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Muhamad Fadel
Master of Computer Science, Information and Technology Faculty Universitas Budi Luhur, Indonesia
Computer Science & IT

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APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY Muhamad Fadel; Kanasfi Kanasfi; Arief Wibowo
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1229

Abstract

Employee compliance is a commitment to comply with regulations and stay away from matters that are prohibited in the laws and or company regulations which if not obeyed, then employees are given disciplinary sanctions. Employee discipline is an obligation and willingness of employees in obeying all existing rules in a company to achieve its vision and mission, a high-level employee disciplinary violation rate of 38% at PT. HCI who are engaged in financial service sector can have a negative impact on a company's reputation, meanwhile a low level of employee disciplinary violations in a company can have a positive impact on the company's reputation.This paper aims to predict the possibility of employees committing discipline violations and evaluating the performance of accuracy by using Machine Learning Random Forest, Decision Tree, and Naive Bayes techniques. The test results prove that the Machine Learning Random Forest technique is the best model with the highest value in terms of accuracy with a value of 87.30%, while the Machine Learning Decision Tree and Naive Bayes technique has a value of 83.28%and 70.27% respectively, the value from each of the Machine Learning techniques, the comparison was made using majority voting techniques, so as to produce a total accuracy value of 85.31%.With this high accuracy value, the Random Forest model is proven to have better performance individually in analyzing the prediction of disciplinary violations in the application of human resources at company, while the total accuracy value uses a majority voting model of 85.31%, slightly decreased due to the high level of accuracy of the Naïve Bayes model compared to other algorithm models.
APPLICATION OF ENSEMBLE METHOD FOR EMPLOYEE TURNOVER PREDICTIONS IN FINANCIAL SERVICES COMPANY Muhamad Fadel; Kanasfi Kanasfi; Zainal Arifin; Gandung Triyono
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1871

Abstract

High employee turnover is a challenge for every company, considering that employees are a valuable asset for the company. A high employee turnover rate indicates the high frequency of employees leaving a company. This will harm the company in terms of time, costs, human resources, and reduce the company's reputation. Low employee turnover is an objective for every company in its efforts to achieve its vision and mission, the employee turnover rate is high at 78.97% at PT. HCI operating in the financial services sector can have a negative impact on the company's reputation. Therefore, there is a need to analyze and predict employee turnover so that company management can take preventive and persuasive actions so as to reduce employee turnover rates. Therefore, a tool is needed to predict whether an employee will leave the company. This paper aims to predict the possibility of employees out of the company using the ensemble method, which is a method that uses a combination of several algorithms consisting of base learners and individual learners, algorithms with the ensemble method used are stacking, random forest, and adaboost, then comparing the result to get the best accuracy. The test results prove that the Stacking algorithm technique is the best model with the highest score in terms of accuracy with a value of 86.84%, while the Random Forest and AdaBoost algorithm techniques have a value of 81.04% and 80.30%. With this high accuracy value, the Stacking model is proven to have better individual performance in analyzing employee turnover predictions in human resource applications in companies.
Co-Authors Arief Wibowo Kanasfi Kanasfi Triyono, Gandung Zainal Arifin