Error numerical analysis for result of rainfall prediction between Tsukamoto FIS and hybrid Tsukamoto FIS with GA

Wahyuni, Ida; Utaminingrum, Fitri

doi:10.1109/icacsis.2016.7872721

Cited by 6 publications

(4 citation statements)

References 11 publications

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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%

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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%

“…The purpose of this work is to proposed a model of operational feasibility using Tsukamoto Fuzzy Inference System to decide that a train is in good condition or need repairing [4][5][6]. Tsukamoto fuzzy inference system mainly used in a lot of filed and show that this method is effective and accurate [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Example: Suppose there are two input variables Var-1 (x) & Var-2 (x), as well as output variables Var-3 (z), where Var-1 is divided into 2 sets ie A1 & A2. Var-2 is divided into 2 sets B1 & B2, Var-3 jg is divided into 2 sets ie C1 & C2 (C1 & C2 must be monotone) [36,31,28,37].…”

Section: Conjunction Of Tsukamoto Fuzzymentioning

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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“…Untuk menentukan hasil yang tegas (Z) dicari dengan cara mengubah input (berupa himpunan fuzzy yang diperoleh dari komposisi aturan-aturan fuzzy) menjadi suatu bilangan pada dominan himpunan fuzzy tersebut. Cara ini disebut sebagai dengan metode defuzzyfikasi (penegasan) (Wahyuni & Utaminingrum, 2016). Metode defuzzyfikasi yangdigunakan dalam metode Tsukamoto adalah metode defuzzyfikasi rata-rata terpusat atau center average defuzzyfier (Wahyuni & Mahmudy, 2017).Dalam inferensinya, metode FIS Tsukamoto menggunakan tahapan berikut (Wahyuni, Mahmudy, & Iryani, 2016)…”

Section: Fuzzy Inference System (Fis) Tsukamotounclassified

Penentuan Topik Judul Tugas Akhir Mahasiswa Menggunakan Fuzzy Inference System Tsukamoto

Kristian¹,

Wahyuni²

2018

jitika

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At the end of the study, every student at all levels of public and private universities is required to complete the final project as a condition of graduated students. In the process of final work is not uncommon students experience many difficulties to determine the topic and research title. Therefore it takes a method that can be used to assist students in choosing the topic of the final project in accordance with the value of the courses obtained. Fuzzy Inference System (FIS) Tsukamoto is one method that can be used for decision making with many attributes or criteria. Therefore, in this study used FIS Tsukamoto to determine the topic of the final assignment of students. The attributes used as input criterion are the preliminary final course subject value of Decision Support System (SPK), Artificial Intelligence (AI), Image Processing and Pattern (PCP), Computer Network 1, Computer Network 2, Network Security, Data Mining and Expert System. There are 4 output topics titles final titles used are Intelligent System, Computer Networking, Bio Informatic and Decision Support System (SPK). By using the value data will be modeled method to determine the topic of the final project in accordance with the value obtained. Testing is done by comparing the ranking of predictions with the actual data rank on 100 student value data using Spearman rank correlation test. Spearman rank correlation value of test result that is equal to 0,998. If viewed from the Spearman rank correlation table the results are categorized very well.

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Error numerical analysis for result of rainfall prediction between Tsukamoto FIS and hybrid Tsukamoto FIS with GA

Cited by 6 publications

References 11 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

Penentuan Topik Judul Tugas Akhir Mahasiswa Menggunakan Fuzzy Inference System Tsukamoto

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