[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123948-en":3,"doc-seo-123948-105":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},123948,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Development Prediction Model to Optimize Cooperative Loans - Based on Machine Learning Algorithms","Loan defaults by borrowers pose a direct risk to the financial stability and business performance of Savings and Loans Cooperatives. This research develops a loan-default prediction model by comparing decision tree, K-NN, logistic regression, and random forest models to determine the most effective and accurate approach. Model performance is evaluated using accuracy, precision, recall, and F1-score. The study uses a cooperative loan dataset containing borrower profile, loan amount, installment counts, and related attributes, split into training and test sets to support lending decision-making.","Development Prediction Model to Optimize Cooperative Loans Based on Machine Learning Algorithms  \nHidayatulloh Himawan1, Tito Pinandita2, Rizky Ridwan3, Hilmi Aziz4  \n1Department of Informatics, UPN Veteran Yogyakarta, Indonesia 2Department of Informatics, Muhammadiyah Purwokerto University, Indonesia 3Department of Accounting, Cipasung University, Tasikmalaya, Indonesia 4Institute of Advanced Informatics and Computing, Indonesia  \n[1](1if.iwan@upnyk.ac.id)[if.iwan@upnyk.ac.id](1if.iwan@upnyk.ac.id), [2](2titop@ump.ac.id)[titop@ump.ac.id](2titop@ump.ac.id), [3](3rizkyridwan@uncip.ac.id)[rizkyridwan@uncip.ac.id](3rizkyridwan@uncip.ac.id), [4](4hilmi@iaico.org)[hilmi@iaico.org](4hilmi@iaico.org)  \n\n| ARTICLE INFORMATION |\n| --- |\n| Article History:\u003Cbr>Received: April 23, 2024\u003Cbr>Last Revision: May 10, 2024\u003Cbr>Published Online: May 13, 2024 |\n| KEYWORDS |\n| Decision Tree,\u003Cbr>Default Prediction,\u003Cbr>Logistic Regression,\u003Cbr>Random Forest,\u003Cbr>K-NN |\n| CORRESPONDENCE |\n| Phone: +62 812-2732-222\u003Cbr>E-mail: [if.iwan@upnyk.ac.id](if.iwan@upnyk.ac.id) |\n\nABSTRACT  \nDefault on loans by borrowers to the cooperative to optimize the cooperative's business performance. In this research, a default prediction model was developed using several quite popular machine learning algorithms, namely decision tree, K-NN, logistic regression, and random forest, then all models with each of these algorithms were compared and evaluated. to find out which algorithm model is the most effective and accurate in predicting loan defaults in cooperatives. Model evaluation is carried out using metrics such as accuracy, precision, recall, and f1-score. The dataset used in this research was obtained from the loan list at one of the Savings and Loans Cooperatives in Tasikmalaya Regency, the contents of which include attributes such as borrower profile, loan amount, number of installments, and others. This dataset is divided into training data and test data to train and evaluate the model. These machine learning algorithms were chosen because they are quite well known among other algorithms for prediction and have been proven in several financial studies. The results of this prediction model can be used by cooperatives to support decisions in providing appropriate loans.  \n1. INTRODUCTION  \nIn carrying out its main business activities, the Savingsand Loans Cooperative always tries to provide loans with the hope that they will be right on target and optimally for members to get profits which will later return to the members [1] . However, savings and loans cooperativesoften face the risk of bankruptcy due to the large number of loan defaults that occur [2] . This could threaten the financial stability of the cooperative which could even result in losses for members. From research [3] that in cooperatives there have been no efforts that are deemed effective enough to reduce bad credit in cooperatives because cooperatives do not have reliable credit analysis like banking.  \nTherefore, it is necessary to carry out risk analysis and develop an accurate and effective loan payment failure prediction model to minimize the risk of loss [4] . The  \nresults of this prediction model will later be used as decision support in optimizing lending to improve business performance and minimize the risk of bankruptcy [5] .  \nThis prediction model can be created using traditional machine learning algorithms such as Decision Tree, KNearest Neighbors, and others. You can also use several Deep Learning algorithms such as Recurrent Neural Network and Convolutional Neural Network. Each of these algorithms has advantages and disadvantages because they can create prediction models with their own characteristics/methods. From research [6] and more complex, which is not suitable for this cooperative dataset, which is quite small. This was also found in credit risk analysis research [7] who found that tree-based models were more stable than models based on multilayer artificial neural networks. The ","cbCaia0sUe9cFUjE","https://ap.wps.com/l/cbCaia0sUe9cFUjE","pdf",376977,1,6,"English","en",105,"# Article Information\n# Abstract\n# Introduction\n## Background and Risk of Loan Defaults\n## Need for Risk Analysis and Prediction Modeling\n## Choice of Traditional Machine Learning Models\n## Research Aim and Expected Impact","[{\"question\":\"Which machine learning algorithms are compared for predicting cooperative loan defaults?\",\"answer\":\"The study compares decision tree, K-NN, logistic regression, and random forest models.\"},{\"question\":\"How is model performance evaluated in the research?\",\"answer\":\"Evaluation uses accuracy, precision, recall, and F1-score metrics.\"},{\"question\":\"What dataset is used to build and test the prediction models?\",\"answer\":\"The dataset comes from a Savings and Loans Cooperative loan list in Tasikmalaya Regency and includes borrower profile, loan amount, number of installments, and other related attributes, split into training and test sets.\"}]","Development Prediction Model to Optimize Cooperative Loans - 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