• Herlina . Teknik Industri, Universitas 17 Agustus 1945 Surabaya


The competence in predicting financial distress becomes an important research due to
the advantage in preventing companies financial failure. Besides, financial distress
prediction model will give benefit to the investors and creditors. This research develop
a financial distress prediction model for listed manufacturing companies in Indonesia
using Support Vector Machines (SVM). Mathematically, SVM is formulated in the form
of quadratic programming, which requires high computational time in finding the
optimal solution. In this research, Cross Entropy (CE) is used to optimize one of the
SVM’s parameter that is Lagrange multipliers to find the optimal solution or near
optimal solution of dual Lagrange SVM. The accuracy of the prediction model and
computation time will be compared between standard SVM and CE-SVM. Finally, note
that the CE-SVM can solve classification problems in computing time 9.7 times shorter
than the standard SVM with good accuracy results.
Keywords: cross entropy, lagrange multipliers, support vector machines, financial


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