Deteksi Kepribadian Melalui Margin Pada Tulisan Tangan Menggunakan Random Forest

Abstract

Graphology in handwriting is a technique for assessing a person's personality by examining various aspects of their handwriting. Each individual's handwriting is unique and has its own characteristics, making it analyzable through a grapho-test. Graphology is used in various fields such as talent identification and employee placement. The challenges faced include the grapho-test being time-consuming, costly, and finding efficient ways to detect character through handwriting while achieving accurate results. Research applying artificial intelligence in the field of graphology has been conducted by several researchers, but most have used multiple characteristics such as margin, slant, text size, and letter shape without classifying them into a single characteristic, resulting in less accurate outcomes. Therefore, this study presents a model for applying computer vision in graphology, focusing on a single characteristic, namely margin, and classifying it into four classes: wide, narrow, widening, and narrowing. Personality is classified using the random forest method, and based on the test results, it is known that the random forest method provides 95% accuracy, demonstrating better performance and yielding accurate handwriting detection results without the need for a psychology expert.