MEMPREDIKSI JUMLAH PRODUKSI ULOS BATAK DENGAN MENGGUNAKAN LOGIKA FUZZY DENGAN METODE TSUKAMOTO (Studi Kasus Cv. Ala Dos Roha)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Sri Handayani Sianipar ◽  
Fince Tinus Waruwu ◽  
Lince Tomoria Sianturi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Method ◽  
Production Data ◽  
Speed Up

Ulos batak toba is one of indonesia traditional fabric, precisely the traditional cloth of the batak toba. From time to time the ulos fabric was growing in terms of  type and motif. One of the companies that produces ulos batak is cv. Ala dos roha. The authors conducted this study aimed at predicting the amount of production of ulos batak to produced later. The author uses the previous request, inventory and production data using fuzzy logic tsukamoto. The final result of the calculation with this method will be more effective and efficient so as to speed up the decision making time to predict the amount of production to be produced next.Keywords: prediction, amount of  production, method of tsukamoto

DEVELOPMENT OF MODELS FOR DIAGNOSTIC DIABETES BASED ON FUZZY LOGIC METODS

2021 ◽  
pp. 90-95
Author(s):  
Евгений Николаевич Коровин ◽  
Екатерина Ивановна Новикова ◽  
Олег Валерьевич Родионов
Keyword(s):  
Mathematical Modeling ◽  
Decision Making ◽  
Information Technology ◽  
Fuzzy Logic ◽  
Human Body ◽  
Patient Information ◽  
Decision Making Process ◽  
Speed Up ◽  
Definition Of

В статье рассматриваются разработки методов интеллектуальной поддержки процесса диагностики сахарного диабета, а также определение его типа. В последние годы количество людей, страдающих данным заболеванием, неуклонно растет, а без своевременной диагностики эта патология может нанести огромный вред организму человека. Сахарный диабет 1 типа опасен тем, что в основном возникает у людей молодого возраста. Оперативное обнаружение диабета, а также определение его типа, поможет не только избежать возможных осложнений, но и в некоторых случаях предотвратить смерть пациента. Информационные технологии все чаще используются в различных сферах деятельности для разработки новых или совершенствования существующих методов обработки данных, особенно это можно заметить в сфере медицины. В настоящее время врач самостоятельно ставит диагноз, основываясь на результатах различных анализов, однако, для ускорения процесса принятия решения, можно воспользоваться методами математического моделирования, а именно: моделями диагностики диабета на основе нечеткой логики. Для наибольшего удобства данный способ распознавания заболевания впоследствии можно реализовать в информационно-программное обеспечение, которое сможет еще больше увеличить эффективность и скорость распознавания патологии The article discusses the issues of the incidence of diabetes in the population, in particular, the definition of its type. In recent years, the number of people suffering from this disease has been steadily growing, and without timely diagnosis, this pathology can cause enormous harm to the human body. Prompt detection of diabetes, as well as determination of its type, will help not only avoid possible complications, but also in some cases prevent the death of the patient. Information technology is increasingly being used in various fields of activity to develop new or improve existing methods of data processing, especially in the field of medicine. Currently, the doctor independently makes a diagnosis based on the results of various analyzes, however, to speed up the decision-making process, you can use the methods of mathematical modeling, namely, models of diabetes diagnostics based on fuzzy logic. For the greatest convenience, this method of disease recognition can subsequently be implemented in information software, which can further increase the efficiency and speed of pathology recognition

Fuzzy Logic Models for Non-Programmed Decision-Making in Personnel Selection Processes Based on Gamification

Informatica ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Javier Albadán ◽  
Paulo Gaona ◽  
Carlos Montenegro ◽  
Rubén González-Crespo ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Personnel Selection ◽  
Logic Models ◽  
Selection Processes

Pengembangan Aplikasi Pendukung Keputusan Pemilihan Program Studi dengan Metode Fuzzy Logic (Studi Kasus: Universitas Multimedia Nusantara)

2016 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Joko Haryanto ◽  
Seng Hansun
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Application ◽  
Prospective Students ◽  
Fuzzy Logic Method ◽  
Index Terms ◽  
Selection Of

This paper describes the development of decision support system application to assist students who want to enter college so that no one choose the majors incorrectly. This application uses fuzzy logic method because fuzzy logic is very flexible in data which are vague and can be represented as a linguistic variable. The purpose of this application is to assist students to choose available majors at University Multimedia Nusantara which are appropriate with his/her capabilities. This application accepts five kinds of input values i.e. Mathematics, Indonesian, English, Physics, and TIK. Received input will be processed by the calculation of the system for decision-making and the application will generate output that shows how great a match for each majors. With this application, prospective students can find out where the majors that match his/her capabilities. This application has ninety nine percentage of match result accuracy. Index Terms—fuzzy logic, decision support system, UMN, selection of major

scholarly journals Application of Interval Fuzzy Logic in Selecting a Sustainable Supplier on the Example of Agricultural Production

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 774
Author(s):  
Adis Puška ◽  
Miroslav Nedeljković ◽  
Sarfaraz Hashemkhani Zolfani ◽  
Dragan Pamučar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Asymmetric Information ◽  
Agricultural Production ◽  
Complete Information ◽  
Sustainable Supply Chain ◽  
Sustainability Criteria ◽  
Criteria Importance ◽  
Selection Of ◽  
Economic Criteria

The selection of sustainable suppliers (SSS) is the first step in applying a sustainable supply chain and sustainable production. Therefore, it is necessary to select the supplier that best meets the set sustainability criteria. However, the selection of suppliers cannot be done by applying symmetric information, because the company does not have complete information, so asymmetric information should be used when selecting suppliers. Since the SSS applies three main sustainability criteria, environmental, social, and economic criteria, this decision-making problem is solved by applying multi-criteria decision-making (MCDM). In order to solve the SSS for the needs of agricultural production, interval fuzzy logic was applied in this research, and six suppliers with whom agricultural pharmacies in Semberija work were taken into consideration. The application of interval fuzzy logic was performed using the methods PIPRECIA (Pivot pairwise relative criteria importance assessment) and MABAC (Multi-Attributive Border Approximation Area Comparison). Using the PIPRECIA method, the weights of criteria and sub-criteria were determined. Results of this method showed that the most significant are economic criteria, followed by the social criteria. The ecological criteria are the least important. The supplier ranking was performed using the MABAC method. The results showed that supplier A4 best meets the sustainability criteria, while supplier A6 is the worst. These results were confirmed using other MCDM methods, followed by the sensitivity analysis. According to the attained results, agricultural producers from Semberija should buy the most products from suppliers A4, in order to better apply sustainability in production. This paper showed how to decision make when there is asymmetric information about suppliers.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

A Kalman filtering fuzzy logic algorithm for recognition of lane departure

2021 ◽  
pp. 1-8
Author(s):  
Kai Ren
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Kalman Filtering ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Fuzzy Logic Algorithm ◽  
The Road ◽  
Departure Decision ◽  
Lane Departure ◽  
Fusion Analysis

In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.

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