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Reseach Article

Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System

by Sri Hartati, Shofwatul 'Uyun
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 36 - Number 6
Year of Publication: 2011
Authors: Sri Hartati, Shofwatul 'Uyun
10.5120/4499-6350

Sri Hartati, Shofwatul 'Uyun . Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System. International Journal of Computer Applications. 36, 6 ( December 2011), 38-45. DOI=10.5120/4499-6350

@article{ 10.5120/4499-6350,
author = { Sri Hartati, Shofwatul 'Uyun },
title = { Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 36 },
number = { 6 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 38-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume36/number6/4499-6350/ },
doi = { 10.5120/4499-6350 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:22:29.958395+05:30
%A Sri Hartati
%A Shofwatul 'Uyun
%T Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System
%J International Journal of Computer Applications
%@ 0975-8887
%V 36
%N 6
%P 38-45
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Determination of dietary food consumed a day for patients with diseases in general, greatly affects the health of the body and the healing process, and is no exception for people with kidney disease and urinary tract. This paper presents the determination of diet composition in the form of food substance for people with kidney and urinary tract diseases with a genetic fuzzy approach. This approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy logic fuzzy tools and techniques to model the components of the genetic algorithm and adapting genetic algorithm control parameters, with the aim of improving system performance. The Mamdani fuzzy inference model and fuzzy rules based on population parameters and generation are used to determine the probability of crossover and mutation, and was using In this study, 400 food survey data along with their substances was used as test material. From the data, a varying amount of population is established. Each chromosome has 10 genes in which the value of each gene indicates the index number of foodstuffs in the database. The fuzzy genetic approach produces 10 best food substance and their compositions. The composition of these foods has nutritional value in accordance with the number of calories needed by people with kidney and urinary tract diseases by type of food.

References
  1. Almatsier, S. 2008. Penuntun Diet. Gramedia Pustaka Utama. Jakarta
  2. Javadi, AA., Farmani, R. and Tan, TP. 2005. A Hybrid Intelligent Genetic Algorithm. Elsevier (19) : 255-262. Paris
  3. Palensky, M. and Ali, H. 2003. A Genetic Algorithm for Simplifying The Amino Acid Alphabet. IEEE Computer Society Bioinformatics Conference (CSB’03). California
  4. Suer, G.A. and Allard, D. 2009. Fuzzy genetic scheduling with single and multiple schedulers, International Journal of Advanced Operations Management . Vol 1. 80 – 107.
  5. Herrera, F. 2008. Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence 1. 27-46.
  6. Chaudhurin, A. and De K. 2010. Fuzzy Genetic Heuristic for University Course Timetable Problem. Int. J. Advance. Soft Comput. Appl., Vol. 2, No. 1, 100-121.
  7. Klir, G,J. and Yuan, S. 1995. Fuzzy Sets and Fuzzy Logic (Theory and Applications). Prentice-Hall Inc. Canada.
  8. Gen, M. and Cheng, R. 2000. Genetic Algorithms and Engineering Optimization. A Wiley-Interscience Publication. New York.
  9. Michalewics, Z. 1996. Genetic Algorithm + Data Structure = Evolution Programs. New York : Springer-Verlag 3rd edition.
  10. Ferentinos, T.A. and Tsiligiridis. 2007. Adaptive Design Optimization of Wireless Sensor Networks Using Genetic Algorithms. Computer Network; 51(4):1031-1051.
  11. Almatsier, S . 2003. Prinsip Dasar Ilmu Gizi. Gramedia Pustaka Utama. Jakarta
Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithm Fuzzy logic Fuzzy genetic Diet Kidney disease Urinary tract