scholarly journals Implementasi Fuzzy Tsukamoto Dalam Menentukan Upah Gaji Karyawan Perbulan

2020 ◽  
Vol 1 (2) ◽  
pp. 105-110
Author(s):  
Siti Maysaroh Saragih ◽  
Ayu Lestari ◽  
Mahadir Soleh Hutasuhut
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Fuzzy Inference ◽  
A Company ◽  
Central Statistics

In a company, salary is a salary for employees who have been working for a month. However, in order to provide fair salary to all employees, the company must determine the criteria for providing salary. By using fuzzy logic, wages can be determined by going through the following stages: Fuzzification, Formation of the knowledge base, Fuzzy Inference, and Defuzzification. One of the fuzzy logic methods that can be used is the Tsukamoto method, where this method has an output in the form of firm values. To determine the salary, the data is collected from the Central Statistics Agency website in accordance with the criteria to be examined. With this research, employers can use the calculations from this research to determine salary salaries for their employees quickly, well, and precisely. So that the problem of determining the wages of their employees' salaries can be resolved properly.

scholarly journals PENENTUAN JUMLAH PRODUKSI SHAMPO DENGAN FUZZY INFERENCE SYSTEM SUGENO: STUDI KASUS PT. GUARDIAN PHARMATAMA TANGERANG

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Ri Sabti Septarini
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Production Plan ◽  
Inference System ◽  
Fuzzy Variables ◽  
Fuzzy Input ◽  
A Company

ABSTRACTHuman are always faced with taking a decision. It also happens to a company in the process of determining which employees. In determination the production plan required a lot of considerations in case of taking decisions. Beside that, the number of employees in a company is to determine who get the production plan of the achievement. System is made to determine employees who will get benefits achievement based on the some criteria have been determined by the company. These criterias will be used as fuzzy input which also process a called fuzzy variables. In this research will construct decision support system by using fuzzy logic with fuzzy variables input that are productivity, quality tabbing and discipline. In of fuzzy logic method there are three stages, namely stage fuzzification, inference and deffuzification. At this stage of the fuzzy inference used the Sugeno method. The results of this experiments has performed that the system is able to display the production planning data for the calculation of the value of production that have been determined based on fuzzy logic with fuzzy variables. Keyword: Decision Support System, Fuzzy Logic,  Sugeno.

scholarly journals Conceptualization, Definition and Assessment of Internal Logistics through Different Approaches Using Artificial Intelligence

2020 ◽  
Author(s):  
Orlem Pinheiro de Lima ◽  
Sandro Breval Santiago ◽  
Carlos Manuel Rodríguez Taboada ◽  
Jorge Laureano Moya Rodríguez ◽  
Maida Bárbara Reyes Rodríguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Mathematical Tool ◽  
Computing Systems ◽  
Inference System ◽  
Processing Information ◽  
Definition Of ◽  
Human Brains ◽  
A Company

The aim of this chapter is to develop a new concept of internal logistics, its components parts and how to evaluate it. To quantify the level of performance of the internal logistics of a company is an important issue to gain competitiveness. There are few papers now at days that analyze how to quantify this issue. In recent years, it has been developed numerous applications of Fuzzy logic and Neural Networks to solve diverse problems of Engineering. Fuzzy logic is a mathematical tool that emulates the method used for humans for managing and processing information and Neural Networks are computing systems inspired by the biological neural networks that constitute human brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules. This chapter offers a new definition of internal logistics and shows the procedure to evaluate its level in a company. This procedure for assessing the internal logistics was developed through an Excel tab, a fuzzy inference system and a neural network. To validate this procedure, it was applied to 93 companies in the Industrial Pole of Manaus. Results obtained by different approaches are very similar, demonstrating the validity of the procedure developed.

scholarly journals Implementasi Fuzzy Tsukamoto Dalam Menentukan Harga Gabah Pada Petani

2020 ◽  
Vol 1 (2) ◽  
pp. 121-125
Author(s):  
Josua Fernando Simanjuntak ◽  
Agnes Prawita Sari ◽  
Aulia Nada Syahputri
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Knowledge Base ◽  
Grain Quality ◽  
Fuzzy Inference ◽  
Human Life ◽  
Selling Price ◽  
Rice Farmers ◽  
Base Formation

In human life, many things require decision making, including in agriculture. One of them is rice farmers who make the decision to determine the selling price of their grain according to the quality of their grain. By using Fuzzy logic, the grain price can be determined by going through the following stages: Fuzzification, Knowledge Base Formation, Fuzzy Inference, and Defuzzification. One of the Fuzzy logic methods that can be used is the Tsukamoto method, where this method has an output in the form of firm values. To be able to determine the price of grain, the data is taken from the Central Statistics Agency website, so that later prices and levels of grain quality can be determined properly. With this research, the farmers can determine the price of their grain exactly according to the quality of the grain. So that the problem of determining their grain prices can be overcome properly.

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 New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

2021 ◽  
Vol 5 (2) ◽  
pp. 33
Author(s):  
Marylu L. Lagunes ◽  
Oscar Castillo ◽  
Fevrier Valdez ◽  
Jose Soria ◽  
Patricia Melin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Dynamic Parameter ◽  
Mathematical Concept ◽  
Parameter Adaptation ◽  
Inference Models ◽  
Fractal Particles ◽  
Novel Method ◽  
Dynamic Stochastic ◽  
Stochastic Fractal Search

Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

scholarly journals Anfis to Detect Brain Tumor Using MRI

2018 ◽  
Vol 7 (3.27) ◽  
pp. 209
Author(s):  
Susmita Mishra ◽  
M Prakash ◽  
A Hafsa ◽  
G Anchana
Keyword(s):  
Fuzzy Logic ◽  
Brain Tumor ◽  
Graphical Representation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
Histogram Equalization ◽  
Input Image ◽  
Inference System ◽  
Magnetic Resonance Imaging Mri ◽  
Contrast Stretching

Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage. 

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 Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

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