Fuzzy Inference System Model from Non-Fuzzy Clustering Output

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian yang lebih baik dan akurat. Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

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Prediction of Chikungunya disease using PSO-based adaptive neuro-fuzzy inference system model

International Journal of Computers and Applications ◽

10.1080/1206212x.2020.1870196 ◽

2021 ◽

pp. 1-9

Author(s):

Sandeep Kaur ◽

Kuljit Kaur Chahal

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

System Model ◽

Inference System ◽

Neuro Fuzzy

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A novel and fast MIMO fuzzy inference system based on a class of fuzzy clustering algorithms with interpretability and complexity analysis

Expert Systems with Applications ◽

10.1016/j.eswa.2017.04.045 ◽

2017 ◽

Vol 84 ◽

pp. 301-322 ◽

Cited By ~ 10

Author(s):

S. Askari

Keyword(s):

Fuzzy Clustering ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Complexity Analysis ◽

Clustering Algorithms ◽

Inference System

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A Fuzzy Logic Based System for Secure Scrum

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9747.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 8961-8964

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

System Performance ◽

Fuzzy Inference ◽

System Model ◽

Basic System ◽

Inference System ◽

General Functioning ◽

Software Engineers

Software is a basic system that acts as a major key part in general functioning system like securing the need of performance and scope of the system. Here the security is given to unauthorized user as unauthorized client that casually gets the change or modification within the system by effecting the efficiency and functionality of the system. So in order to overcome this issue new improved software is taken that improves the system performance and security. the paper represents a new fuzzy logic based system for handling secured attribute and assessment in software. Based on this reason we propose PC1 and bugs dataset for fuzzy inference system can be used. This secured system model helps software engineers to select secured and safety software for the performance and ambiguity.

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Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v22n6p412-417 ◽

2018 ◽

Vol 22 (6) ◽

pp. 412-417 ◽

Cited By ~ 4

Author(s):

Patrícia F. P. Ferraz ◽

Tadayuki Yanagi Junior ◽

Yamid F. Hernandez-Julio ◽

Gabriel A. e S. Ferraz ◽

Maria A. J. G. Silva ◽

...

Keyword(s):

Respiratory Rate ◽

Fuzzy Inference System ◽

Prediction Accuracy ◽

Fuzzy Inference ◽

Fuzzy Rule ◽

System Model ◽

Inference System ◽

Rule Based ◽

Rule Based System ◽

Genetic Fuzzy System

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

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