scholarly journals PENILAIAN MUTU DOSEN TERHADAP TRI DHARMA PERGURUAN TINGGI DENGAN MENERAPKAN LOGICA FUZZY LOGIC DI STIKES NAN TONGGA

2019 ◽  
Vol 8 (2) ◽  
pp. 175
Author(s):  
Tri Monarita Johan ◽  
Renty Ahmalia
Keyword(s):  
Higher Education ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Teaching Staff ◽  
Output Variable ◽  
Inference System ◽  
Quality Value ◽  
Input Variables ◽  
Collection Methods ◽  
Functional Position

Tri Dharma of Higher Education is an activity that must be carried out by every Lecturer. In this study an application was designed to apply Fuzzy logic to calculate the quality value of Lecturers on the implementation of Higher Education Tri Dharma. Higher Education has the aim of producing quality qualifications. Therefore we need competent teaching staff needed. The background of this research is to study the results obtained from the application and calculation using Fuzzy logic, also help the lecturer evaluation in the field of quality control. The Mamdani Method is often also known as the Max-Min Method. This method was introduced by Ebrahim Mamdani in 1975. To get results, four stages are needed: 1. The formation of the fuzzy set; 2. Application function implications (rules); 3. Composition of rules; 4. Affirmation (deffuzy). The results obtained in this study the value of the function that has been optimized where lecturers will get the best in performance. Data collection methods in the fuzzy inference system function meeting, the author requires input data consisting of three variables and one output variable. Input variables consist of: 1. Research Variables 2. Dedication Variables 3. Teaching Variables. 4. Functional Position Variables After calculations and experiments, the results obtained using the Fuzzy Mamdani method with Matlab

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.

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 Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 832
Author(s):  
Xuguang Zhang ◽  
Qinan Yu ◽  
Yuxi Wang
Keyword(s):  
Public Management ◽  
Fuzzy Inference ◽  
Video Monitoring ◽  
Video Sequences ◽  
Fuzzy Evaluation ◽  
Output Variable ◽  
Inference System ◽  
Small Areas ◽  
Distribution Uniformity ◽  
Input Variables

Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian’s number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian’s number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian’s number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian’s number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

scholarly journals The Design of a Portable Municipal Waste Incinerator With Fuzzy Logic Based Support for Emission Estimation

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Jude C. Akpe ◽  
Olatunde A. Oyelaran ◽  
Ibrahim O. Abdulmalik
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Low Carbon Steel ◽  
Maximum Temperature ◽  
Waste Incinerator ◽  
Low Carbon ◽  
Possibility Distribution ◽  
Inference System ◽  
Complete Combustion ◽  
Input Variables

A fuzzy logic interface system to estimate oxygen requirement for complete combustion as well as the level of pollution from incinerator gas flue in order to manage solid waste from domestic, institutional, medical and industrial sources was designed. The designed incinerator is double chambered operating with a maximum temperature of 760 °C in the lower chamber and 1000°C in the upper chamber.  The insulating wall is made up of a refractory brick of 55mm in thickness having a 2mm thickness low carbon steel as the outer wall.  Hydrogen Chloride (HCl) and Nitrous oxides (NO)x are the gases was used to demonstrate the Fuzzy Inference System (FIS) model. The FIS was built with five input variables (Food, PVC, Polythene, Paper and Textile) and three input variables with two membership functions. The FIS was developed to estimation the degree of possibility distribution of pollution that should be expected when a certain composition of waste is incinerated. The plots of composition of waste high in food against oxygen require for combustion gives a possibility distribution of about 0.9 which is high according to the fuzzy set definition while the plot of waste composition high in PVC against HCL shows linearity.

scholarly journals APLIKASI SISTEM INFERENSI FUZZY METODE TSUKAMOTO DALAM PENILAIAN MUTU BENIH PADI

2015 ◽  
Vol 7 (1) ◽  
pp. 19
Author(s):  
Diasta Risi Esa Annisa ◽  
Mutia Nur Estri
Keyword(s):  
Fuzzy Inference System ◽  
Seed Quality ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Rice Seed ◽  
Output Variable ◽  
Inference System ◽  
Rice Seeds ◽  
Input Variables

In this paper, we use the Fuzzy inference system of Tsukamoto method to determine the rice seeds quality. The input variables are production average, the age of plant, and fallen seeds, and the output variable is rice seed quality. The output is determined through 4 steps i.e. fuzzification, determine fuzzy rules, and defuzzification. The results show that the best  quality of  rice seed is IR 64 and the worst is Lusi.

scholarly journals SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA IKAN HIAS AIR TAWAR DENGAN FUZZY INFERENCE SYSTEM

JOUTICA ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 194
Author(s):  
Indahsari Dewi Rina ◽  
Dina Komar Lia
Keyword(s):  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fish Farming ◽  
Ornamental Fish ◽  
Fish Disease ◽  
Inference Process ◽  
Output Variable ◽  
Inference System ◽  
The Media ◽  
Input Variables

One of the main causes of failure in aquaculture activities is due to disease factors. The emergence of disease disorders in fish farming is a biological risk that must always be anticipated. The emergence of diseases in fish is generally the result of complex / unbalanced interactions between the three components in the aquatic ecosystem, namely weak hosts (fish), malignant pathogens and deteriorating environmental quality. Fish cultivators must obtain fast information related to diseases that infect their fish, and how to deal with them. In this study an expert system was created to diagnose ornamental fish disease using the media website, so that it can be used at any time without having to see a doctor / expert. Knowledge base involves 23 symptoms and 5 diseases that are common in freshwater ornamental fish, using a decision table producing 20 Rule. The inference process uses the Tsukamoto fuzzy, the modeling has 23 input variables and 1 output variable. Each input variable has 3 sets and the output variable has 5 sets. The implementation results indicate that the system built can provide diagnostic results with an 85% accuracy rate.

Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

2014 ◽  
Vol 984-985 ◽  
pp. 425-430 ◽  
Author(s):  
Thangavel Ramya ◽  
A.C. Kannan ◽  
R.S. Balasenthil ◽  
B. Anusuya Bagirathi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Decision Making Process ◽  
Computing Environment ◽  
Output Variable ◽  
Inference System ◽  
Logic Modeling ◽  
Decision Making Processes

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

scholarly journals Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

2021 ◽  
Vol 28 (121) ◽  
pp. 39-47
Author(s):  
Hilal Bilgiç ◽  
Yusuf Kuvvetli ◽  
Pınar Duru Baykal
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Real Life ◽  
Operation Time ◽  
Difficulty Level ◽  
Decision Framework ◽  
Inference System ◽  
Fuzzy Logic Approach ◽  
Input Variables ◽  
Fabric Weight

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

scholarly journals Line Follower Robot Optimization based Fuzzy Logic Controller Using Membership Function Tuning

2018 ◽  
Vol 7 (2.2) ◽  
pp. 112
Author(s):  
Supriadi Supriadi ◽  
Ansar Rizal ◽  
Didi Susilo Budi Utomo ◽  
Agusma Wajiansyah
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Inference System ◽  
On Line ◽  
Input Variables ◽  
The Right ◽  
Triangular Membership Function

The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.  

scholarly journals Fuzzy Logic Application for Drought Risk Determination in Kulon Progo Regency, Daerah Istimewa Yogyakarta Province, Indonesia

2021 ◽  
Vol 2 (1) ◽  
pp. 68-81
Author(s):  
Bertolomeus Laksana Jayadri ◽  
Agus Maman Abadi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Risk Level ◽  
Drought Risk ◽  
Risk Map ◽  
Output Variable ◽  
Exposed Population ◽  
Northern Regions ◽  
Input Variables

This study aims to determine the drought risk of Kulon Progo Regency using fuzzy logic and study the characteristics. The input variables used in this study are the drought level, exposed population, and vulnerable population. The Mamdani method used in the fuzzy inference to obtain the output variable, that is, the Drought Risk Index (DRI). Then, the DRI are mapped to generate the drought risk map. The result shows that the fuzzy logic can be used to determine the drought risk. The drought risk level of the subdistricts in Kulon Progo Regency was fluctuated from 2010 to 2019. The drought risk level in 2010-2015 and 2019 were dominated by the low category. Meanwhile, the drought risk level in 2016-2018 was dominated by the very low category. Furthermore, the result also shows that the subdistricts located in the southern region of Kulon Progo Regency had a higher risk than those in the middle and northern regions during the last 10 years

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