Detection of Children with Personality Through Fingerprint Random Forest And Maximum Entropy Method
Jannata Arianda, Burhanuddin Dirgantoro, Casi Setianingsih · 2019
Every child has a personality and learning style that can be known from birth, now has found the latest method that is by analyzing fingerprint patterns. A person's fingerprint is a very unique characteristic, because no one has the same fingerprint pattern. Some have learned that fingerprint patterns are related to a person's character.Then it will be discussed the design and discussion of a person's character system based on fingerprint patterns using the gray co-occence matrix (GLCM) for feature extraction and classified with the Random Forest and Maximum Entropy Method. The dataset in this study was taken directly to elementary school children, ranging in age from 7 to 8 years. To find out the child's personality and learning style, fingerprint patterns were taken on the middle finger, ring finger and little finger taken with ink. In this case the class will be divided into three Loop, Whorl, and Arch. From that experiment has been done, of the 123 student data or datasets that have the best accuracy results is with 95% obtained using the random forest method while for the maximum entropy method only gets an accuracy of 44%.