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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science
Wijaya, Waskitha
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Personality Analysis through Handwriting Detection Using Android Based Mobile Device Wijaya, Waskitha; Tolle, Herman; Utaminingrum3, Fitri
Journal of Information Technology and Computer Science Vol. 2 No. 2: November 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (816.519 KB) | DOI: 10.25126/jitecs.20172237

Abstract

Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of one’s personality through graphology. In addition, by using android based mobile device, graphology analysis could show one’s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.
Metode Grid-Blok Untuk Deteksi Margin Kiri Tulisan Tangan Pada Aplikasi Grafologi wijaya, waskitha; Tolle, Herman; Utaminingrum, Fitri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (393.474 KB) | DOI: 10.25126/jtiik.201851575

Abstract

Grafologi merupakan salah satu cabang ilmu psikologi yang khusus mempelajari tentang tulisan tangan. Melalui grafologi bisa diperoleh informasi tentang karakter kepribadian seseorang. Melalui perangkat mobile berbasis android, analisis grafologi akan menjadi lebih cepat dalam menampilkan pendekatan karakter kepribadian seseorang. Penelitian dilakukan dengan mengambil 42 sampel tulisan tangan dari orang yang memiliki perbedaan latar belakang. Fitur yang digunakan dalam penelitian ini adalah margin kiri pada tulisan tangan. Metode Support Vector Machine digunakan untuk mengklasifikasikan fitur hasil dari proses ekstraksi. Menggunakan metode baru yaitu Grid-double block dengan satu kali proses menghasilkan rata – rata akurasi margin kiri sebesar 69%.
Co-Authors Herman Tolle Utaminingrum, Fitri