{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:10:50Z","timestamp":1751429450087,"version":"3.41.0"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>The objective of this research was to forecast the resignation of skilled technicians at a large\nautomobile service center in the country using machine learning techniques. This study used\nthe Random Forest algorithm along with the SMOTE (Synthetic Minority Oversampling\nTechnique) method developed in Python. The research was conducted by preparing a questionnaire, with questions divided into 3 areas: personal factors\u037e push factors and pull factors.\nEach question consisted of 31 subtopics. The total number of employees who responded to\nthe questionnaire was 244 people, with 227 of them who were still working and 17 having\nresigned. Therefore, the data was unbalanced, requiring the creation of synthetic data using\nthe Random Forest algorithm with the SMOTE technique in order to balance the two types of\ndata. The experimental results showed that the model used to predict employee resignation\nusing 3 types of input factors, personal factors + push factors, personal factors + pull factors,\nand personal factors + push factors + pull factors, was effective. When using personal factors\nand with only 2 push factors, it was found that the efficiency of the forecasting model had\nan accuracy of 100%, sensitivity of 100%, precision of 100% and F1-score of 100%. The\nresults of the research showed that using Random Forest and SMOTE to address the data\nasymmetry problem resulted in high accuracy in the model\u2019s prediction performance.<\/jats:p>","DOI":"10.54364\/aaiml.2025.52211","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:50:14Z","timestamp":1751367014000},"page":"3717-3735","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting the Resignation of Skilled Technicians in Automotive Companies Using Artificial Intelligence: A case study of large car service centers in Thailand"],"prefix":"10.54364","volume":"05","author":[{"given":"Pravig","family":"Jeenprecha","sequence":"first","affiliation":[]},{"given":"Natworapol","family":"Rachsiriwatcharabul","sequence":"additional","affiliation":[]}],"member":"32807","published-online":{"date-parts":[[2025]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/394352211.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:50:15Z","timestamp":1751367015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/394352211.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2025.52211","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}