Nazri Mohd Nawi, Eneng Tita Tosida, Hamiza Hasbi, Norhamreeza Abdul Hamid

Judul	:	An Implementation of First and Second Order Neural Network Classification on Potential Drug Addict Repetition
Abstrak	:	Back propagation (BP) neural network is known for its popularity and its capability in prediction and classification. BP used gradient descent (GD) method as one of the most widely used error minimization methods used to train back propagation (BP) networks. Besides its popularity BP still faces some limitation such as very slow in learning as well as easily get stuck at local minima. Many techniques have been introduced to improve BP performance. This research implements second order method together with gradient descent in order to improve its performance. The efficiency of both methods are verified and compared by means of simulations on classifying drug addict repetition. The results show that the second order methods are more reliable and significantly improves the learning performance of BP. Keywords: Back propagation, classification, gradient descent, neutral network, second order
Tahun	:	2021	Media Publikasi	:	Jurnal Internasional
Kategori	:	Jurnal	No/Vol/Tahun	:	1 / 2 / 2021
ISSN/ISBN	:	2773-5540
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	Deng, W. J., Chen, W. C., and Pei, W.: Back-propagation neural network based importance-performance for determining critical service attributes, J. of Expert Systems and Applications, vol. (2), pp. 1--26. (2008)   Basheer, I. A., and Hajmeer, M.: Artificial Neural Networks: fundamentals, computing, design and application, J. of Microbiological Methods, vol. 43 (1), pp. 03--31. (2000)   Nazri Mohd Nawi, R.S. Ransing, Mohd Najib Mohd Salleh, Rozaida Ghazali, Norhamreeza Abdul Hamid, "An Improved Back Propagation Neural Network Algorithm on Classification Problems," Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia, (2010)   Fauziah Ibrahim*, "Pengguna Dadah Wanita di Malaysia: Pengalaman Penagihan dan Hubungan Kekeluargaan," Pusat Pengajian Psikologi dan Pembangunan Manusia, Fakulti Sains Sosial dan Kemanusiaan, Universiti Kebangsaan Malaysia, Bangi, Selangor, (2014)   E. T. a. S. Nikolenko, "Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews," Hindawi Journal of Healthcare Engineering, Russia, (2017)   Fauziah Ibrahim, "FAKTOR MENYUMBANG KEPADA PENAGIHAN RELAPS DALAM KALANGAN PENAGIH DADAH PUSPEN DI SEMENANJUNG MALAYSIA," AADK, Malaysia, (2015).   N. H. J. S. M. &. C. M. Malhotra, "Marketing Research: Applied Orientation (1st Edition)," Prentice Hall, Sydney, (1996)   C. Y. Piaw, "Kaedah dan Statistik Penyelidikan: Kaedah Pendidikan.," McGraw Hill, Kuala Lumpur, (2006).   G. Diekhoff, "Statistics for the Social and Behavioral Sciences.," Wm. C. Brown Publisher, Dubuque, (1992).   Nazri Mohd Nawi, "The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks," Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia, (2017)   V. N. a. B. Trivedi, Fundamentals of ANN, Back propagation algorithm and its parameters, International journal of science, technology and management, (2014)   T. A. J. C. A. Syed Muhammad Aqil Burney, "A Comparison of First and Second Order Training Algorithms for Artificial Neural Networks," World Academy of Science, Engineering and Technology, vol. 1, p. 1, (2007).   A. F.Paulin, "Classification of Breast cancer by comparing Back propagation training algorithms," International Journal on Computer Science and Engineering, vol. 3, p. 1, (2011)   D. V. Nagori, "Fine tuning the parameters of back propagation algorithm for optimum learning performance," 2nd International Conference on Contemporary Computing and Informatics, (2016)   Rehman, M.Z., Nawi, N. M., and Ghazali, R.: Studying the effect of adaptive momentum in improving the accuracy of gradient descent back propagation algorithm on classification problems, J. International Journal of Modern Physics (IJMPCS), vol.1 (1). (2012)   Basheer, I. A., and Hajmeer, M.: Artificial Neural Networks: fundamentals, computing, design and application, J. of Microbiological Methods, vol. 43 (1), pp. 03--31. (2000)   J. H. Rüdiger Wirth, "CRISP-DM: Towards a Standard Process Model for Data Mining," European Commission, Germany
URL	:	https://publisher.uthm.edu.my/ojs/index.php/emait/article/view/8499