmetrical model. The objective function of the linear program contains fuzzy parameters, while the constraint function contains the fuzzy parameters and random variables. The purpose of this study is to develop an algorithm to transform the MOFSLP be a Program of linear Single-Objective Deterministic Linear Programming (SODLP) so that it can be solved using simplex method. In the process of transforming MOFSLP to SODLP, several approaches have been used. They are; weighted additive model, analytic hierarchy process and chance constrained technique. An example of numerical computations has been provided at the end of the discussion in order to illustrate how the algorithm works. The resulted Model and algorithm are expected to help companies in the decision making process.

This paper discusses how to solve balanced transportation problems, with transportation costs in the form of trapezoidal fuzzy numbers. Fuzzy costs are transformed into crisp costs using the Robust’s method as a ranking function. A new approach of modified Hungarian method has been applied to solve the problem of fuzzy transportation. This approach solves the fuzzy transportation problem in one stage of optimization and yields the same results as other methods that solve the problem in two stages.