Hybrid System for Palm Line Detection and Educational Health Prediction Using Certainty Factor Method

Erwin Panggabean; Wira Apriani; Nuraisana Nuraisana; Penda Sudarto Hasugian

Authors

Erwin Panggabean Program Studi Teknologi Informasi,STMIK Pleita Nusantara,Jln. Iskandar Muda No. 1, Medan, Indonesia
Wira Apriani Program Studi Teknologi Informasi, STMIK Pleita Nusantara,Jln. Iskandar Muda No. 1, Medan, Indonesia
Nuraisana Nuraisana Program Studi Teknologi Informasi, STMIK Pleita Nusantara,Jln. Iskandar Muda No. 1, Medan, Indonesia
Penda Sudarto Hasugian Program Studi Teknologi Informasi, STMIK Pleita Nusantara,Jln. Iskandar Muda No. 1, Medan, Indonesia

Keywords:

Palm Line Detection, Laptop Camera, Certainty Factor, Educational Expert System, Non-Medical Prediction, Visual Analysis of Palm Lines.

Abstract

The difficulty in understanding individual characteristics based on palm lines is still an attraction in the context of education and technology-based experiments. This study aims to develop an educational application that is able to detect palm lines using a laptop camera, then predict certain characters or conditions based on the input. This system is built using the Certainty Factor (CF) method to provide certainty-based inferences on the visual symptoms of the detected palm lines. The process begins with taking a picture of the hand directly through the camera, followed by detection of main lines such as the life line, head line, and heart line using simple image processing techniques. After that, the system will display symptom-based questions related to the shape of the visible palm lines, then calculate the certainty value of the inference results using CF. This application is non-commercial and was developed as an educational tool to introduce the basic concepts of expert systems and Python-based visual processing. The system has successfully detected major palm lines with an accuracy of 80% under standard lighting conditions, and produced predictive results with certainty values that matched expected outcomes in over 70% of test cases. This demonstrates the potential of the CF method in processing visual data for educational inference. The system functions reliably as an educational tool, successfully demonstrating how certainty-based logic can be applied to simple visual data, and has been well-received in testing scenarios for learning purposes.

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