PENERAPAN PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI ATIRBUT PADA METODE DECISION TREE C 4.5 UNTUK PERSETUJUAN

Andika Dwi Hadiri

Abstract


Bad credit is one of the credit risk faced by the financial and banking industry. Improved accuracy of credit ratings can be done by doing the selection of attributes, because the selection of attributes reduce the dimensionality of the data so that operation of the data mining algorithms can be run more effectively and more quickly. In this study will be used method Decision Tree algorithm C 4.5 and will be selected attributes using particle swarm optimization to determine credit ratings. With this decision method, Credit Approval process is expected to be more accurate, so that errors caused in decision making is minimized.


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References


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