Author(s) :

Nor Bazilah Safar, Sabri Ahmad, Jusoh Yacob, Mustafa Man.

Volume :

Abstract :

Currently, The use of statistical models to predict business failures have been noticed. However, very few studies have been conducted to predict the loss of a co-operative business. The objective of this study to construct the Logistic Regression model (LR) and Neural Network (NN), to compare and to identify the best model (LR and NN) and identify important variables in forecasting losses cooperative. Data from 2009 until 2011 were obtained from the Department of Cooperatives Commission of Malaysia (CCM) Terengganu branch were analyzed using SPSS Clementine version 12.0 Results shown that the Prune method in the NN model gave the highest value of testing 82.77%, the sensitivity of 82.90%, the specifcity of 75% and minimum error (MSE) of 17%. Capital was the most variable influence towards the cooperative loss due to the highest value of 0.23(23%).

Keywords: Cooperative, Cooperative loss, Logistic Regression and Neural Network

Penggunaan model statistik untuk meramalkan kegagalan perniagaan telah mendapat perhatian pada masa kini. Walau bagaimanapun, sangat sedikit kajian telah dijalankan untuk meramalkan kerugian dalam perniagaan sesebuah koperasi. Objektif kajian ialah untuk membina model Regresi Logistik (RL) dan Rangkaian Neural (RN), membandingkan dan mengenalpasti model ramalan terbaik (RL dan RN) serta mengenalpasti pembolehubah penting dalam peramalan kerugian koperasi. Data 2009 sehingga 2011 diperolehi daripada Jabatan Suruhanjaya Koperasi Malaysia (SKM) cawangan Terengganu di analisis menggunakan SPSS Clementine versi 12.0. Hasil kajian mendapati bahawa kaedah Prun di dalam model RN memberikan nilai pengujian yang paling tinggi iaitu 82.77%, nilai kepekaan iaitu 82.90%, nilai ketentuan iaitu 75% dan kadar ralat (MSE) terendah iaitu 17%. Modal menjadi pembolehubah yang paling mempengaruhi kerugian koperasi kerana mempunyai nilai yang paling tinggi iaitu 0.23(23%).

Kata Kunci : Koperasi, Kerugian Koperasi, Regresi Logistik dan Rangkaian Neural.

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