Algoritma Genetika untuk Optimasi Komposisi Makanan Bagi Penderita Hipertensi

Genetic Algorithm for Optimizing Food Composition for Hypertension Patients

Anggi Mahadika Purnomo -  Faculty of Computer Science, Universitas Brawijaya, Indonesia
Davia Werdiastu -  Faculty of Computer Science, Universitas Brawijaya, Indonesia
Talitha Raissa -  Faculty of Computer Science, Universitas Brawijaya, Indonesia
Restu Widodo -  Faculty of Computer Science, Universitas Brawijaya, Indonesia
*Vivi Nur Wijayaningrum -  Faculty of Computer Science, Universitas Brawijaya, Indonesia
Received: 27 Oct 2018; Revised: 13 Jan 2019; Accepted: 30 Jan 2019; Published: 31 Jan 2019; Available online: 31 Mar 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Abstract
Hypertension can be prevented and handled by eating nutritious foods with the right composition. The genetic algorithm can be used to optimize the food composition for people with hypertension. Data used include sex, age, weight, height, activity type, stress level, and patient hypertension level. This study uses a reproduction method that is good enough to be applied to integer chromosome representations so that the search results provided are not local optimum solutions. The testing results show that the best genetic algorithm parameters are as follows population size is 15 with average fitness 20.97, the generation number is 40 with average fitness 50.10, and combination crossover rate and mutation rate are 0.3 and 0.7 with average fitness 41.67. The solution obtained is the optimal food composition for people with hypertension.

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Keywords
genetic algorithm; hypertension; food composition; stroke; high blood pressure

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  1. H. Nguyen, O. A. Odelola, J. Rangaswami, and A. Amanullah, “A Review of Nutritional Factors in Hypertension Management,” International Journal of Hypertension, vol. 2013, pp. 1-12, 2013.
  2. M. Ardiansyah, Medikal Bedah untuk Mahasiswa. Yogyakarta: Diva Press, 2012.
  3. M. Puspitorini, Hipertensi Cara Mudah Mengatasi Tekanan Darah Tinggi. Yogyakarta: Image Press, 2009.
  4. H. Okubo, S. Sasaki, K. Murakami, T. Yokoyama, N. Hirota, A. Notsu, M. Fukui, and C. Date, “Designing Optimal Food Intake Patterns to Achieve Nutritional Goals for Japanese Adults through the use of Linear Programming Optimization Models,” Nutrition Journal, vol. 14, no. 57, pp. 1-10, 2015.
  5. A. C. Iwuji, M. Nnanna, and N. I. C. Ndulue, “An Optimal DASH Diet Model for People with Hypertension Using Linear Programming Approach,” Open Journal of Optimization, vol. 5, no. 1, pp. 14-21, 2016.
  6. F. Zhang and W. B. Roush, “Multiple-objective (Goal) Programming Model for Feed Formulation: An Example For Reducing Nutrient Variation,” Poultry Science, vol. 81, no. 2, pp. 182-192, 2002.
  7. M. A. Wolters, “A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening,” Journal of Statistical Software, vol. 47, no. 6, pp. 1-23, 2012.
  8. V. N. Wijayaningrum and F. Utaminingrum, “Numerical Methods for Initialization in Fodder Composition Optimization,” in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, Indonesia, Oct. 2016, pp. 397-400.
  9. V. N. Wijayaningrum, W. F. Mahmudy, and M. H. Natsir, “Optimization of Poultry Feed Composition Using Hybrid Adaptive Genetic Algorithm and Simulated Annealing,” Journal of Telecommunication, Electronic, and Computer Engineering, vol. 9, no. 2–8, pp. 183-187, 2017.
  10. F. Ramadhani, F. A. Fathurrachman, R. Fitriawanti, A. C. Rongre, and V. N. Wijayaningrum, “Optimasi Pendistribusian Barang Farmasi Menggunakan Algoritma Genetika,” Kumpulan Jurnal Ilmu Komputer (KLIK), vol. 5, no. 2, pp. 159-168, 2018.
  11. A. Kartikasari, D. E. Ratnawati, and T. S. Kusuma, “Optimasi Komposisi Makanan untuk Penderita Hipertensi Menggunakan Algoritma Genetika dan Simulated Annealing,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 11, pp. 1236-1243, 2017.
  12. A. J. Umbarkar and P. D. Sheth, “Crossover Operators in Genetic Algorithms: A Review,” ICTACT Journal on Soft Computing, vol. 6, no. 1, pp. 1083-1092, 2015.
  13. A. Hussain, Y. S. Muhammad, M. N. Sajid, I. Hussain, A. M. Shoukry, and S. Gani, “Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator,” Computational Intelligence and Neuroscience, vol. 2017, no. 1, pp. 1-7, 2017.
  14. B. Sutomo, Menu Sehat Penakluk Hipertensi. Jakarta: DeMedia Pustaka, 2009.
  15. W. Welis and M. S. Rifki, Gizi untuk Aktifitas Fisik dan Kebugaran. Padang: Sukabina Press, 2013.
  16. Cornelia, E. Sumedi, I. Anwar, R. Ramayulis, S. Iwaningsih, T. Kresnawan, and H. Nurlita, Konseling Gizi. Jakarta: Penebar Plus+, 2013.
  17. N. Devi, Nutrition and Food: Gizi untuk Keluarga. Jakarta: Kompas, 2010.
  18. M. Mehra, M. L. Jayalal, A. J. Arul, S. Rajeswari, K. K. Kuriakose, and S. A. V. S. Murty, “Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants,” International Journal of Intelligent Systems and Applications, vol. 6, no. 1, pp. 20-28, 2014.
  19. M. Shafaat, I. Cholissodin, and E. Santoso, “Optimasi Komposisi Makanan Diet bagi Penderita Hipertensi menggunakan Algoritme Genetika,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 1, pp. 226-236, 2018.