Fuzzy Sugeno Algorithm for Clustering Document Management

Kamila, Layli Chusna Nikmatul

doi:10.30534/ijatcse/2020/05912020

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The range of input, number of membership functions and trapezoidal numbers (a, b, c, d) was set in Params input box according to the FTS method [2] as can be seen in Figure 3. The output of Sugeno type FIS is a linear function [24]. Here, the constant output was set based on the average of each of the trapezoidal membership set as given in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

Electricity Consumption Forecasting Using Fuzzy Inference System

2020

IJATCSE

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Electricity consumption in Universiti Tun Hussein Onn Malaysia (UTHM) has previously been forecasted using a time series model, fuzzy time series (FTS) and multiple linear regression (MLR). Even though FTS gives the best prediction among these methods, the manual implementation of FTS can be a time consuming and laborious process. Like FTS, Fuzzy inference system (FIS) is an alternative system that utilizes fuzzy logic concept. The prediction using FIS, however, required multivariate data such as day, time, temperature, humidity and historical consumption data as its input. In this study, monthly univariate data of UTHM electricity consumption from January 2009 to December 2018 was employed for the forecasting of electricity consumption for the year 2019. FIS was chosen to be compared with the FTS method. Historical univariate data were fuzzified using trapezoidal rule following the FTS method before Sugeno type FIS was chosen to give constant output. A simple fuzzy rule was applied to map the input to output. 2.3173% of mean absolute percentage error (MAPE) was obtained in FIS which is considerably low if compared to the time series model (11.14%), MLR (10.62%), and FTS (5.74%).

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Section: Methodsmentioning

confidence: 99%

Electricity Consumption Forecasting Using Fuzzy Inference System

2020

IJATCSE

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“…The application of the FL Sugeno method provides a systematic approach to generate fuzzy rules from a given input to the output. To calculate the resulting output, Sugeno technique uses weighted average where the resulting output can be a separate characteristic with the final result not in the form of a fuzzy set but a linear or constant equation [21], [22].…”

Section: Calculation Academic Performancementioning

confidence: 99%

Decision Support Model using FIM Sugeno for Assessing the Academic Performance

Kurniawan¹,

Utama²

2021

Adv. sci. technol. eng. syst. j.

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Assessing academic performance is a common way of evaluating and assessing the abilities of students in tertiary institutions. Usually it is practically performed based on the cumulative grade point average (GPA) at the end of each semester passed. Unwittingly there are many factors that are able to influence student performance results apart from GPA as a performance measure; i.e. gender, hometown, sibling, family status, residence, father education, mother education, family income, motivation, mileage, traveled time, transportation, scholarship, community, social media, and hang-out. Academic performance assessment is proposed through the decision support model (DSM) applying the fuzzy logic (FL) Sugeno technique. The model output generates a decision value (linear or constant equation) for academic performance based on the calculation of the measured fuzzy parameter value (ax) and conventional parameter value (bx). The DSM with the FL Sugeno method is able to provide sharp output in assessing student academic performance. In this case, the model is able to be applied then to assist academics in higher education in determining educational strategies for students with poor academic performance results.

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“…If all propositions have been evaluated, the output will contain a fuzzy set that reflects the contribution of each proportion. Rules determine the input and output of the membership function that will be in the linguistic inference process and also the rules entitled "IF-THAN" [26]- [27].…”

Section: Inference Enginementioning

confidence: 99%

Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

Zuliana¹

2020

IJATCSE

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The purpose of this study is to make a simulation for the assessment of student performance in elementary mathematics learning process by using fuzzy logic to overcome the problem of the assessment process of student performance. Besides that there is no special system that can optimize in providing support for teachers in conducting evaluations that are still manual calculations. One way of determining the calculation of student evaluation results can be facilitated by using the help of Artificial Intelligence (AI) considerations as an optimization. In consideration of the performance evaluation (performance) of these students using fuzzy logic with the sugeno inference system method. This sugeno method is a fuzzy inference method for rules which is represented in the form of IF-THEN, where the system output is not a fuzzy set, but in the form of a linear equation. The criteria used in the assessment of student performance include very unsuccesusful, unsuccessful, average, successful, and very successful. In this simulation the results displayed with manual calculations and MatLab calculations as a comparison of the results of the manual calculation result value of 45 while in the MatLab calculation the result value is 48,5. So it can be concluded that the difference caused by the level of accuracy of the results of the inference rule in manual calculations is less effective and sometimes even a lot of inference rules must be adjusted.

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Fuzzy Sugeno Algorithm for Clustering Document Management

Cited by 4 publications

References 6 publications

Electricity Consumption Forecasting Using Fuzzy Inference System

Electricity Consumption Forecasting Using Fuzzy Inference System

Decision Support Model using FIM Sugeno for Assessing the Academic Performance

Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

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