scholarly journals PENERAPAN LOGIKA FUZZY DALAM OPTIMASI PRODUKSI BARANG MENGGUNAKAN METODE MAMDANI

2017 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Wahyu Toto Priyo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Set ◽  
Alternative Solution ◽  
Output Variable ◽  
Centroid Method ◽  
Production Problem ◽  
Input Variables ◽  
Production Problems ◽  
Maximum Method

Developement and revolution of the era are always followed by the development of technology and sciences. Along with the development of the era, it will be found various problems in various aspects of life, including in the field of industry. Often companies experience obstacles in meeting the number of requests from consumers, or even the amount of inventory of many goods. From these problems required an appropriate solution for production problems can be resolved. In this research, we will apply Fuzzy Logic as one of alternative solution of goods production problem, that is by using Mamdani method, with the quantity of demand and inventory of goods as input variable and quantity of goods produced as output variable. Then followed by 4 stages, namely: (a) Formation of fuzzy set by fuzzification process, (b) Application of implication function, (c) Composition of rules with maximum method, (d) Process defuzzification with centroid method that will result outout amount of goods which must be produced by the company. From the results of the analysis that has been done, by entering the input variables the number of requests amounted to 54,900 units and the amount of inventory amounted to 4060 units produce output production amounted to 46,600 units.Keywords: Fuzzy Logic, Mamdani methode, goods production

scholarly journals Penerapan Fuzzy Mamdani Dalam Menentukan Pencapaian Keberhasilan Mengajar Guru MI Mambaul Ulum Al-Amin Sampit

2021 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
R Rahmawati
Keyword(s):  
Fuzzy Sets ◽  
Achievement Level ◽  
Output Variable ◽  
Centroid Method ◽  
The Third ◽  
Input And Output ◽  
Processing Data ◽  
Teacher Success ◽  
Input Variables ◽  
Certain Answers

Current problems often do not have certain answers. The use of fuzzy mamdani logic to determine a level of achievement of the success of the teacher in teaching students at MI Mambaul Ulum Al Amin Sampit is the content of this paper. The problem will be solved by determining the level of achievement of the teacher's success in teaching students, if only two input variables are used, namely the teacher and also the value. So the first thing to solve the problem of the level of achievement of teacher success in teaching through the fuzzy mamdani method is to determine the input and output variables of a firm set. The second thing is changing the input variables into fuzzy sets through fuzzyfication. The third thing is processing data from fuzzy sets using the maximum system. The last or fourth thing is changing the results issued into a firm set through defuzzyfication with the centroid method, so the results are as desired in the output variable. The calculation of fuzzy mamdani results in an achievement level of success for MI teachers with a teacher variable value of 55 and a variable value of 80

Determination of the Amount of Masterbatch Production Using the Fuzzy-Mamdani Method

2019 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Fikri Radiansyah ◽  
Wahyu Oktri Widyarto
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Set ◽  
Centroid Method ◽  
Maximum Production ◽  
On Demand ◽  
Late Delivery ◽  
Total Inventory

Based on demand data from January 2013 to December 2013 at PT. Bukit Surya Mas there is an increase in demand, supply and amount of consumer production every month. With the occurrence of demand, supply and maximum production results PT. Bukit Surya Mas always tries to ensure that the amount of production ordered can be completed on time. This is done as a way to give satisfaction to customers, so that there will be no shortage of production services to consumers just because of the late delivery of the product. Problems that arise in this world often contain uncertainty, fuzzy logic is one method for analyzing systems that contain uncertainty. In this study the Mamdani method or often also known as the Min - Max method. The design of the system to get the output is done in stages (a) the formation of the fuzzy set, (b) the application of the implication function, (c) forming the rules, (d) affirmation (defuzzification). In this study defuzzification was carried out using the centroid method. In this method the defuzzyfication value moves smoothly, so changes in the fuzzy set will also move smoothly. From the results of the research that has been carried out, the following is the Masterbatch product from the input variable in January 2014, namely the total demand of 150,735.00 kg and the total inventory of 24,785 kg resulting in an output of 20,300 units.

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 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

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.

Prediction of State of Charge (SOC) of Battery Electric Vehicle

2021 ◽  
Vol 104 ◽  
pp. 65-71
Author(s):  
Illa Rizianiza ◽  
Dian Mart Shoodiqin
Keyword(s):  
Fuzzy Logic ◽  
Maximum Energy ◽  
State Of Charge ◽  
Battery Management ◽  
Charge Condition ◽  
Available Energy ◽  
Energy Needs ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Charging And Discharging Processes

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

scholarly journals Penerapan metode fuzzy sugeno untuk prediksi persediaan bahan baku

2019 ◽  
Vol 9 (2) ◽  
pp. 12-20
Author(s):  
Julio Warmansyah ◽  
Dida Hilpiah
Keyword(s):  
Fuzzy Logic ◽  
Raw Materials ◽  
Mean Absolute Percentage Error ◽  
Raw Material ◽  
Percentage Error ◽  
Human Power ◽  
Absolute Percentage Error ◽  
Input Variables ◽  
A Company

 PT. Cahaya Boxindo Prasetya is a company engaged in the manufacture of carton boxes or boxes. The company's activities also include cutting and printing services using machinery and human power. The problem faced in this company is the difficulty of predicting the amount of inventory of raw materials that will be  included in the production. The remaining raw materials for production will be used as the final stock to get the minimum, the goal is to reduce excess stock Overcoming this problem, fuzzy logic is used to predict raw material inventories by focusing on the final stock. In this study using Fuzzy Sugeno, with three input variables, namely: initial inventory, purchase, production, while the output is the final stock. Determination of prediction results using defuzzification using the average concept of MAPE (Mean Absolute Percentage Error). The results obtained, using the Fuzzy Sugeno method can predict the inventory of raw materials with a MAPE value of 38%. 

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.

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