IMPLEMENTASI PARTICLE SWARM OPTIMIZATION UNTUK OPTIMALISASI DATA MINING DALAM EVALUASI KINERJA ASISTEN DOSEN

Indah Ariyati, . Ridwansyah, . Suhardjono

Abstract


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.

Keywords


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

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References


I. Ariyati, “METODE SIMPLE ADDITIVE WEIGHTING DALAM PENENTUAN PENERIMA BEASISWA YAYASAN,” in Konferensi Nasional Ilmu Sosial & Teknologi (KNiST), 2017, pp. 204–208.

K. L. Tanojo, “Identifikasi Kompetensi Asisten Mahasiswa Dan Penerapannya Pada Rancangan Sosialisasi Dan Rancangan Rekrutmen Dan Seleksi,” GEMA Aktual., vol. 4, no. 2, pp. 58–65, 2015.

Mawardi, “Dosen Dan Asisten Dosen Dalam Pengelolaan Perkuliahan,” J. Ilm. Didakt., vol. 11, no. 2, pp. 221–228, 2011.

T. S. Lim, W. Y. Loh, and Y. S. Shih, “A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms,” Mach. Learn., vol. 40, no. 3, pp. 203–228, 2000.

W.-Y. Loh and Y.-S. Shih, “Split Selection Methods for Classification Trees,” Stat. Sin., vol. 7, no. 4, pp. 815–840, 1997.

N. K. Lee, H. Souri, and H. K. Lee, “Neural Network Application Overview in Prediction of Properties of Cement-Based Mortar and Concrete,” 2014 World Congr. Adv. Civil, Environ. Mater. Res., 2014.

H. Adeli, “Neural Networks in Civil Engineering: 1989–2000,” Comput. Civ. Infrastruct. Eng., vol. 16, no. 2, pp. 126–142, 2001.

S. C. Satapathy, S. Chittineni, S. Mohan Krishna, J. V. R. Murthy, and P. V. G. D. Prasad Reddy, “Kalman particle swarm optimized polynomials for data classification,” Appl. Math. Model., vol. 36, no. 1, pp. 115–126, 2012.

K. S. Kavitha, K. V Ramakrishnan, and M. K. Singh, “Modeling and design of evolutionary neural network for heart disease detection,” Int. J. Comput. Sci. Issues, vol. 7, no. 5, pp. 272–283, 2010.

T. Xu, Q. Peng, and Y. Cheng, “Identifying the semantic orientation of terms using S-HAL for sentiment analysis,” Knowledge-Based Syst., vol. 35, pp. 279–289, 2012.

S. Wang, D. Li, X. Song, Y. Wei, and H. Lia, “A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification,” Expert Syst. Appl., vol. 38, no. 7, pp. 8696–8702, 2011.

H. Liu, H. Motoda, and L. Yu, “Feature selection with selective sampling.,” Proc. Ninet. Int. Conf. Mach. Learn., pp. 395–402, 2002.

S. Das, “Filters, wrappers and a boosting-based hybrid for feature selection.,” Proc. Eigh- teenth Int. Conf. Mach. Learn. ing, pp. 74–81, 2001.

R. Poli, “Analysis of the Publications on the Applications of Particle Swarm Optimisation,” J. Artif. Evol. Appl., vol. 2008, no. 2, pp. 1–10, 2008.

Ridwansyah and E. Purwaningsih, “Particle Swarm Optimization Untuk Meningkatkan Akurasi Prediksi Pemasaran Bank,” J. PILAR Nusa Mandiri, vol. 14, no. 1, 2018.

R. S. Wahono and N. Suryana, “Combining particle swarm optimization based feature selection and bagging technique for software defect prediction,” Int. J. Softw. Eng. its Appl., vol. 7, no. 5, pp. 153–166, 2013.




DOI: http://dx.doi.org/10.26798/jiko.v3i2.127

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Copyright (c) 2018 Indah Ariyati, . Ridwansyah, . Suhardjono

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