The week's pick
Reseach Article
Modified Chi-Square Distance to Improve Personality Type Recognition based on Handwriting
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 1 |
| Year of Publication: 2022 |
| Authors: Dian Pratiwi, Syaifudin, Muhammad Azamy |
10.5120/ijca2022921915
|
Dian Pratiwi, Syaifudin, Muhammad Azamy . Modified Chi-Square Distance to Improve Personality Type Recognition based on Handwriting. International Journal of Computer Applications. 184, 1 ( Mar 2022), 6-12. DOI=10.5120/ijca2022921915
Abstract
This research was conducted to develop a mobile device that is able to recognize one’s personality to support expert decisions based on handwriting through the application of graphology and enneagram psychology. In the process, handwritten data is processed in three main stages, namely pre-processing, texture feature extraction in the form of contrast, energy, and entropy with GLCM, and similarity measure through the modified chi-square method. The value of the feature is categorized into 4 categories in the form of slant, size, breaks and baseline, which will be stored in the SQLite database as a reference. Later, the determination of personality will be seen based on the calculation of the smallest distance of the test data on the reference value of the combination of the categories. Based on the results of the study, the three GLCM texture feature values obtained have intervals that are not unique, and difficult to distinguish between one personality type to another. But the use of the modified chi-square method in the form of random weights can process the feature values so that the test data can be distinguished by type personality with each other with a precision of 60-80% and an accuracy of 72%.
References
- Howard, C., Graphology, The Pen Publishing Company, Philadelphia,1922.
- Coll, R. Fornes, A. Llados, J., Graphological Analysis of Handwritten Text documents for Human Resources Recruitment, International Conference on Document Analysis and Recognition, IEEE Computer Society, DOI: 10.1109/ICDAR.2009.213, 2009.
- Kamath, V. et al, Development of An Automated Handwriting Analysis System, Asean Research Publishing Network (ARPN) Journal of Engineering and Applied Sciences, Vol.6, No.9, ISSN: 1819-6608, 2011.
- Pratiwi, D., Ariwibowo, A.B., Oktaviyanti, F., Penerapan Ilmu Grafologi dalam Membangun Piranti Penganalisa Tulisan Tangan melalui Ekstraksi Fitur Bentuk, Seminar Nasional Teknologi Industri (SNTI IV), Universitas Trisakti, pp: (82)1-6, ISSN: 2335-925X, 2014.
- Pratiwi, D., Santoso, G.B., Saputri, F.H., Pengembangan Sistem Tes Kepribadian berbasis Graphology dan Enneagram, Seminar Nasional Teknologi Industri (SNTI V), pp: 426-433,TIF-015, ISSN: 2335-925X, 2016.
- Pratiwi, D., Santoso, G.B., Saputri, F.H., The Application of Graphology and Enneagram Techniques in Determining Personality Type based on Handwriting Features, Journal of Computer Science and Information, Vol.10, No.1, pp: 11-18, DOI: http://dx.doi.org/10.21609/jiki.v10i1.372. ISSN: 2088-7051, e-ISSN: 2502-9274, 2017.
- Pratiwi, D., Syaifudin, Handwritten Document Security System with Inner Product and Shaped based Feature Extraction Method, International Journal of Computer Application (IJCA), Vol.134, No.1, ISSN: 0975-8887, 2016.
- Syaifudin, Pratiwi, D., Security Handwritten Documents using Inner Product, Proceedings of Second International Conference on Electrical Systems, Technology and Information (ICESTI 2015), Lecture Notes in Electrical Engineering, Vol.365, Springer, Singapore, 2016.
- Davin, R,R.P, Pratiwi, D., Syaifudin, Trubus R., Rizky, D.LP, Implementation of Inner Product to Analyze Digital Handwriting based on Texture Traits, International Conference on Computer Science and Artificial Intelligence, pp. 114-118, ACM, New York, USA, ISBN: 978-1-4503-5392-2, DOI: 10.1145/3168390.3168418, 2017.
- Duan, G., Yang, J., and Yang, Y., Content-Based Image Retrieval Research, International Conference on Physics Science and Technology (ICPST), Physics Procedia 22, pp. 471-477, 2011.
- Vargas, J.F., Ferrer, M.A., Travieso, C.M., and Alonso, J.B., Off-line Signature Verification based on Grey Level Information using Texture Features, Pattern Recognition 44, pp: 375-385, Elsevier, 2011.
- Babu, N.P., Sagar, K.C., and Reddy, A.S., Offline Signature Verification based on GLCM, International Journal of Electronics Signals and Systems (IJESS), Vol.3, Issue 2, ISSN: 2231-5969, pp: 66-72, 2013.
- Pratiwi, D., The Use of Self Organizing Map Method and Feature Selection in Image Database Classificaton System, International Journal of Computer Science Issues (IJCSI), Vol.9, Issue 3, No.2, ISSN : 1694-0814, 2012.
- Pratiwi, D., Santika, D.D., Pardamean, B., An Application of Backpropagation Artificial Neural Network for Measuring The Severity of Osteoarthritis, International Journal of Engineering & Technology (IJET-IJENS), Vol.11, No.3, ISSN : 117303-8585, 2011.
- Absultanny, Y.A., Pattern Recognition using Multilayer Neural Genetic Algorithm, Neurocomputing, 237-247, Elseiver Science, 2003.
- Huang, A. Similarity Measures for Text Document Clustering. New Zealand Computer Science Research Student Conference, Christchurch, New Zealand, 2008.
- Ludvianto, B., Analisis Tulisan Tangan : Grapho for Success, PT Gramedia Pustaka Utama, Jakarta, 2013.
- Poizner, A., Graphology in Clinical Practice. Psychologica. Spring Burlington, Vol. 24, No.1, 2004.
Index Terms
Keywords