ANN and SVM algorithm in Divorce Predictor
Noor Hafidz1, Sfenrianto2, Yogie Pribadi3, Evita Fitri, Ratino4
1Noor Hafidz*, Master of Computer Science – Postgraduate Programs STMIK Nusa Mandiri, Indonesia.
2Sfenrianto, Information Systems Management Department, BINUS Graduate Program – Master of Information Systems Management, Bina Nusantara University, Jakarta.
3Yogie Pribadi, Master of Computer Science – Postgraduate Programs STMIK Nusa Mandiri, Indonesia.
4Evita Fitri, Master of Computer Science – Postgraduate Programs STMIK Nusa Mandiri, Indonesia.
5Ratino, Master of Computer Science – Postgraduate Programs STMIK Nusa Mandiri, Indonesia.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2523-2527 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5902029320/2020©BEIESP | DOI: 10.35940/ijeat.C5902.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification is a technique used to predict group membership or label for data samples (instances). In order to predict the result, the classification algorithm processes the training set, which contains a set of attributes and corresponding results. One of these classification technique is implemented in order to predict divorce in Turkey. This research is executed by Yöntem, M. K. et al. in 2019. In this research, Yöntem, M. K. concluded that the ANN algorithm combined with correlation-based feature selection has the best performance with an accuracy of 98.82% and Kappa value of 0.9765. Nevertheless, unlike any previous research, ANN is not considered very good in terms of the required training time. In several previous studies, it was also concluded that other classification algorithms, such as SVM, have better prediction accuracy compared to ANN. In this study, prediction accuracy and Kappa value between ANN and SVM algorithms are compared using the same dataset and feature selection as the research done by Yöntem, M. K., to ensure a fair comparison between both of the algorithms. The result obtained from comparing both algorithms is that the SVM algorithm performs better than ANN with an accuracy of 99.8235 and a Kappa value of 0.9964. The training time required by SVM is also better than the ANN training time.
Keywords: Cassification, support vector machine, artificial neural network, divorce prediction.