[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124362-en":3,"doc-seo-124362-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124362,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Implementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction","Employees are vital assets for organizations, and accurate recruitment decision-making is essential for long-term performance. Incorrect selections increase costs through re-hiring, onboarding burdens, and reduced productivity. This study builds a recruitment decision prediction model using 2024 CPNS Ministry of Finance final results with attributes including education, age, GPA, SKD score, and SKB score. Correlation analysis and exploratory data visuals support understanding variable relationships. Five machine learning classifiers are evaluated, with Naïve Bayes achieving the best performance (88% accuracy, AUC 0.97), indicating strong discrimination between positive and negative classes.","JOURNAL LA MULTIAPP  \nVOL. 06, ISSUE 02 (341-352), 2025 DOI: 10.37899/journallamultiapp.v6i2 .2050  \nImplementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction  \nMuhammad Fauzan Nur Adillah1, Sinung Suakanto1, Nur Ichsan Utama1  \n1Master of Information Systems, Faculty of Industrial Engineering, Telkom University, Bandung, Indonesia  \n*Corresponding Author: Muhammad Fauzan Nur Adillah [Email: ](Email: mfauzanna@student.telkomuniversity.ac.id)[mfauzanna@student.telkomuniversity.ac.id](Email: mfauzanna@student.telkomuniversity.ac.id)  \nArticle Info  \nArticle history:  \nReceived 3 March 2025 Received in revised form 21 March 2025  \nAccepted 8 April 2025  \nKeywords:  \nRecruitment Prediction Machine Learning Recruitment Decision Classification  \nCivil Servant Selection  \nEmployees are vital assets for any organization, and accurate recruitment decision-making is crucial for the organization's long-term success. Incorrect decisions can lead to high costs due to re-hiring processes, onboarding, and decreased productivity. This study aims to develop a recruitment decision prediction model using data obtained from the Final Results of the 2024 CPNS Recruitment in the Ministry of Finance. The data includes attributes such as educational background, age, GPA, SKD Score, and SKB Score. To understand the relationships between variables, correlation analysis was conducted using a correlation matrix and heatmap visualization. Additionally, data exploration was performed using histograms to show the influence of attributes on recruitment decisions. This study employs five machine learning algorithms for prediction: Linear Support Vector Machine, Decision Tree (C5.0), Random Forest, k-Nearest Neighbor (k-NN), and Naïve Bayes Classifier. The results indicate that some attributes significantly influence recruitment decisions, and machine learning models can identify candidates who are more suitable for the available positions. Among the five models tested, Naïve Bayes proved to be the most effective, achieving an accuracy of 88% and an AUC of 0.97, demonstrating its strong performance in distinguishing positive and negative classes. The key factors contributing to the model's success include relevant feature selection, data quality, as well as appropriate preprocessing and validation techniques. This model is expected to enhance objectivity, efficiency, and accuracy in employee recruitment processes, thereby assisting organizations in making more precise and  fair decisions.   \nAbstract  \nIntroduction  \nEmployee Recruitment is a crucial process for any organization as it directly impacts workforce quality and efficiency (Pampouktsi et al. , 2021) . Recruitment decisions involve evaluating candidates based on various criteria, such as educational qualifications, professional experience, skills, and personality traits (Goretzko & Israel, 2022) . While, in theory, candidate selection predictions can be made using simple calculations and logic, the complexity and large volume of data in modern recruitment make manual approaches less effective and prone to human bias.  \nIn this context, applying machine learning-based classification models offers a more objective, efficient, and accurate solution (Smelyakov et al., 2023) . Simple algorithms may handle basic cases, but they often fail to address non-linear relationships and complex interactions between various factors influencing recruitment decisions. For instance, work  \nexperience and technical skills may have different weights depending on the required position, and factors such as personality or organizational cultural fit are challenging to measure manually. Machine learning algorithms excel at identifying patterns and relationships in large datasets, making them highly effective for this research. By utilizing historical recruitment data, these models can predict a candidate’s likelihood of success, thereby accelerating and enhancing the recruitmen","cbCaiqe3fJS1hZXl","https://ap.wps.com/l/cbCaiqe3fJS1hZXl","pdf",802459,1,12,"English","en",105,"# Introduction\n## Recruitment decisions and machine learning motivation\n# Methods\n## K-Nearest Neighbour (KNN)\n## Feature correlation and data exploration\n# Results and evaluation\n## Model comparison and performance metrics","[{\"question\":\"What problem does the study address in employee recruitment?\",\"answer\":\"The study targets the risk and cost of inaccurate recruitment decisions, which can lead to re-hiring, onboarding overhead, and productivity loss.\"},{\"question\":\"Which dataset and candidate attributes are used for prediction?\",\"answer\":\"It uses data from the 2024 CPNS Recruitment Final Results in the Ministry of Finance, including educational background, age, GPA, SKD score, and SKB score.\"},{\"question\":\"Which machine learning model performs best, and what are its results?\",\"answer\":\"Among five tested algorithms, Naïve Bayes is most effective, reaching 88% accuracy and an AUC of 0.97 for distinguishing positive and negative classes.\"}]","Implementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction | 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problem does the study address in employee recruitment?","Question",{"text":75,"@type":76},"The study targets the risk and cost of inaccurate recruitment decisions, which can lead to re-hiring, onboarding overhead, and productivity loss.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and candidate attributes are used for prediction?",{"text":80,"@type":76},"It uses data from the 2024 CPNS Recruitment Final Results in the Ministry of Finance, including educational background, age, GPA, SKD score, and SKB score.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best, and what are its results?",{"text":84,"@type":76},"Among five tested algorithms, Naïve Bayes is most effective, reaching 88% accuracy and an AUC of 0.97 for distinguishing positive and negative 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