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Journal : International Journal of Electrical and Computer Engineering

Mobile Decision Support System to Determine Toddler's Nutrition Using Fuzzy Sugeno Suharjito Suharjito; Jimmy Jimmy; Abba Suganda Girsang
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (13.601 KB) | DOI: 10.11591/ijece.v7i6.pp3683-3691

Abstract

Determination of nutritional status is closely related to the determination of dietary patterns should be given to infants. Nutrition is very important role in mental, physical development, and human productivity. In this study, the system based on android is developed to determine the nutritional status of infants by using Fuzzy Sugeno. Indicator variables are age, height, circle head, and body weight according to the male or female. In this study, the results of measurements of nutritional status of children with Fuzzy Sugenoare tested by comparing the nutritional quality of the data Posyandu toddler by using anthropometric tables. The results of the evaluation measurement accuracy in this application are compared with the results of manual calculation based infant growth charts according to WHO standards. Therefore, these applications can be used to help the community in monitoring the nutritional status of children so that the growth of children is more appropriate in line with expectations.
Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint Suharjito Suharjito; Bahtiar Imran; Abba Suganda Girsang
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (12.646 KB) | DOI: 10.11591/ijece.v7i5.pp2738-2745

Abstract

This study aims to find out the relations correspondence by using Gray Level Co-occurrence Matrix (GLCM) feature on parents and children finger print. The analysis is conducted by using the finger print of parents and family in one family There are 30 families used as sample with 3 finger print consists of mothers, fathers, and children finger print. Fingerprints data were taken by fingerprint digital persona u are u 4500 SDK. Data analysis is conducted by finding the correlation value between parents and children fingerprint by using correlation coefficient that gained from extract feature GLCM, both for similar family and different family. The study shows that the use of GLCM Extract Feature, normality data, and Correlation Coefficient could identify the correspondence relations between parents and children fingerprint on similar and different family. GLCM with four features (correlation, homogeneity, energy and contrast) are used to give good result. The four sides (0o, 45o, 90o and 135o) are used. It shows that side 0o gives the higher accurate identification compared to other sides.