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All Journal Jurnal Varian Unnes Journal of Mathematics
Andika Ellena Saufika Hakim Maharani
Universitas Bumigora
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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The Defuzzification Methods Comparison of Mamdani Fuzzy Inference System in Predicting Tofu Production Grandianus Seda Mada; Nugraha Kristiano Floresda Dethan; Andika Ellena Saufika Hakim Maharani
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1816

Abstract

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.
Prediction of final grade in linear algebra course with multiple linear regression approach Andika Ellena Saufika Hakim Maharani; Siti Soraya; Gilang Primajati; Habib Ratu Perwira Negara; Ahmad Ahmad
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i01.67448

Abstract

This article discusses the analysis of the final grade of Linear Algebra course with multiple linear regression approach. The study was conducted by collecting data on attendance, daily grades, and final grades from students of the Bumigora University Computer Science Program who took Linear Algebra courses in the odd semester of 2022/2023. Collected data were analyzed using multiple linear regression techniques. The purpose of this study is to determine the relationship between the variables that have a significant effect on student’s final grade and how to predict these variables using multiple linear regression models. The results of the analysis show that both independent variables, namely attendance and daily grades, have a significant impact on the dependent variable, namely student's final grade, with a significance value less than 0.05. The resulting multiple linear regression model can also be used to predict student’s final grade with an accuracy of 70.4%. Furthermore, the results of this analysis also show that daily grades has a greater influence than attendances in predicting final grades. The results of this study can provide useful information for lecturers in improving teaching and for students to improve their performance in the course.
Co-Authors Ahmad Ahmad Gilang Primajati Habib Ratu Perwira Negara Mada, Grandianus Seda Nugraha K. F. Dethan Siti Soraya