The Analysis Factor Effecting the User Satisfaction in Software Project using Data Mining Techniques
Article Sidebar
Main Article Content
Abstract
This study aimed to analyze the factors affecting the satisfaction of users participating in software projects. The data was analyzed from 191 projects with 71 factors by using data mining techniques. The methodology consisted of three main steps: 1) Preparation of information, 2) Factor selection using feature selection techniques: Correlation, Chi-Square, Forward, Backward Elimination, Evolutionary, Information Gain, and Gain Ratio, and 3) Efficiency test by using Backpropagation Neural Network Model.
The results of the research were as follows: The result revealed that 33 factors from the Evolutionary technique provided the highest predictive percentage at 90.61. The result can be employed as basic information for software project implementation to satisfy users and applied as data to develop a more effective satisfaction predictive model.
Article Details
References
สุวิมล สิทธิชาติ. (2017). การวิเคราะห์คุณลักษณะพื้นฐานทางการศึกษาด้วยเทคนิคเหมืองข้อมูล. วารสารเทคโนโลยีสารสนเทศ ปีที่ 13, ฉบับที่ 2 กรกฎาคม-ธันวาคม หน้า 20-28.
Comptia. (2020). IT industry outlook 2021. Retrieved from https://connect.comptia.org/
content/research/it-industry-trends-analysis.
C. Venugopal, S. P. Devi and K. S. Rao. (2010). Predicting ERP user satisfaction and adaptive neuro fuzzy interface system (ANFIS) approach. Intelligent Information Management, 2, pp.422-430. doi: https://10.4236/iim.2010.27052.
International Software Benchmarking Group. (2018). ISBSG Repository Data Release 12 http://www.isbsg.org.
International Software Benchmarking Standard Group. (2018). Guideline of use the ISBSG dataset. http://www.isbsg.org.
J. Han and M. Kamber. (2001). Data Mining Concepts and Techniques. Morgan Kaufman Publishers.
J. Venkatesh, P. Cherurveettil, Thenmozhi. S and Balasubramanie. (2012). Analyses usage of process performance models to predict customer satisfaction. International Journal of Computer Applications, Vol.47, No.12, June, pp.35-40. doi: https://10.5120/7243-0288.
K. Esaki. (2013). Prediction models for total customer satisfaction based on the ISO/IEC 9126 system
quality model. American Journal of Operations Research, Vol.3, pp.393-401. doi: http://10.4236/ajor.2013.34037.
K. M. Alhendawi and A. S. Baharudia. (2014). A classification model for predicting web users satisfaction with information systems success using data mining techniques. Journal of Software Engineering, Vol.8, No.4, pp.265-277. doi: https://10.3923/jse.2014.265.277.
K. P. Engelbrecht, F. Hartard, H. Katabdar and S. Moller. (2009). Modeling user satisfaction with hidden markov models. pp.170-177.
doi:https://dl.acm.org/doi/pdf/10.5555/1708376.1708402.
L.Radlinski. (2015). Preliminary evaluation of schemes for predicting user satisfaction with the ability of system to meet state objectives. Journal of Theoretical and Applied Computer Science. Vol.9, No.2, 2015. pp.32-50.
M. Bano, D. Zowghi and F. D. Rimini. (2016). User satisfaction and system success: an empirical exploration of user involvement in software development. Empirical Software Engineering. pp.1-36. doi: https://10.1007/s10664-016-94651.
S. Petter, W. DeLone and E. McLean. (2008). Measuring information system success: models, dimensions, measures, and interrelationships.European Journal of Information System, 17, pp.236-263, doi: https://doi.org/10.1057/ejis.2008.15
T. Nafees. (2011). Impact of user satisfaction of software quality in use. International Journal of Electrical & Computer Science Vol.11, No.03, pp.48-56.
V. Kotu and B Deshpande. (2019). Data Science Concepts and Practices. Elsevier.
X. Xia, D. Lo, J. Tang and S. Li. (2015). Customer satisfaction feedback in an IT outsourcing company: a case study on the insigma
hengtian company. April 27-29. doi: https://dl.acm.org/doi/10.1145/2745802.2745834.