Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Shinta Anggun Larasati
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimasi Komposisi Makanan Bagi Penderita Obesitas Pada Orang Dewasa Menggunakan Algoritme Particle Swarm Optimization (PSO) Shinta Anggun Larasati; Imam Cholissodin; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1433.089 KB)

Abstract

Obesity occurs due to buildup of fat in the body is very high. Thus causing weight gain be not ideal. Obesity can also cause disease complications, some of which can endanger lives. To get the ideal body weight and the minimum cost incurred, the patient needs to control the amount of food intake is by regulating the composition of food that enters the body. The research was done by optimizing food composition for obese people in adults using Particle Swarm Optimization Algorithm (PSO). In this study, the initial particle formation of the particle random based on the amount of food so there is no need to convert that into food index. The results displayed by the program is actual body weight, ideal weight, nutritional status, energy needs, the needs of protein, fat and carbohydrate needs needs. While the test results obtained the optimal parameters such as the number of particles = 80, the number of iterations based on testing convergence of 703, = 0,4, = 0,7 c1i = 1,5 and c1f = 0,3, c2i = 0,3 and c2f = 1,5. The results of the program with the first patient parameters produce an average difference between the actual data with the data from the program registration -2,08% and it can reduce the cost of expenditure up to 6,85%. While the second patient the average of the actual data difference with data from the program amounted to -1,06% and it can reduce the cost of expenditure up to 5,93%.
Co-Authors Imam Cholissodin Marji Marji