CROSS ENTROPY UNTUK OPTIMASI LAGRANGE MULTIPLIERS PADA SUPPORT VECTOR MACHINES SEBAGAI MODEL PREDIKSI FINANCIAL DISTRESS - ., Herlina (Heuristic, 2016)
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 distress