Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques
To run a software development project, an effective and efficient project management mechanism is needed to coordinate the activities carried out. The agile method was developed because there are several weaknesses in the classic method that can interfere with the course of the software development...
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| Format: | Article |
| Language: | English |
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2023-12-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/57 |
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| _version_ | 1832583814754336768 |
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| author | Muchamad Bachram Shidiq Windu Gata Sigit Kurniawan Dedi Dwi Saputra Supriadi Panggabean |
| author_facet | Muchamad Bachram Shidiq Windu Gata Sigit Kurniawan Dedi Dwi Saputra Supriadi Panggabean |
| author_sort | Muchamad Bachram Shidiq |
| collection | DOAJ |
| description | To run a software development project, an effective and efficient project management mechanism is needed to coordinate the activities carried out. The agile method was developed because there are several weaknesses in the classic method that can interfere with the course of the software development process according to user desires. However, in applying agile methods, time effort estimation cannot be done properly. This can cause project managers to have difficulty preparing resources in software development in scrum projects. For this reason, this research aims to predict the time effort of agile software development using Machine Learning techniques, namely the Decision Tree, Random Forest, Gradient Boosting, and AdaBoost algorithms, as well as the use of feature selection in the form of RRelieff and Principal Component Analysis (PCA) to improve prediction accuracy. The best-performing algorithm uses Gradient Boosting k-fold validation with PCA with an MSE value of 2.895, RMSE 1.701, MAE 0.898, and R2 0.951. |
| format | Article |
| id | doaj-art-cbd6067974944f08987fe4aaef3b7bd3 |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-cbd6067974944f08987fe4aaef3b7bd32025-01-28T05:41:12ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082023-12-01132394810.35585/inspir.v13i2.5757Time Effort Prediction Of Agile Software Development Using Machine Learning TechniquesMuchamad Bachram Shidiq0Windu Gata1Sigit Kurniawan2Dedi Dwi Saputra3Supriadi Panggabean4Universitas Nusa MandiriUniversitas Nusa MandiriUniversitas Teknologi Muhammadiyah JakartaUniversitas Siber IndonesiaUniversitas DarunnajahTo run a software development project, an effective and efficient project management mechanism is needed to coordinate the activities carried out. The agile method was developed because there are several weaknesses in the classic method that can interfere with the course of the software development process according to user desires. However, in applying agile methods, time effort estimation cannot be done properly. This can cause project managers to have difficulty preparing resources in software development in scrum projects. For this reason, this research aims to predict the time effort of agile software development using Machine Learning techniques, namely the Decision Tree, Random Forest, Gradient Boosting, and AdaBoost algorithms, as well as the use of feature selection in the form of RRelieff and Principal Component Analysis (PCA) to improve prediction accuracy. The best-performing algorithm uses Gradient Boosting k-fold validation with PCA with an MSE value of 2.895, RMSE 1.701, MAE 0.898, and R2 0.951.https://ojs.unitama.ac.id/index.php/inspiration/article/view/57adaboostagiledecision treegradient boostingrandom forest |
| spellingShingle | Muchamad Bachram Shidiq Windu Gata Sigit Kurniawan Dedi Dwi Saputra Supriadi Panggabean Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques Inspiration adaboost agile decision tree gradient boosting random forest |
| title | Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques |
| title_full | Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques |
| title_fullStr | Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques |
| title_full_unstemmed | Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques |
| title_short | Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques |
| title_sort | time effort prediction of agile software development using machine learning techniques |
| topic | adaboost agile decision tree gradient boosting random forest |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/57 |
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