Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee
Abstract
HR's purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees' perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. HR employees no longer need to manually handle vast amounts of data with the advent of data mining. The basic purpose of data mining is to extract information from hidden patterns and trends in data to get near-optimal results. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using PCA. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best for the prediction of employee performance.
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X. Li, H. Liu, W. Wang, Y. Zheng, H. Lv, and Z. Lv, “Big data analysis of the internet of things in the digital twins of smart city based on deep learning,” Future Generation Computer Systems, vol. 128, pp. 167–177, 2022.
K. Srinath, “Page ranking algorithms–a comparison,” International Research Journal of Engineering and Technology (IRJET), 2017.
M. Okoye-Ubaka, A. Adewole, O. Folorunso, and J. Ezike, “Neural network model for performance evaluation of academic staff of tertiary institutions,” International Journal of Applied Information Systems (IJAIS), 2013.
W. Li et al., “A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system,” Mobile Networks and Applications, vol. 26, no. 1, pp. 234–252, 2021.
N. Seliya, A. Abdollah Zadeh, and T. M. Khoshgoftaar, “A literature review on one-class classification and its potential applications in big data,” Journal of Big Data, vol. 8, no. 1, pp. 1–31, 2021.
M. Yağcı, “Educational data mining: Prediction of students’ academic performance using machine learning algorithms,” Smart Learning Environments, vol. 9, no. 1, pp. 1–19, 2022.
Y. K. Atri, S. Pramanick, V. Goyal, and T. Chakraborty, “See, hear, read: Leveraging multimodality with guided attention for abstractive text summarization,” Knowledge-Based Systems, vol. 227, p. 107152, 2021.
S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC medical informatics and decision making, vol. 19, no. 1, pp. 1–16, 2019.
Z. Jaffar, W. Noor, and Z. Kanwal, “Predictive human resource analytics using data mining classification techniques,” Int. J. Comput, vol. 32, no. 1, pp. 9–20, 2019.
K. A. Nisha et al., “A comparative analysis of machine learning approaches in personality prediction using MBTI,” in Computational intelligence in pattern recognition, Springer, 2022, pp. 13–23.
C. Kapadiya, A. Shah, K. Adhvaryu, and P. Barot, “Intelligent cricket team selection by predicting individual players’ performance using efficient machine learning technique,” Int. J. Eng. Adv. Technol, vol. 9, no. 3, pp. 3406–3409, 2020.
D. Paikaray and A. K. Mehta, “An extensive approach towards heart stroke prediction using machine learning with ensemble classifier,” in Proceedings of the international conference on paradigms of communication, computing and data sciences, 2022, pp. 767–777.
H. Yang et al., “Risk prediction of diabetes: Big data mining with fusion of multifarious physical examination indicators,” Information Fusion, vol. 75, pp. 140–149, 2021.
T.-Y. Tseng and Q. Luo, “Company employee quality evaluation model based on BP neural network,” Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 5883–5892, 2021.
M. G. T. Li, M. Lazo, A. K. Balan, and J. de Goma, “Employee performance prediction using different supervised classifiers.” Singapore, 2021.
D. Rich, “Human resources data set.” https://www.kaggle.com/datasets/rhuebner/human-resources-data-set?select=HRDataset_v14.csv, 2021.
M. Fathi, M. Haghi Kashani, S. M. Jameii, and E. Mahdipour, “Big data analytics in weather forecasting: A systematic review,” Archives of Computational Methods in Engineering, pp. 1–29, 2021.
V. R. P. Borges, S. Esteves, P. de Nardi Araújo, L. C. de Oliveira, and M. Holanda, “Using principal component analysis to support students’ performance prediction and data analysis,” in Brazilian symposium on computers in education (simpósio brasileiro de informática na educação-SBIE), 2018, vol. 29, p. 1383.
K. Annisa and A. Arpan, “Decision tree algorithm and gini calculation for puppies skin disease diagnosis,” Jurnal Mantik, vol. 5, no. 4, pp. 2682–2687, 2022.
L. E. Vivanco-Benavides, C. L. Martı́nez-González, C. Mercado-Zúñiga, and C. Torres-Torres, “Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review,” Computational Materials Science, vol. 201, p. 110939, 2022.
H. Liu and J. Liu, “Female employment data analysis based on decision tree algorithm and association rule analysis method,” Scientific Programming, vol. 2022, 2022.
M. Lotfirad, H. Esmaeili-Gisavandani, and A. Adib, “Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of iran,” Journal of Water and Climate Change, vol. 13, no. 2, pp. 383–406, 2022.
A. Abdulhafedh, “Comparison between common statistical modeling techniques used in research, including: Discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest,” Open Access Library Journal, vol. 9, no. 2, pp. 1–19, 2022.
H. Valecha, A. Varma, I. Khare, A. Sachdeva, and M. Goyal, “Prediction of consumer behaviour using random forest algorithm,” in 2018 5th IEEE uttar pradesh section international conference on electrical, electronics and computer engineering (UPCON), 2018, pp. 1–6.
D. Rajković et al., “Yield and quality prediction of winter rapeseed—artificial neural network and random forest models,” Agronomy, vol. 12, no. 1, p. 58, 2021.
F. Hidayat and T. M. S. Astsauri, “Applied random forest for parameter sensitivity of low salinity water injection (LSWI) implementation on carbonate reservoir,” Alexandria Engineering Journal, vol. 61, no. 3, pp. 2408–2417, 2022.
S. Dharumarajan and R. Hegde, “Digital mapping of soil texture classes using random forest classification algorithm,” Soil Use and Management, vol. 38, no. 1, pp. 135–149, 2022.
A. T. Prihatno, H. Nurcahyanto, and Y. M. Jang, “Predictive maintenance of relative humidity using random forest method,” in 2021 international conference on artificial intelligence in information and communication (ICAIIC), 2021, pp. 497–499.
A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial neural networks: A tutorial,” Computer, vol. 29, no. 3, pp. 31–44, 1996.
A. J. Kehinde, A. E. Adeniyi, R. O. Ogundokun, H. Gupta, and S. Misra, “Prediction of students’ performance with artificial neural network using demographic traits,” in Recent innovations in computing, Springer, 2022, pp. 613–624.
S. Wang et al., “State-of-the-art review of artificial neural networks to predict, characterize and optimize pharmaceutical formulation,” Pharmaceutics, vol. 14, no. 1, p. 183, 2022.
A. Duykuluoglu, “The significance of artificial neural networks in educational research a summary of research and literature,” Technium BioChemMed, vol. 2, no. 2, pp. 107–116, 2021.
S. W. Chin, K. G. Tay, C. C. Chew, A. Huong, and R. A. Rahim, “Dorsal hand vein authentication system using artificial neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 3, pp. 1837–1846, 2021.
R. Tabbussum and A. Q. Dar, “Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction,” Environmental Science and Pollution Research, vol. 28, no. 20, pp. 25265–25282, 2021.
G. Piecuch and R. Żyła, “Diagnosing extrusion process based on displacement signal and simple decision tree classifier,” Sensors, vol. 22, no. 1, p. 379, 2022.
Y. Zhang, P. Geng, C. Sivaparthipan, and B. A. Muthu, “Big data and artificial intelligence based early risk warning system of fire hazard for smart cities,” Sustainable Energy Technologies and Assessments, vol. 45, p. 100986, 2021.
S. A. Ajagbe, J. B. Awotunde, M. A. Oladipupo, and O. E. Oye, “Prediction and forecasting of coronavirus cases using artificial intelligence algorithm,” in Machine learning for critical internet of medical things, Springer, 2022, pp. 31–54.
O. D. Adeniji, D. B. Adekeye, S. A. Ajagbe, A. O. Adesina, Y. J. Oguns, and M. A. Oladipupo, “Development of DDoS attack detection approach in software defined network using support vector machine classifier,” in Pervasive computing and social networking, Springer, 2023, pp. 319–331.
E. O. Ogunseye, C. A. Adenusi, A. C. Nwanakwaugwu, S. A. Ajagbe, and S. O. Akinola, “Predictive analysis of mental health conditions using AdaBoost algorithm,” ParadigmPlus, vol. 3, no. 2, pp. 11–26, 2022.
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