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All Journal JURNAL RELASI STIE MANDALA JEMBER International Journal of Social Science and Business YUME : Journal of Management JIAI (Jurnal Ilmiah Akuntansi Indonesia) Jurnal Pengabdian Masyarakat IPTEKS Jurnal Sains Manajemen dan Bisnis Indonesia MBA: Journal of Management and Business Aplication Jurnal Teknik Informatika (JUTIF) Jurnal Terapan Manajemen dan Bisnis JAKUMA : Jurnal Akuntansi dan Manajemen Keuangan ABM: International Journal of Administration, Business and Management Jurnal Pengabdian Masyarakat (JPM) Wiga : Jurnal Penelitian Ilmu Ekonomi Assets : Jurnal Ilmiah Ilmu Akuntansi, Keuangan dan Pajak Riset Akuntansi (RISTANSI) Jurnal Ekonomi dan Bisnis GROWTH (JEBG) Relasi : Jurnal Ekonomi JEBD ABM: International Journal of Administration, Business and Management Jurnal Pengabdian Masyarakat Clean and Sustainable Production International Journal of Economics, Commerce, and Management KUNKUN Journal of Multidisciplinary Research Journal of Management
Muhaimin Dimyati
Institut Teknologi Dan Sains Mandala
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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 Ade Permana, Angga Agung Muliawan Agus Dedi Mustofa Agustin HP, Agustin Ahmad Sauqi Ahmad Sauqi Aini, Siti Isriyah Nurul Ajeng Eka Pratama Alfiatin Alfiatin Anastasya Dyah Puspita Andini Angga Ade Permana Arofatul Jannah Bambang Sulistio Budi iwantoro Danang Wikan Carito Devi Indah Permatasari Dimyati, Haifah Eka Pratama, Ajeng Eka Pratiwi, Yanna Fauziah, Difari Afreyna Fitri, Ayu Ningratul Fitria Ningsih, Wiwik Frandy Irawan Handayani, Yuniorita Indah Handayani, Yuniorita Indah Handayani Hanipan, Hanipan Hary Sulaksono Hary Sulaksono Hayatul Maspufah Hilmiyah, Siti Imam Suroso Istichomah Istichomah Lia Rachmawati Mauliyati, Mauliyati Mrenda Ayu Setyowati Muchamad Taufiq Muhammad Firdaus Muhammad Firdaus Muzakki Hidayat Nanda Widaninggar Nanda Widaninggar Nanda Widaninggar, Nanda Noviansyah Rizal Nur Fadilah Nurma Yunita Nurma Yunita Nurshadrina Kartika Sari Nury Yasien Rachmatullah Petrus Amat Sutadi Ponang Undaghi T Prastyowati, Agustin Hari R. Hedy Ubaidillah Ratih Rakhmawati Ratna Wijayanti Daniar Paramita Retno Widiyastiwi Rini Ekowati St. Nur Fadilah Sucipto, Afan Supardi Supardi Supardi Supardi Supardi Supardi Sutadi, Petrus Amat Suwignnyo Widagdo Suwignyo Widagdo Suwognyo Widagdo Syamsiar, Syamsiar Tamriatin Hidayah Taufiqqurachman Trio Akbar Pamungkas Ummu Hanik Vicky Andika Prahemas Widagdo, Suwognyo Wigatiningsih, Wigatiningsih Wiwik Fitria Ningsih Wiwik Supriyatin Yanna Eka Pratiwi Yeni Kumalasari Yulianto - Yuniorita Indah Handayani Zainollah Zainollah Zainollah Zainollah Zainul Ulum