Indah Ariyati, . Ridwansyah, . Suhardjono


The existing complaints on the performance of assistant lecturers show the impact of the absence of better competence, so that an accurate evaluation process on the performance of lecturer assistants based on their duties and obligations in a certain period of time. The evaluation process required an improved model of accuracy which was a formidable challenge in the selection of more efficiency and effectiveness features, in which case we proposed a method of particle swarm optimization to improve the accuracy of neural network methods that experienced problems in the selection of features that were weighted in detailed analysis by particle swarm optimization with neural network learning performance. This study aims to find a complex alternative solution in the evaluation of lecturer's assistant where research is based on parameters obtained from UCI Machine Repository. The final research shows that particle swarm optimization method can in-crease the accuracy of 75.56% from the previous value of 51.75% and increase the kappa value of 0,632 from the previous kappa value 0,276. The result of developing particle swarm optimization toward neural network by increasing the accuracy and kappa value can be used as controlling periodically in evaluating the performance of assistant lecturer.


feature selection; lecturer assistant; neural network; particle swarm optimization

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