1.png)
JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI KINERJA SATPAM
Abstract
Employee performance assessment is a very important task that has to be performed by Human Resource Department in a certain institution, because it would be useful for the policy holders to help them making a decision in promoting or not their employees. This paper delivered the analysis of using Multi Layer Perceptron (MLP) to predict the performance of security unit personnels which has been trained by a formal institution. The data that was used in this research were collected from PT. Garuda Merah Indonesia, that is a company that has role to train and to educate people who wants to be a security personnel. The data consist of 175 record with 10 attributes which include the assessment from aspects of cognitive, personality, and skill. MLP predicted the security personnel performances into three categories, i.e. “Good”, “Enough”, and “Fail”. The 10 folds Cross Validation technique was also used in testing phase to measure its performance comprehensively with the output of the best accuracy was 97,75%.
Keywords
Full Text:
PDFReferences
J. Han and M. Kamber, Data Mining: Concepts and Techniques – Second Edition, CA: Morgan Kauffmann, 2006.
S. Haykin, Neural Network and Learning Machines – Third Edition, New Jersey: Pearson Prentice Hall, 2009.
L.V. Fausett and E. Cliffs, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, New Jersey: Prentice Hall, 1994.
A.P. Wibowo and S. Hartati, “Sistem klasifikasi kinerja satpam menggunakan metode naive bayes classifier,” Jurnal Inovasi Teknologi Politeknik Bengkaslis – Seri Informatika, vol. 1(2), pp. 192-201, 2016.
U. Pauziah, “Analisis penentuan karyawan terbaik menggunakan Metode Algoritma Naive Bayes (Studi Kasus PT. XYZ),” Prosiding Diskusi Panel Pendidikan “Menjadi Guru Pembelajar”, Jakarta, April 2017.
R. Kotalwar, R. Chavan, S. Gandhi, and V. Parmar, “Data Mining: Evaluating Performance of Employee’s using Classification Algorithm Based on Decision Tree,” Engineering Science and Technology: An International Journal, vol. 4(2), pp. 29-35. 2014.
S. Defiyanti, “Analisis dan Prediksi Kinerja Mahasiswa Menggunakan Teknik Data Mining,” Syntax Jurnal Informatika, vol. 2(2), pp. 1-8, 2013.
V. Kalaivani and Elamparithi, “An Efficient Classification Algorithms for Employee Performance Prediction,” International Journal of Research in Advance Technology, vol. 2(9), pp. 27-32, 2014.
H. Jantan, R. Hamdan., A.R. Othman, and Z.A. Othman, “Applying Data Mining Classification Techniques for Employee’s Performance Prediction,” Proceeding of Knowledge Management 5th Int’l Conference, Malaysia. 2010.
Suyanto. Data Mining untuk Klasifikasi dan Klasterisasi, Bandung: Informatika, 2017.
S. Arlot. “A survey of cross-validation procedures for model selection”, Statistics Survey, vol. 4, pp. 40-79, 2010.
DOI: http://dx.doi.org/10.26798/jiko.v3i1.80
Article Metrics
Abstract view : 1394 timesPDF - 2013 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 Muhammad Fachrie, Adityo Permana Wibowo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.