[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120042-en":3,"doc-seo-120042-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120042,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY","Employee compliance reflects commitment to obey regulations and avoid prohibited actions under company policies. Discipline violations can harm a financial services company’s reputation when violation rates are high, while low rates support a more positive public perception. This study predicts the likelihood of employee discipline violations and evaluates predictive accuracy using Machine Learning models, including Random Forest, Decision Tree, and Naïve Bayes, with performance comparison via majority voting.","APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY  \nMuhamad Fadel1, Kanasfi2, Arief Wibowo3  \n1,2,3Master of Computer Science, Information and Technology Faculty Universitas Budi Luhur, Indonesia [Email:](Email:12211600081@student.budiluhur.ac.id)[1](Email:12211600081@student.budiluhur.ac.id)[2211600081@student.budiluhur.ac.id](Email:12211600081@student.budiluhur.ac.id), [2](22211601170@student.budiluhur.ac.id)[2211601170@student.budiluhur.ac.id](22211601170@student.budiluhur.ac.id),  \n[3](3arief.wibowo@budiluhur.ac.id)[arief.wibowo@budiluhur.ac.id](3arief.wibowo@budiluhur.ac.id)  \n(Article received: July 20, 2023; Revision: August 5, 2023; published: February 13, 2024)  \nAbstract  \nEmployee 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.  \nKeywords: Decision Tree, Machine Learning, Majority Voting, Naïve Bayes, Random Forest.  \nPENERAPAN MACHINE LEARNING DALAM PREDIKSI PELANGGARANKEDISIPLINAN KARYAWAN PADA PERUSAHAAN JASA KEUANGAN  \nAbstrak  \nKepatuhan karyawan merupakan komitmen untuk mematuhi peraturan dan menjauhi hal-hal yang dilarang dalamundang-undang dan peraturan perusahaan yang apabila tidak dipatuhi, maka karyawan diberikan sanksi disiplin. Kedisiplinan karyawan adalah sebuah kewajiban dan kesediaan karyawan dalam mentaati segala aturan yang adadalam suatu perusahaan dalam upaya pencapaian visi misinya, tingkat pelanggaran kedisiplinan karyawan yang tinggi sebesar 38% pada PT. HCI yang bergerak pada bidang jasa keuangan dapat berdampak negatif pada reputasi perusahaan, sementara itu tingkat pelanggaran kedisiplinan karyawan yang rendah pada sebuah perusahaan dapat berdampak positif pada reputasi perusahaan. Paper ini bertujuan untuk memprediksi kemungkinan karyawan melakukan pelanggaran kedisiplinan, dan mengevaluasi kinerja akurasi dengan menggunakan teknik Machine learning Random Forest, Decision Tree, dan Naive Bayes. Hasil pengujian membuktikan bahwa teknik Machine Learning Random Forest merupakan model terbaik dengan nilai tertinggi dalam hal akurasi dengan nilai 87,30%, sementara teknik Machine Learning Decision Tree dan Naive Bayes memiliki nilai masing-masing 83,28% dan 70,27%, nilai dari masing-masing teknik Machine Learning tersebut kemudian dilakukan perbandingan menggunakan teknik Majority Voting, sehingga menghasilkan nilai akurasi t","cbCaippyZn0xeAKI","https://ap.wps.com/l/cbCaippyZn0xeAKI","pdf",887719,1,"English","en",105,"# Introduction\n## Employee discipline and organizational impact\n## Discipline evaluation by HR compliance\n# Methods\n## Data-driven prediction approach\n## Machine learning models compared\n## Majority voting comparison\n# Results\n## Model accuracy comparison\n## Best model selection\n# Conclusion\n## Key findings and implications","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses predicting the possibility of employees committing discipline violations and assessing how accurately different machine learning models can do so.\"},{\"question\":\"Which machine learning techniques are used?\",\"answer\":\"Random Forest, Decision Tree, and Naïve Bayes are used, and their outputs are compared using majority voting.\"},{\"question\":\"Which model achieves the highest accuracy?\",\"answer\":\"Random Forest provides the highest individual accuracy (87.30%), while the total accuracy using majority voting is 85.31%.\"}]","APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY | 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