SIGNATURE ANALYSIS FOR PERSONAL CHARACTERISTICS PREDICTION USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD

  • Udkhiati Mawaddah ISTTS Surabaya
  • Hendrawan Armanto Institut Sains dan Teknologi Terpadu Surabaya
  • Endang Setyati Institut Sains dan Teknologi Terpadu Surabaya
https://doi.org/10.35457/antivirus.v15i1.1526
Abstract views: 903 , PDF downloads: 1172
Keywords: classification, graphology, hand signature, CNN

Abstract

Graphology is the study of handwriting that can describe the characteristics of a writer and his emotional disposition. Knowing the characteristics of prospective applicants is very important for the Human Resource Development (HRD) that responsible for selecting employees in their fields. HRD often experienced the Mistaken when in the process of hiring employees who identify the candidate employee signature to lose both time and costs in that company. This research using 7 signature features which are divided into two algorithms respectively, 5 signature features consisting are Curved Start, End Streak, Shell, Middle Streaks, Underline and Identification Structure Algorithm consist 2 signature features are Dot Structure and Streaks disconnected. The evaluation results obtained a training data accuracy value of 0.7333, training data loss of 0.7693, test data accuracy of 0.7778, and test data loss of 0.8377 which can be concluded that the results of the two data is underfitting. Thus, we must concern to collecting other dataset which has features similarity in every classes.

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Published
2021-06-10
How to Cite
[1]
U. Mawaddah, H. Armanto, and E. Setyati, “SIGNATURE ANALYSIS FOR PERSONAL CHARACTERISTICS PREDICTION USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD”, antivirus, vol. 15, no. 1, pp. 123-133, Jun. 2021.
Issue
Vol. 15 No. 1 (2021): Mei 2021
Section
Articles

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