Abstract-Graphology is a study of representing personality based on handwriting. Individual's handwriting is unique and has own feature that it can be analyzed to understand personality. Graphology is used in some fields such as staffing, determining interest and talent. Some researches in graphology using artificial intelligence have been studied before. However, most of the researches still used one handwriting feature and did not classify into personality type. In this study, using some features of handwriting, i.e. left margin, right margin, size, and slant to classify personality type. Personality is classified based on Myers-Briggs Type Indicator (MBTI) using Back Propagation and Learning Vector Quantization method. The result shows that Learning Vector Quantization has better performance, with 90% accuracy, than Back Propagation, which has 82% accuracy.Intisari-Grafologi adalah ilmu yang merepresentasikan kepribadian seseorang berdasarkan tulisan tangan. Tulisan tangan setiap orang bersifat unik dan memiliki karakteristiknya masing-masing, sehingga dapat dianalisis untuk memahami watak seseorang. Grafologi digunakan dalam berbagai bidang, seperti penentuan minat bakat dan penempatan karyawan. Penelitian yang menerapkan kecerdasan buatan di bidang grafologi sudah dilakukan oleh beberapa peneliti. Akan tetapi, sebagian besar masih menggunakan satu karakteristik tulisan tangan dan tidak mengklasifikasikan ke dalam salah satu tipe kepribadian. Oleh karena itu, pada makalah ini dibangun aplikasi pengenalan kepribadian berdasarkan beberapa karakterisitik tulisan tangan yaitu margin kiri, margin kanan, ukuran, kemiringan, dan bentuk huruf. Kepribadian diklasifikasikan berdasarkan Myers-Briggs Type Indicator (MBTI) menggunakan metode Back Propagation dan Learning Vector Quantitazion. Berdasarkan hasil pengujian, diketahui metode Learning Vector Quantitazion, dengan tingkat akurasi 90%, memiliki kinerja yang lebih baik dibandingkan Back Propagation yang memiliki tingkat akurasi sebesar 82%.

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In a learning process, learning style becomes one crucial factor that should be considered. However, it is still challenging to determine the learning style of the student, especially in an online learning activity. Data-driven methods such as artificial intelligence and machine learning are the latest and popular approaches for predicting the learning style. However, these methods involve complex data and attributes. It makes it quite heavy in the computational process. On the other hand, the literate based driven approach has a limitation in inconsistency between results with the learning behavior. Combination, both approaches, gives a better accuracy level. However, it still leaves some issues such as ambiguity and a wide of range of attributes value. These issues can be reduced by finding the right approach and categorization of attributes. Rough set proposed the simple way that can compromise with the ambiguity, vague, and uncertainty. Rough set generated the rules that can be used for prediction or classification decision attributes. Yet, due to the method based on categorical data, it must be careful in determining the category of attributes. Hence, this research investigated several categorizing attributes in the identification learning style. The results showed that the approach gives a better prediction of the learning style. Different categories give different results in terms of accuracy level, number of eliminated data, number of eliminated attributes, and number of generated rules criteria. For decision making, it can be considered by balancing of these criteria.

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