[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121409-en":3,"doc-seo-121409-105":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":4,"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},121409,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction - Optimization and Evaluation of a Random Forest Model","Employee turnover involves replacing employees and can generate substantial recruitment costs while lowering productivity and team continuity. This study optimizes employee turnover prediction by integrating hash encoding with machine learning. An open-source Kaggle dataset with 14,994 records and 10 features is used, including object-type categorical attributes that are converted into numeric values via hash encoding to reduce memory use and speed preprocessing. A Random Forest model is trained on the processed data and evaluated using accuracy, recall, precision, and F1-score, achieving 0.988, 0.961, 0.988, and 0.974 respectively.","Journal of Information Systems and Informatics  \nVol. 7, No. 2, June 2025 e-ISSN: 2656-4882 p-ISSN: 2656-5935  \nDOI: 10.51519/journalisi.v7i2.1129 Published By DRPM-UBD  \nIntegration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction  \nAhya Radiatul Kamila1, Johanes Fernandes Andry2, Francka Sakti Lee3  \nFelliks F. Tampinongkol4  \n1,4Data Science Department, Bunda Mulia University, Jakarta, Indonesia  \n2,3Information System Department, Bunda Mulia University, Jakarta, Indonesia  \n[Email:](Email:1 akamila@bundamulia.ac.id)[1](Email:1 akamila@bundamulia.ac.id)[ akamila@bundamulia.ac.id](Email:1 akamila@bundamulia.ac.id)  \nAbstract  \nEmployee turnover refers to the replacement of employees within an organization, which can lead to losses such as recruitment costs and decreased productivity. Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. The dataset used in this study is an open-source dataset obtained from Kaggle dataset. It consists of 14,994 rows and 10 columns (features) representing employee-related information such as satisfaction level, evaluation score, number of projects, average monthly hours, and whether the employee left the company. Among these features, some are of object data type. Since machine learning algorithms generally cannot work directly with object-type features, the use of hash encoding is proposed. This technique converts object-type data into numerical data. It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. The evaluation is conducted using accuracy, recall, precision, and F1-score metrics, which yielded results of 0.988, 0.961, 0.988, and 0.974, respectively. These results indicate that the integration of hash encoding techniques and machine learning can produce a well-performing model for predicting employee turnover.  \nKeywords: Hash Encoding, Machine learning, Turnover Prediction, Random Forest  \n1. INTRODUCTION  \nTurnover is a term used in human resource management to describe the replacement of employees within an organization [1] . The turnover rate can be measured by the number of employees who voluntarily leave a company within a certain period [2] . A high turnover rate is generally considered problematic, as it can lead to significant losses for the company [3] . With a high turnover rate, the company is required to find replacement employees to ensure smooth operations and maintain workflow continuity. Moreover, high turnover may result in a loss of  \n1859  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \np-ISSN: 2656-5935 [http://journal-isi.org/index.php/isi](http://journal-isi.org/index.php/isi) e-ISSN: 2656-4882  \nknowledge and skills that are difficult for new employees to immediately replace. Consequently, work processes and team performance can be disrupted, ultimately affecting the company’s overall productivity [4] . Therefore, predicting employee turnover is crucial, enabling companies to anticipate spikes in turnover and take appropriate steps to retain valuable employees.  \nNumber of studies have been conducted to control employee turnover rates through analytical approaches using machine learning algorithms. One notable study in this context [5] compared the performance of several machine learning algorithms and found that Logistic Regression achieved the highest accuracy of 87.71% . A similar study was conducted by [6], who compared various machine learning algorithms for the same objective, with the best result obtained from the K-Nearest Neighbor algorithm, which achieved 84% accuracy after preprocessing","cbCaimo3mFegadus","https://ap.wps.com/l/cbCaimo3mFegadus","pdf",818439,1,18,"English","en",105,"# Introduction\n## Employee turnover and business impact\n## Related work on machine learning turnover prediction\n## Gap in encoding strategies and motivation\n# Methodology and Model Training\n## Dataset and feature description\n## Hash encoding for object-type features\n## Random Forest training\n# Evaluation\n## Metrics used for performance assessment\n## Results and discussion","[{\"question\":\"Why is employee turnover prediction important for organizations?\",\"answer\":\"Predicting turnover helps companies anticipate spikes in departures and take actions to retain valuable employees, reducing recruitment costs and preserving productivity.\"},{\"question\":\"What role does hash encoding play in this study?\",\"answer\":\"Hash encoding converts object-type categorical features into numeric values, addressing limitations of machine learning algorithms that cannot directly process non-numeric data and improving preprocessing efficiency.\"},{\"question\":\"Which machine learning model and evaluation metrics are used?\",\"answer\":\"The study trains a Random Forest model and evaluates it with accuracy, recall, precision, and F1-score to measure predictive quality.\"}]","Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction - Optimization and Evaluation of a Random Forest Model | PDF",1785735538,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integration-of-hash-encoding-technique-with-machine-learning-for-employee-turnover-prediction-optimization-and-evaluation-of-a-random-forest-model","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integration-of-hash-encoding-technique-with-machine-learning-for-employee-turnover-prediction-optimization-and-evaluation-of-a-random-forest-model/121409/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is employee turnover prediction important for organizations?","Question",{"text":75,"@type":76},"Predicting turnover helps companies anticipate spikes in departures and take actions to retain valuable employees, reducing recruitment costs and preserving productivity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does hash encoding play in this study?",{"text":80,"@type":76},"Hash encoding converts object-type categorical features into numeric values, addressing limitations of machine learning algorithms that cannot directly process non-numeric data and improving preprocessing efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model and evaluation metrics are used?",{"text":84,"@type":76},"The study trains a Random Forest model and evaluates it with accuracy, recall, precision, and F1-score to measure predictive quality.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]