scholarly journals Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM

2013 ◽  
Vol 7 (1) ◽  
pp. 45
Author(s):  
Nur Hasanah ◽  
Retantyo Wardoyo
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inference Engine ◽  
Rule Based ◽  
Knowledge Based ◽  
Menu Planning ◽  
Model Complex ◽  
Uncertainty Method ◽  
Management Expert ◽  
Diabetes Melitus

AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product.            Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu  AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average.            The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

scholarly journals An Application Expert System for Evaluating Effective Factors on Trust in B2C WebsitesTrust, Security, ANFIS, Fuzzy Logic, Rule Based Systems, Electronic Commerce

Engineering ◽  
2011 ◽  
Vol 03 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Karamollah Bagherifard ◽  
Othman Ibrahim ◽  
Nasim Janahmadi ◽  
Mousa Barisami
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Electronic Commerce ◽  
Rule Based ◽  
Effective Factors ◽  
Logic Rule ◽  
Rule Based Systems

ICOT: An Integrated C-Object Tool for Knowledge-Based Programming

1998 ◽  
Vol 06 (03) ◽  
pp. 273-293 ◽  
Author(s):  
Hyung Jeong Yang ◽  
Jae Dong Yang ◽  
Yeongho Kim
Keyword(s):  
Expert System ◽  
Object Oriented ◽  
Fuzzy Information ◽  
Major Drawback ◽  
Rule Based ◽  
Knowledge Based ◽  
Complex Engineering ◽  
Knowledge Based Engineering ◽  
Current Rule ◽  
New Framework

In this paper, an Integrated C-Object Tool, namely ICOT, is proposed for knowledge-based programming. A major drawback of current rule-based expert system languages is that they have difficulty in handling composite objects as a unit of inference. An object-oriented model is a powerful alternative to complement the drawback. Each of these alone cannot capture all the semantics of knowledge, particularly in complex engineering domains. For a knowledge-based approach to be effective, both the object-oriented paradigm and the rule-based mechanism may need to be integrated into one framework. The framework may also need to support manipulation of fuzzy knowledge to model the real world as close as possible. Three types of fuzzy information are identified, and a proper way of representing and inferencing them is developed. ICOT provides a new framework into which rule-based deduction, object-oriented modeling, and fuzzy inferencing are combined altogether. This can become especially useful for developing knowledge-based engineering applications.

Knowledge-based systems as decision support tools in an ecosystem approach to fisheries: Comparing a fuzzy-logic and a rule-based approach

2008 ◽  
Vol 79 (2-4) ◽  
pp. 390-400 ◽  
Author(s):  
Astrid Jarre ◽  
Barbara Paterson ◽  
Coleen L. Moloney ◽  
David C.M. Miller ◽  
John G. Field ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Ecosystem Approach ◽  
Decision Support Tools ◽  
Knowledge Based Systems ◽  
Rule Based ◽  
Knowledge Based ◽  
Support Tools ◽  
Rule Based Approach

scholarly journals Rule Based and Supervisory Training Approach To Develop Expert System Tool For Detecting Lung Cancer Disease

2010 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
K. Balachandran ◽  
R. Anitha
Keyword(s):  
Lung Cancer ◽  
Expert System ◽  
Expert Systems ◽  
Medical Attention ◽  
Cancer Disease ◽  
Rule Based ◽  
Knowledge Based ◽  
Training Approach ◽  
Dreaded Disease ◽  
Rule Based Approach

Knowledge-based expert systems, or expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Lung cancer is one of the dreaded disease in the modern era. It is responsible for the most cancer deaths in both men and women throughout the world. Early diagnosis and timely treatment are imperative for the cure. Longevity and cure depends on early detection. This paper gives on insight to identify the forget group of people who are suffering or susceptible to suffer lung cancer disease. Seeking proper medical attention con be initiated based on the findings. Expert system tool developed, to find this target group based on the non-clinical parameters. Symptoms and risk factors associated with Lung cancer ore token as the basis of this study. This expert system basically works on the rule based approach to collect the data. Then Supervisory learning approach is used to infer the basic data. Once sufficient knowledge base is generated the system can be made to adopt in unsupervised learning mode.

scholarly journals Sistem Pakar Diagnosa Penyakit THT Menggunakan Metode Dempster Shafer

Repositor ◽  
2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Doni Yulianto ◽  
Yufiz Azhar ◽  
Nur Hayatin
Keyword(s):  
Expert System ◽  
Search Method ◽  
Rule Based ◽  
Forward Chaining ◽  
Lack Of Information ◽  
The Subject ◽  
The Right ◽  
Uncertainty Method ◽  
Development Step ◽  
Nose And Throat

AbstrakBerbagai penyakit pada manusia dapat menimbulkan masalah serius jika tidak cepat ditangani, seperti halnya penyakit THT (Telinga, Hidung, dan Tenggorokan). Penderita penyakit THT di Indonesia cukup tinggi, karena masyarakat sering menganggap remeh penyakit THT dan kurangnya informasi mengenai penyakit tersebut. Perlu adanya sistem yang memberikan informasi mengenai gejala pada penyakit THT dan jenis penyakit apa saja yang diderita, serta solusi apa yang tepat untuk menangani penyakit THT. Subjek dalam penelitian ini adalah sistem pakar untuk mendiagnosa penyakit THT. Pada penelitian ini menggunakan dua metode, yaitu metode ketidakpastian menggunakan Dempster Shafer dan metode penelusuran yaitu Forward Chaining. Langkah pengembangan diawali dari pengumpulan data, lalu pembuatan Rule Based, mengimplementasikan metode, dan melakukan pengujian akurasi pakar. Hasil penelitan ini adalah sistem pakar mendiagnosa penyakit THT sebanyak 7 jenis penyakit dengan gejala sebanyak 24 jenis. Penelitian ini juga menggunakan metode Dempster Shafer untuk mendapatkan nilai kepastian berupa persentase nilai kepastian pada hasil diagnosa penyakitnya. Berdasarkan hasil pengujian pakar, dapat disimpulkan bahwa sistem pakar memiliki tingkat kesamaan dengan pakar sebesar 85% yang berarti bahwa sistem pakar ini layak untuk digunakan.AbstractVarious diseases in humans can cause serious problems if not quickly handled, such as ENT diseases (ear, nose, and throat). People with ENT disease in Indonesia is quite high, because people often consider the condition of ENT disease and lack of information about the disease. There is a system that provides information about the symptoms in ENT diseases and what types of diseases suffered, as well as what is the right solution to handle ENT diseases. The subject in this study is an expert system for diagnosing ENT diseases. The study used two methods, namely the uncertainty method using Putty Shafer and the search method that is Forward Chaining. The development step starts from collecting data, then creating a Rule Based, implementing methods, and conducting expert accuracy testing. The results of this research is a system of experts diagnose ENT diseases as many as 7 types of diseases with the symptoms as much as 24 types. This research also uses the method of putty Shafer to get certainty of the percentage value of certainty in the diagnosis of diseases. Based on expert testing results, it can be concluded that an expert system has a level of similarity with experts at 85% which means that the expert system is worthy of use.

KNOWLEDGE EXTRACTION FROM FALL-TIME AUTO-CORRELATED PATTERNS BY USING NEURAL RULE BASED EXPERT SYSTEM

2006 ◽  
Vol 03 (01) ◽  
pp. 15-24 ◽  
Author(s):  
NAZAR ELFADIL ◽  
INTISAR IBRAHIM
Keyword(s):  
Expert System ◽  
Correlated Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Rule Based ◽  
Fall Time ◽  
Knowledge Based ◽  
Correlated Patterns ◽  
Automated Knowledge ◽  
Learning Data

In this paper, the author presents an approach for automated knowledge acquisition system using Kohonen self-organizing maps and k-means clustering. The extracted knowledge in terms of rules are used as knowledge base for a rule based expert system. For the sake of illustrating and validating the system overall architecture, a fall-time auto-correlated data patterns has been used as a learning data set. The verification of the produced knowledge based was conducted by conventional expert system.

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