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Öğe Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model(Mdpi, 2023) Nordin, Noor Ilanie; Mustafa, Wan Azani; Lola, Muhamad Safiih; Madi, Elissa Nadia; Kamil, Anton Abdulbasah; Nasution, Marah Doly; Hamid, Abdul Aziz K. AbdulSupport ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.Öğe Sample median approximation on stochastic data envelopment analysis(Inderscience Publishers, 2020) Nasution, Marah Doly; Mawengkang, Herman; Kamil, Anton Abdulbasah; Efendi, Syahril; SutarmanThis paper study a new approximation model to solving stochastic data envelopment analysis (SDEA) problem. The proposed approach is based on problems that might occur in everyday life. This paper discusses the approach in determining the efficiency and super efficiency ratings of a decision making unit (DMU) in the DEA model with stochastic data. In determining efficiency, SDEA is first transformed into an equivalent deterministic DEA by changing its chance constraints in such a way that the SDEA problem can be solved easily. The author proposes an approach technique called a sample median approximation (SMA) to change the chance constraints so that it will be easy to get the optimal solution in determining the efficiency of DMUs. In the process, the data to be processed first is determined by the median average which will later be considered to represent the actual sample average. As a numerical example, the author resolves the vendor selection problem as presented by Wu and Olson (2006) in their paper. By taking the same parameter value (a = 0.2 and beta = 0.9), the efficiency score and super efficiency of the problem are obtained. © 2020 Inderscience Enterprises Ltd.. All rights reserved.