[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125348-en":3,"doc-seo-125348-105":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},125348,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine learning-based B2C software project success prediction model in Indonesia - Research","Software project success is essential in the information technology industry, yet it is difficult to predict because of complexity and rapidly changing conditions. This research develops a machine learning model to predict the success of B2C e-business software projects in Indonesia using 28 variables collected from historical project records. Support Vector Machine and Artificial Neural Network models are trained with hyperparameter tuning via Grid Search, including preprocessing with synthetic data generation and imbalance handling (SMOTE, ADASYN, and variants). Model quality is assessed using a confusion matrix, while Shapley Additive Explanations supports feature importance and automated recommendations, achieving best accuracy of 87.8%.","Machine learning-based B2C software project success prediction model in Indonesia  \nRudi Setiawan a, 1,*, Titik Khawa Abdul Rahman b,2  \na Department of Information Systems, Trilogi University, South Jakarta, 12760, Indonesia b School of Science and Technology, Asia e University, 47500, Shah Alam, Malaysia  \n[1](1 rudi@trilogi.ac.id)[ rudi@trilogi.ac.id](1 rudi@trilogi.ac.id); [2](2 titik.khawa@aeu.edu. my)[ titik.khawa@aeu.edu. my](2 titik.khawa@aeu.edu. my)  \n* corresponding author  \nARTICLE INFO ABSTRACT  \n\n| Article history\u003Cbr>Received June 10, 2025\u003Cbr>Revised July 3, 2025\u003Cbr>Accepted July 30, 2025\u003Cbr>Available online August 31, 2025\u003Cbr>Keywords\u003Cbr>Software project prediction Project success prediction Success and failure software project Software prediction\u003Cbr>Software project management | \u003Cbr>The success of a software project is a crucial factor in the information technology industry, but it is often difficult to predict due to its complexity and high dynamics. This research aims to develop a model for predicting the success of software projects, particularly B2C e-business software in Indonesia, utilizing a machine learning approach. This study involved 28 variables that affect the success of software projects obtained from previous research. The dataset was compiled from the historical records of software projects from various software development companies in Indonesia. The predictive model was developed using Support Vector Machine and Artificial Neural Network algorithms, with hyperparameter tuning performed via Grid Search. The modelling process includes the preprocessing stage of data, which involves synthetic data generation due to inadequate data collection, as well as the application of several dataset mining techniques (SMOTE, ADASYN, SMOTE Tomek Links, and ADASYN Tomek Links) . Additionally, model training and performance evaluation are conducted using a confusion matrix. The search for important features using the Shapley Additive Explanations method is also conducted to develop an automated recommendation system based on key factors that require improvement. The results showed that the SVM model with Grid Search tuning of hyperparameters in the SMOTE Tomek Links data test yielded the best performance, with an accuracy of 87.8%, demonstrating the significant potential of machine learning in identifying project success factors from the early stages. This study contributes to the development of decision-support tools for B2C project managers in Indonesia by providing accurate early predictions and interpretable recommendations.\u003Cbr>\u003Cbr>© 2025 The Author(s) .\u003Cbr>This is an open access article under the CC–BY-SA license\u003Cbr> |\n| --- | --- |\n| 1. Introduction\u003Cbr>In the rapidly growing field of software development, the success of software projects is critical for organizations because it provides broad strategic benefits, including operational efficiency and driving product innovation in a competitive market [1], [2]. However, research shows that most projects do not meet their objectives [3], [4] most software projects exceed their established time and budget constraints [5], which often leads to financial losses and waste of resources [6] . The Chaos report from Standish Group highlights that around 65% of software projects face challenges of exceeding budget, being late, or failing due to not meeting quality standards [7]. The alarming trend of software project failures makes it imperative to develop predictive models [8], to be able to assess the likelihood of project success or failure early on [9], [10], thus enabling proactive intervention against the factors that cause failure. |  |\n\nMany factors affect the success of software projects [11], [12], but the Project Planning and Requirement factor is a common factor used by several researchers, including [13]–[15] . Other researches [16], [17] have explored various factors and methodologies to predict the success ofa software project. Similarly, [18] conducted","cbCaidsH7ubMut2s","https://ap.wps.com/l/cbCaidsH7ubMut2s","pdf",1346127,1,19,"English","en",105,"# Introduction\n## Research gaps and motivation\n# Methodology\n## Dataset and variables\n## Modeling algorithms and tuning\n## Data preprocessing and imbalance handling\n## Explainability and feature selection\n## Evaluation using confusion matrix\n# Results and contribution\n## Best-performing configuration\n## Decision-support implication","[{\"question\":\"What is the goal of this study on B2C software projects in Indonesia?\",\"answer\":\"The study aims to build a machine learning model that predicts the success or failure likelihood of B2C e-business software projects in Indonesia at an early stage.\"},{\"question\":\"Which machine learning methods are used and how are hyperparameters tuned?\",\"answer\":\"The model is developed using Support Vector Machine and Artificial Neural Network, with hyperparameter tuning performed through Grid Search.\"},{\"question\":\"How does the study address insufficient or imbalanced data?\",\"answer\":\"It includes a preprocessing stage that generates synthetic data and applies dataset mining techniques such as SMOTE, ADASYN, SMOTE Tomek Links, and ADASYN Tomek Links.\"}]","Machine learning-based B2C software project success prediction model in Indonesia - 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