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All Journal BERKALA SAINSTEK Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Kreativitas dan Inovasi (Jurnal Kreanova) Jurnal Pengabdian Masyarakat Journal of Digital Literacy and Volunteering Journal of Informatics Development RECORD Journal of Loyality and Community Development Journal of Digital Literacy and Volunteering
Fauziah, Difari Afreyna
Institut Teknologi Dan Sains Mandala
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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IMPLEMENTATION OF MACHINE LEARNING ON EMPLOYEE ATTRITION BASED ON PERFORMANCE PARAMETERS USING PARTICLE SWARM OPTIMIZATION AND ENSEMBLE CLASSIFER METHODS Fauziah, Difari Afreyna; Muliawan, Agung; Dimyati, Muhaimin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3442

Abstract

This research aims to apply machine learning to predict the start of employee attrition by considering performance parameters and other related factors in the company environment. Employee attrition refers to employee turnover in an organization for various reasons such as resignation, moving, retirement, and so on. This research uses a dataset originating from the IBM HR Analytics Employee Attrition dataset available on Kaggle (https://www.kaggle.com/) which consists of 35 attributes. Particle Swarm Optimization (PSO) method is a dimension reduction method to improve the efficiency and performance of machine learning models by reducing unnecessary data. The machine learning approaches used in the early prediction of employee attrition in this research are Support Vector Machine, Deep Learning and Neural Network methods. This research will combine the dimensionality reduction process with machine learning to obtain employee attrition prediction results that are optimized using the Ensemble method, namely Bagging and Boosting to increase the accuracy value of the prediction results. The results of this research show that applying dimensionality reduction using the PSO method can improve the accuracy of results on the IBM HR Analytics Employee Attrition dataset. The best accuracy in attrition prediction was obtained by the Deep Learning method with an accuracy value of 86.94%, a precision value of 88.90%, and a recall value of 96.40% after combining it with PSO and optimizing with Bagging.
Co-Authors Abdul Wahid Achmad Maududie Agung Muliawan Ahmad Nurdianyah Aini, Arifatul Andita Prasetyo , Nur Andita Prasetyo, Nur Angga Pranadaning, Fadila Arini Shufia Dwi Sukmawati Daffa Duta Perdana, Mohammad Dwi Natasya, Amelia Edwar Yudhistira, Gedion Hamzah Fansuri Hary Sulaksono Hermansyah, Masud Hermawan, Muhammad Ida Ayu Putu Sri Widnyani Ifrina Nuritha Iqbal Sabilirrasyad laili, Nufilatul Muhaimin Dimyati Muhamat Abdul Rohim Narisa, Defani Nurfaizeh, Nurfaizeh Nurhayati Nurhayati Oktavinda, Diana Purnamie Titisari Shofyan, Firdaus Syaifuddin, Ageng Tamriatin Hidayah Wiranto, Ferry Zahro Alifia Rohma, Sofiatus