SIGNATURE ANALYSIS FOR PERSONAL CHARACTERISTICS PREDICTION USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD
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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References
E. C. Djamal, R. Darmawati, and S. N. Ramdlan, “Application image processing to predict personality based on structure of handwriting and signature,” Proceeding - 2013 Int. Conf. Comput. Control. Informatics Its Appl. “Recent Challenges Comput. Control Informatics”, IC3INA 2013, pp. 163–168, 2013, doi: 10.1109/IC3INA.2013.6819167.
S. Prasad, V. K. Singh, and A. Sapre, “Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine,” Int. J. Comput. Appl., vol. 8, no. 12, pp. 25–29, 2010, doi: 10.5120/1256-1758.
H. N. Champa and D. K. R. AnandaKumar, “Artificial Neural Network for Human Behavior Prediction through Handwriting Analysis,” Int. J. Comput. Appl., vol. 2, no. 2, pp. 36–41, 2010, doi: 10.5120/629-878.
V. R. Lokhande and B. W. Gawali, “Analysis of signature for the prediction of personality traits,” Proc. - 1st Int. Conf. Intell. Syst. Inf. Manag. ICISIM 2017, vol. 2017-January, pp. 44–49, 2017, doi: 10.1109/ICISIM.2017.8122145.
M. A. Rahman, “ASPECT-BASED SENTIMEN ANALISIS OPINI PUBLIK PADA INSTAGRAM DENGAN CONVOLUTIONAL NEURAL NETWORK ( STUDI KASUS : BIZNETNETWORKS ) ASPECT-BASED SENTIMEN ANALISIS OPINI PUBLIK PADA INSTAGRAM DENGAN CONVOLUTIONAL NEURAL NETWORK ( STUDI KASUS : BIZNETNETWORKS ),” 2020.
W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.
N. Fadlia and R. Kosasih, “Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn),” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 207–215, 2019, doi: 10.35760/tr.2019.v24i3.2397.
A. T. Vo, H. S. Tran, and T. H. Le, “Advertisement image classification using convolutional neural network,” in 2017 9th International Conference on Knowledge and Systems Engineering (KSE), 2017, pp. 197–202, doi: 10.1109/KSE.2017.8119458.
B. Fallah and H. Khotanlou, “Identify human personality parameters based on handwriting using neural network,” 2016 Artif. Intell. Robot. IRANOPEN 2016, pp. 120–126, 2016, doi: 10.1109/RIOS.2016.7529501.
T. Purwaningsih, I. A. Anjani, and P. B. Utami, “Convolutional Neural Networks Implementation for Chili Classification,” Proceeding - 2018 Int. Symp. Adv. Intell. Informatics Revolutionize Intell. Informatics Spectr. Humanit. SAIN 2018, pp. 190–194, 2019, doi: 10.1109/SAIN.2018.8673373.
K. P. Danakusumo, “Implementasi Deep Learning Menggunakan Convolutional Neural Network Untuk Klasifikasi Citra Candi Berbasis Gpu,” Tugas Akhir, p. 68, 2017.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510–4520, 2018, doi: 10.1109/CVPR.2018.00474.
R. Rismi and S. N. Azhari, Convolutional Neural Network implementation for image-based Salak sortation. 2016.
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