Funding eligibility decision support system using fuzzy logic Tsukamoto: (Case: BMT XYZ)

Ardika, Bayu Sanggi; Setianingrum, Anif Hanifa; Hakiem, Nashrul

doi:10.1109/iac.2017.8280622

Cited by 5 publications

(3 citation statements)

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“…According to evaluation list, we found that this proposed model has exactly 99% accuration value. It means that Tsukamoto inference system works effectively [9] in this model [10,7,11,12].…”

Section: Resultsmentioning

confidence: 99%

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

et al. 2018

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Train is one of the most favorite mass transportation in the world. In 2017 train in Indonesia can bring about 341.605 passengers to many destinations. PT. Kereta Api Indonesia is a state owned enterprise that has responsibility to make sure that train is safe and works well. As we know that a train has several components to check. It is very difficult to identify whether a train is in a good condition or needs repairing. The purpose of this work is to proposes a model of operational feasibility by several main criteria: bogie, breaking system, boffer, electric coupler, and safety kit. In its experiment phase, this model uses Tsukamoto Fuzzy Inference System to decide that a train is in good condition or need repairing. In evaluation phase we compare this model with traditional method and this model shows exactly 99% of the same result. It is suggested for further work to include several methods such as Tsugeno and Mamdani fuzzy inference system.

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Section: Resultsmentioning

confidence: 99%

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

et al. 2018

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“…As a result, the inference output of each rule is given explicitly based on the predicate. The end result is obtained by using weighted averages [28,31,32]. This method mainly implemented in several field as follow: promotion system, multimedia, game, education, etc.…”

Section: Tsukamoto Fuzzy Inference Systemmentioning

confidence: 99%

Decision support system for football player's position with tsukamoto fuzzy inference system

et al. 2018

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Nowadays, football is one of the most famous sports in the world. Many football clubs and football academies have been established in Indonesia. In football academy, each player will be trained and selected to get the best positon in the team formation. In fact, each player has a different ability and skill. If a player gets a correct position, he can open the opportunity for his team to win a competition. This condition absolutely gives a good impact for the team. However, it will be a serious problem if a player plays in an incorrect position. The player's best position can be deciding by his own ability and skill. This study proposes the selection model of player's position by understanding a player's speed, stamina, strength, and other skills that covering, shooting, passing, dribble, and header with Tsukamoto Fuzzy Inference System. A player may have the following positions: central forward, midfielder, winger, goal keeper. In evaluation phase, this model exactly shows 52.17% accurate value. This means that the model decreases the misplacement of player's position. It is recommended for further study to make some additional criteria such as player's emotion, attitude, etc in order to increase accurate.

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“…Solving problems with this DSS has developed a lot [4], [5]. One of them is the Fuzzy Inference System method, both Mamdani and Tsukamoto [6] and Sugeno [7]. This fuzzy method is reliable enough to be used in various fields, including aerospace, automotive, control [8], prediction [9], [10], and informatics.…”

Section: Introductionmentioning

confidence: 99%

The Comprehensive Mamdani Inference to Support Scholarship Grantee Decision

Humaira

Rasyidah

Junaldi

et al. 2021

JOIV : Int. J. Inform. Visualization

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Fuzzy Mamdani has been mostly used in various disciplines of science. Its ability to map the input-output in the form of a surface becomes an interesting thing. This research took DSS case of a scholarship grantee. Many criteria in taking a decision need to be simplified so that the result obtained remains intuitive. The model completion by conducting two stages consisted of two phases. The first phase consists of four FIS blocks. The second phase consists of one FIS block. The FIS design in the first phase was designed in such a way so that the output obtained has a big score interval. FIS output at the first phase will become FIS input at the second phase. This big value range becomes good input at FIS in the second phase. Each FIS block has different total input. Until the surface formed must be seen from various dimensions to assure trend surface increasing or decreasing softly. This kind of thing is conducted by observing the movement of output dots kept for its soft surface form. The output dots change influenced by the membership function, the regulations used, total fuzzy set, and parameter value of membership function. This research used the Gaussian membership function. The Gaussian membership function is highly suitable for this DSS case. This article also explains the usage of a fuzzy set in each input, the parameter from the membership function, and the input value range. After observing the surface form with an intuitive approach, then this model needs to be evaluated. The evaluation was done to measure the model performance using Confusion Matrix. The result of model performance obtained accuracy in the amount of 85%.

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Funding eligibility decision support system using fuzzy logic Tsukamoto: (Case: BMT XYZ)

Cited by 5 publications

References 0 publications

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

Decision support system for football player's position with tsukamoto fuzzy inference system

The Comprehensive Mamdani Inference to Support Scholarship Grantee Decision

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