scholarly journals Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur

2016 ◽  
Vol 2 (3) ◽  
pp. 21-30 ◽  
Author(s):  
Zaimatun Niswati ◽  
Aulia Paramita ◽  
Fanisya Alva Mustika
Keyword(s):  
Diabetes Mellitus ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fuzzy Inference ◽  
Treatment Decision ◽  
Inference System ◽  
Family History Of Diabetes ◽  
Patients With Diabetes ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Abstract— Patients with diabetes mellitus increased from year to year. This is due to delays in diagnosis of the disease and also because of unhealthy lifestyles. This study aims to create an application of decision support systems in the field of health, namely the diagnosis of the disease Diabetes Mellitus with Fuzzy Inference System (FIS) Mamdani, so that a layman can perform early diagnosis and immediate treatment. Decision Support System Techniques developed to improve the effectiveness of decision-makers. Samples are six puskesmas in East Jakarta. This application uses five variables as inputs consisting of glucose 2 hours after a meal, Diastolic blood pressure, body mass index, family history of diabetes, total pregnancies and one variable as output. The data obtained be processed using fuzzy logic approach to programming Matlab and made Graphical User Interface (GUI). Result is an expert system for diagnosis of Diabetes Mellitus .The trial results by midwives and nurses puskesmas is 100% of these applications in accordance with the doctor”s diagnosis. It is to help improve the quality of service in the Puskesmas in East Jakarta, thus satisfying the users and puskesmas be able to compete both nationally and internationally. 

Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME

2017 ◽  
Vol 24 (4) ◽  
pp. 973-993 ◽  
Author(s):  
Rohit Agrawal ◽  
P. Asokan ◽  
S. Vinodh
Keyword(s):  
Fuzzy Logic ◽  
Lean Manufacturing ◽  
Fuzzy Inference ◽  
Evaluation Model ◽  
Medium Size ◽  
Inference System ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Leanness Evaluation

Purpose The purpose of this paper is to present a study that is focused on application of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) approaches for leanness evaluation in an Indian small- and medium-size enterprise (SME). Design/methodology/approach Lean manufacturing concepts are being adopted by SMEs to sustain in the competitive manufacturing landscape. Performance of lean system needs to be assessed using appropriate methods. A model for measuring lean performance is proposed with five enablers, 30 criteria and 90 attributes. Leanness index is computed using fuzzy logic approach and benchmarked with ANFIS approach. Findings Leanness index computed using fuzzy logic approach is found to be (4.47, 5.97, 7.55) and that of ANFIS approach is found to be 5.84 to facilitate benchmarking of leanness evaluation. After finding weaker areas, certain improvement initiatives are being deployed. Research limitations/implications The developed model for leanness evaluation has been test implemented in an SME. In future, the model could be test implemented in several SMEs. Practical implications A case study conducted in an SME involved in heavy engineering fabrication is presented. Therefore, the inferences derived from the study has practical propensity. Originality/value The development of leanness evaluation model for SMEs and deployment in an industrial scenario are the original contributions of the authors.

scholarly journals Course recommendation system using fuzzy logic approach

2020 ◽  
Vol 17 (1) ◽  
pp. 365
Author(s):  
Mohd Suffian Sulaiman ◽  
Amylia Ahamad Tamizi ◽  
Mohd Razif Shamsudin ◽  
Azri Azmi
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Education Environment ◽  
User Design ◽  
Interest Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
The Right

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.

scholarly journals Fuzzy Logic Approach for Routing in Internet of Things Network

2021 ◽  
Vol 15 (1) ◽  
pp. 18-24
Author(s):  
Reena P. Pingale ◽  
S. N. Shinde
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Network Performance ◽  
Fuzzy Inference ◽  
Residual Energy ◽  
Link Quality ◽  
Inference System ◽  
Input And Output ◽  
Fuzzy Logic Approach ◽  
Logic Approach

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%.

scholarly journals Bankruptcy Prediction Model with Risk Factors using Fuzzy Logic Approach

2021 ◽  
Vol 6 (2) ◽  
pp. 102-110
Author(s):  
Teoh Yeong Kin ◽  
Akmal Haziq Ahmad Aizam ◽  
Suzanawati Abu Hasan ◽  
Anas Fathul Ariffin ◽  
Norpah Mahat
Keyword(s):  
Risk Factors ◽  
Fuzzy Logic ◽  
Prediction Model ◽  
Fuzzy Inference ◽  
Bankruptcy Prediction ◽  
Financial Flexibility ◽  
Inference System ◽  
Financial Firms ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Forecasting bankruptcy remains crucial, especially during this pandemic. Managers, financial institutions, and government agencies rely on the information regarding an impending bankruptcy threat to make decisions. This paper developed a straightforward bankruptcy prediction model using the fuzzy logic approach for individuals and companies to evaluate their performance and analyse the tendency of getting bankrupt. A sample of 250 respondents from banks and financial firms were tested using the qualitative risk factors, namely, industrial risk, management risk, financial flexibility, credibility, competitiveness, and operational risk. This study provides a comprehensive analysis using the Fuzzy Inference System (FIS) editor in the MATLAB software, where the model's accuracy is compared to the actual results. The results show an accuracy rate of 99.20%, indicating that this approach can determine the likelihood of bankruptcy. The fuzzy logic approach can improve prediction accuracy while also guiding decision-makers in detecting and preventing possible financial crises in their early phases.

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 Detection of type of thalassemia disease in patients: A fuzzy logic approach

2016 ◽  
Vol 1 (02) ◽  
pp. 88-95 ◽  
Author(s):  
S. Thakur ◽  
S. N. Raw ◽  
R. Sharma ◽  
P. Mishra
Keyword(s):  
Mean Corpuscular Volume ◽  
Fuzzy Inference ◽  
Mean Corpuscular Hemoglobin ◽  
End User ◽  
Corpuscular Hemoglobin ◽  
Inference System ◽  
Fuzzy Logic Approach ◽  
Rbc Indices ◽  
Logic Approach ◽  
Mamdani Fuzzy Inference System

In this paper, we have determined the severity of Thalassemia disease in a patient with the help of their Red Blood Cell (RBC) indices components such as Mean Corpuscular Hemoglobin (MCH) and Mean corpuscular volume (MCV). Also level of blood (Hemoglobin) is considered. We use a fuzzy application, the Mamdani Fuzzy Inference System (FIS) to generate a model for Thalassemia diagnosis. Obtained model is applied on set of data such that 15 results are similar and 3 are marginally off. It shows that the accuracy of the proposed system is 83.3%. Sensitivity analysis is carried out the result of which shows that the developed Thalassemia diagnosis model is more stable. From the viewpoint of an end-user, the results of this work can facilitate laboratory work by reducing the time and cost.

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