Open AccessEmployee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest
Author(s) -
Aris Nurhindarto,
Esa Wahyu Andriansyah,
Farrikh Alzami,
Purwanto Purwanto,
Moch Arief Soeleman,
Dwi Puji Prabowo
Publication year - 2021
Publication title -
kinetik
Language(s) - English
Resource type - Journals
eISSN - 2503-2267
pISSN - 2503-2259
DOI - 10.22219/kinetik.v6i4.1345
Subject(s) - random forest , univariate , attrition , feature selection , computer science , selection (genetic algorithm) , decision tree , receiver operating characteristic , feature (linguistics) , recall , machine learning , statistics , operations management , data mining , artificial intelligence , operations research , multivariate statistics , engineering , mathematics , psychology , linguistics , philosophy , dentistry , cognitive psychology , medicine
Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%.