Improving Employee Retention using Path Analysis At E-Commerce Company in Indonesia
Noerlina1, Bachtiar H. Simamora2, Idris Gautama So3, Tirta Nugraha Mursitama4, Ferlyn Angelina5, Jessie Prasetya6, Joy Stevani7
1Noerlina, Information Systems Department, School of Information Systems, Bina Nusantara University, Jakarta, Indonesia.
2Bachtiar H. Simamora, Management Department, BINUS Business School, Undergraduate Program, Bina Nusantara University, Jakarta, Indonesia.
4Idris Gautama So, Management Department, BINUS Business School, Undergraduate Program, Bina Nusantara University, Jakarta, Indonesia.
5Tirta Nugraha Mursitama, International Relations Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia.
6Ferlyn Angelina,International Relations Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia.
7Jessie Prasetya, International Relations Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia.
Stevani, , International Relations Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia 11480. Email: tmursitama@binus.edu
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1382-1386 | Volume-8 Issue-3 September 2019 | Retrieval Number: B3506078219/19©BEIESP | DOI: 10.35940/ijrte.B3506.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.
Keywords: Ensemble Classifiers, VGG16, VGG19, Resnet56, Probability Voting Technique, CIFAR-10.
Scope of the Article: E-Commerce