Detection of Children's Personality with Fingerprint Using K-Nearest Neighbor (Knn) and Decision Tree Methods
Ziqra Haniffah, Burhanuddin Dirgantoro, Casi Setianingsih · 2019
Now in many parts of the world technology has been developed that is able to identify individuals from individual biological characters known as Biometrics. Biometrics itself is a way of identifying and verifying individuals based on their physical characteristics or behavior. So fingerprints are an option to detect a child's personality. The desire of parents to print their children into superior seeds is getting bigger. Questions about how to maximize talent, potential, and children from the start often haunt the minds of today's parents. Realizing the importance of this, psychologists continue to perfect tests to analyze children's intelligence and personality. With the occurrence of these problems, this study will design a system that can read fingerprints with the results knowing the child's personality and Learning style. This system is designed by using the Gray Level Co-Occurance (GLCM) feature extraction and is classified by the K-Nearest Neighbor (KNN) Method and Decision Tree which can go through a data or a fact that moves forward to a conclusion. In this research, the two classification methods have different accuracy, KNN has an accuracy of 85% and 89% Decision Tree has more accuracy than KNN because it uses a decision tree.