[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123424-en":3,"doc-seo-123424-105":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},123424,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based","An efficiency metaheuristic model is developed to forecast customer churn in business markets using machine learning and optimization. Metaheuristic methods are used to search for optimal solutions quickly, while machine learning extracts patterns from large datasets. The study defines churn as customers who stop transacting within a specific time frame. Particle swarm optimization (PSO) searches parameter criteria, and support vector regression (SVR) estimates continuous churn-related outcomes. Efficiency improvements integrate predictive flexibility and risk minimization to reduce churn.","An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based  \nRahmad B.Y. Syah1,2, Rizki Muliono1,2, Muhammad Akbar Siregar3, Marischa Elveny2  \n1Department of Informatics, Faculty of Engineering, Universitas Medan Area, Medan, Indonesia 2Excellent Centre of Innovations and New Science-PUIN, Universitas Medan Area, Medan, Indonesia 3Faculty of Business Economics, Universitas Medan Area, Medan, Indonesia  \nArticle history:  \nReceived Mar 6, 2023 Revised Oct 10, 2023 Accepted Oct 31, 2023  \nKeywords:  \nCustomers churn Machine learning Metaheuristic  \nParticle swam optimization Prediction  \nSupport vector regression  \nCorresponding Author:  \nMetaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR) . SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.  \nThis is an open access article under the CC BY-SA license.  \nRahmad B.Y. Syah  \nDepartment of Informatics, Faculty of Engineering, Universitas Medan Area St. Setia Budi No.79 B, Medan, Indonesia  \nEmail: [rahmadsyah@uma.ac.id](rahmadsyah@uma.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe goal of metaheuristics is to solve problems faster and to be able to solve complex problems. Algorithms are used in metaheuristics to solve optimization problems [1] . The metaheuristic algorithm combines natural phenomena's rules and randomness to find the best result globally. Its application in various cases demonstrates metaheuristics efficiency and effectiveness in solving large and complex problems [2] .  \nMachine learning benefits businesses by accelerating growth, generating new revenue streams, and resolving issues. Data is an important driver in business decisions, but companies have traditionally used data from a variety of sources, including customer, employee, and financial feedback [3], [4] . Machine learning automates and optimizes the process of rapidly analyzing large amounts of data, allowing businesses to achieve results faster [5] . Churn customers can have a significant impact on a business because acquiring new users costs more money and effort than retaining existing ones. So, it is preferable if you can maintain customer loyalty by taking preventive measures against churn [6] .  \nIn this study, churn refers to customers who no longer make transactions within a specific time frame. Customer churn is an important factor to consider when evaluating a company or business. This is because the  \ncompany's goal is to acquire as many customers as possible, and retaining customers is more difficult. If the company is unable to retain customers, it will fall behind, and the cycle of company performance will decrease and decline [7], [8] .  \nPredictions are estimates based on past and present data. The goal of prediction is to gather information about future changes ","cbCainqFUMgXb9Wy","https://ap.wps.com/l/cbCainqFUMgXb9Wy","pdf",576364,1,10,"English","en",105,"# INTRODUCTION\n# METHOD","[{\"question\":\"What does “customer churn” mean in this research?\",\"answer\":\"Customer churn refers to customers who no longer make transactions within a specific time frame, indicating they stop using the service or leave the company.\"},{\"question\":\"How do PSO and SVR work together in the proposed model?\",\"answer\":\"PSO searches for parameter criteria using a swarm-based optimization process, then the best positions are re-evaluated with SVR, which predicts values of continuous variables for churn-related outcomes.\"},{\"question\":\"What efficiency enhancements are added to improve prediction results?\",\"answer\":\"The model adds two efficiency optimizations: prediction flexibility and risk minimization, aiming to reduce customer churn through better predictive performance.\"}]","An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based | 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