scholarly journals Artificial Intellegence Untuk Mendeteksi Penyakit Kelenjar Getah Bening (Lymphadennopathy) Menggunakan Fuzzy Inference System (Fis) Di Kota Batam

2018 ◽  
Vol 6 (01) ◽  
pp. 54
Author(s):  
Sestri Novia Sestri ◽  
Algifanri Maulana
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Inference System ◽  
Average Value ◽  
Fuzzy Methods ◽  
System Output ◽  
Input Variables ◽  
To Come ◽  
The City

Health is a basic thing to be maintained in life, because with a strong health and physical man can run his life. Batam is a rapidly growing city seen from many residents who live in the city of batam, but most of them are less healthy so that the disease is easy to come, another thing that is less his expert in the lymph node and costly in his treatment. The dangerous condition that causes swollen glands is a blood infection. A person suffering from a blood infection will look very weak. And will also experience a fever that will worsen and also accompanied by a body that feels shivering. This infection is caused by a bacterial attack and someone who experienced it should be treated as soon as possible in hospital. The clear-spoken cleavage is part of the human body's defense system. output Solving production problems using the Sugeno and Sugeno fuzzy methods corrects the weaknesses of the pure fuzzy system to add a simple mathematical calculation as part of THEN. In this change, the fuzzy system has a weighted average value (Values) in the IF-THEN fuzzy rules section. The Sugeno fuzzy system also has a disadvantage, especially in the THEN part, that is, by mathematical calculations that it can not provide a natural framework for representing human knowledge in fact. This method uses the mathematical constants or functions of the input variables, and in the defuzzification process uses the centralized mean method.

scholarly journals A fuzzy system for cloacal temperature prediction of broiler chickens

2012 ◽  
Vol 42 (1) ◽  
pp. 166-171 ◽  
Author(s):  
Leandro Ferreira ◽  
Tadayuki Yanagi Junior ◽  
Wilian Soares Lacerda ◽  
Giovanni Francisco Rabelo
Keyword(s):  
Decision Support ◽  
Fuzzy System ◽  
Broiler Chickens ◽  
Broiler Chicken ◽  
Fuzzy Inference ◽  
Air Velocity ◽  
Output Variable ◽  
Average Standard Deviation ◽  
Inference System ◽  
Input Variables

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

scholarly journals Fuzzy Inference System (FIS) dengan Metode Tsukamoto dan Mamdani dalam Menentukan Kelayakan Kenaikan Gaji Karyawan

2017 ◽  
Vol 1 (2) ◽  
pp. 7-14 ◽  
Author(s):  
Tri Yani Akhirina ◽  
Michael Sonny
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Membership Degree ◽  
Inference System ◽  
Fuzzy Methods ◽  
Mamdani Fuzzy Inference System

Each Company will evaluate the employees by their performance to determine the payroll increase. The payroll increase is a kind of reward from company to the employee’s performance in a year, and this is given once a year. In some respects, the awarding of payroll increase often meets problems, such as the un-objective appraisal and the inappropriate scale amount of increase for it emerges polemic between employees that will give an impact to their performance. To avoid such things, the company needs an appropriate and a flexible method to do the evaluation. The purpose of this research is to implement Fuzzy Inference System (FIS) in determining the employee’s payroll increase feasibility. Logical Fuzzy is a problem completion technique where the membership degree which is commonly represented by the score of 0 and 1, so that it is more balance. One of Fuzzy methods which can be used to solve the problem is Tsukamoto method and Mamdani. Fuzzy Inference System which implements a weighted average to calculate the feasibility of payroll increase is as the final result. The Inference Support System or SPK of the payroll increase feasibility with Tsukamoto method and Mamdani.

scholarly journals Application of The Fuzzy Inference System Method to Predict The Number of Weaving Fabric Production

2018 ◽  
Vol 7 (1) ◽  
pp. 19 ◽  
Author(s):  
Tundo Tundo ◽  
Enny Itje Sela
Keyword(s):  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Production Costs ◽  
Woven Fabric ◽  
Mathematical Functions ◽  
Inference System ◽  
The Difference ◽  
Input Variables ◽  
Fabric Production

In this study discusses the application of fuzzy logic in solving production problems using the Tsukamoto method and the Sugeno method. The problem that is solved is how to determine the production of woven fabric when using three variables as input data, namely: stock, demand and inventory of production costs. The first step is to solve the problem of woven fabric production using the Tsukamoto method which is to determine the input variables and output variables which are firm sets, the second step is to change the input variable into a fuzzy set with the fuzzification process, then the third step is processing the fuzzy set data with the maximum method. And the last or fourth step is to change the output into a firm set with the defuzzification process with a weighted average method, so that the desired results will be obtained in the output variable. The solution to the production problem using the Sugeno method is almost the same as using the Tsukamoto method, it's just that the system output is not a fuzzy set, but rather a constant or a linear equation. The difference between the Tsukamoto Method and the Sugeno Method is in consequence. The Sugeno method uses constants or mathematical functions of the input variables.

scholarly journals PEMANFAATAN FUZZY INFERENCE SYSTEM UNTUK MENENTUKAN DAMPAK COVID-19 TERHADAP PEREKONOMIAN DI KOTA BATAM

JURTEKSI ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 341-348
Author(s):  
Nanda Jarti
Keyword(s):  
Decision Making ◽  
Common Cold ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Human Life ◽  
Inference System ◽  
Corona Virus ◽  
Input Variables ◽  
The Impact ◽  
The City

Abstract : Corona virus is a very dangerous virus and can kill human life. This virus causes minor illnesses and serious illnesses such as colds or colds, since the emergence of the Corona Virus or Covid 19 paralyzing all human activities carried out outside the home. The problem of this research is in the form of the impact of the corona virus on the economy, especially in the city of Batam so that the residents of Batam can overcome this corona virus outbreak to improve the weakening economy. The main objective of this research is to examine the impact of Covid 19 on the economy of the Batam population so that the Batam population can improve the already weakened economy. This study uses Fuzzy Inference Sistym the Mamdani Method for Decision Making, using Operators or through the process of Fuzification of Input Variables, Inference Machines to process rules and produce Defuzification to get the final value  Keywords: corona prediction fuzzy inference system; mamdani method  Abstrak:Virus Corona merupakan  sebuah virus yang sangat berbahaya dan  bisa menghilangkan nyawa manusia. Virus ini  mengakibatkan penyakit  ringan dan penyakit berat  seperti common cold atau pilek, Sejak munculnya Virus Corona  atau Covid 19 melumpuhkan semua  kegiatan aktivitas manusia  yang dilakukan diluar rumah. Permasalahan  Penelitian ini berupa dampak akibat virus corona terhadap perekonomian khususnya pada Kota Batam sehingga penduduk Batam bisa mengatasi Wabah Virus corona ini untuk meningkatkan perekonomian yang semakin melemah. Tujuan Utama Penelitian ini mengkaji Dampak akibat Covid 19 terhadap perekonomian penduduk batam sehingga  penduduk Batam bisa meningkatkan perekonomian yang sudah melemah. Penelitian ini menggunakan Fuzzy Inference Sistem  Metode Mamdani untuk Pengambilan sistem Keputusan, menggunakan Operator Or dan melalui proses Fuzifikasi penentuan Variabel Input, Mesin Inferensi untuk melakukan proses aturan dan menghasilkan Defuzifikasi untuk mendapatkan nilai akhir. Kata Kunci : fuzzy inference sistem;  metode mamdani; prediksi corona

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

scholarly journals Implementasi Fuzzy Inference System Untuk Menentukan Tingkat Kriminalitas Di Kota Batam

2020 ◽  
Vol 10 (2) ◽  
pp. 206-221
Author(s):  
Sesri Novia Rizki ◽  
Handra Tipa
Keyword(s):  
Crime Rate ◽  
Fuzzy Inference ◽  
Inference System ◽  
Research Results ◽  
Fuzzy Process ◽  
Job Vacancy ◽  
Main Factors ◽  
High Crime ◽  
The City ◽  
High Crime Rate

Kriminalitas merupakan sebuah perbuatan meyimpang serta merugikan banyak orang.Pada tahun 2017 perekonomian Kota Batam menurun, sehigga banyak perusahaan yang tutup dan menyebabkan tingkat pengangguran meningkat.Kejahatan yang marak terjadi saat ini seperti pembegalan, pencopetan, Penipuan, dan penjampretan tanpa belas kasihan bahkan menyebabkan korban meninggal dunia.Contoh  kejahatan yang sering terjadi berupa pembegalan dan penjamretan  pada daerah tamiang, banyak  korban yang berjatuhan, pelaku Tidak segan melukai bahkan menghilangkan nyawa korbannya. Faktor utama penyebab  kriminalitas seperti tingkat kesenjangan social, pendidikan, pendidikan, pergaulan,  PengangguranLowongan Pekerjaan serta pendidikan sehingga banyak hal yang membuat tingkat kriminalitas yang tinggi di kota Batam. Tujuan penelitian ini yaitu untuk mengetahui tingkat kriminalitas di kota Batam, serta sabagai pembelajaran bagi penduduk batam sehingga terhindar dari kejahatan. Metode yang digunakan menggunakansystem max-mix atau logika sugeno, langkah kerja metode fuzzy ada empat, yang pertama pembentukan himpunan fuzzy, yang kedua aplikasi fungsi implikasi yang ketiga komposisi aturan dan yang keempat adalah defuzzifikasi.Fokus penelitian ini berupa 1.Menentukan Tingkat kriminalitas 2.Penyebab  kriminalitas 3.Menggunakan metode sugeno dan aplikasi matlab untuk menyelesaikan hasil penelitian. Penelitian ini menghasilkan system mendukung keputusan berupa hasil akhir sebesar 0.72 berada pada posisi Output dengan nilai keputusan tingkat kriminalitas tinggi di Kota Batam.                                                               AbstractCrime is an act that deviates and harms many people. In 2017 the economy of Batam City declined so that many companies closed and caused the unemployment rate to increase. Crimes are rife at this time, such as hijacking, pickpocketing, fraud, and mugging without mercy, even causing death. Examples of crimes that often occur in the form of kidnapping and mugging in the Tamiang area, many victims have fallen, the perpetrators do not hesitate to hurt or even kill the lives of their victims. The main factors were causing crime such as the level of social inequality, education, relationships, Job Vacancy Unemployment and education so that many things that make a high crime rate in the city of Batam. The purpose of this study is to determine the level of crime in the city of Batam, as well as learning for residents of Batam, is avoid way. The method used uses the max-mix system or Sugeno logic, there are four steps in the fuzzy process, the first is the formation of the fuzzy set, the second is the application of the implication function, the third is the composition of the rules, and the fourth is defuzzification. The scope of this research is 1.  They are using the level of crime 2. Cause of crime 3. They are using the Sugeno method and the application of Matlab to complete the research results. This research results offence ina system was supporting the decision in the form of the final They are determining of 0.72 is in the Output position with a high crime rate decision value in Batam City.  

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