TY - GEN
T1 - Analysis of Success Factors Ranking
T2 - 13th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021
AU - Wijayanto, Riko
AU - Raharjo, Teguh
AU - Hardian, Bob
AU - Suhanto, Agus
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - A research about software development project revealed that only 31% projects were successfully conducted and the entire was challenged. On the other report, 62% IT projects failed in terms of delivery time, 49% over the cost, and 47% projects obtained higher maintenance cost. In the era of the machine learning also digital intelligence as now, e-commerce located in Indonesia competes to win a market acquisition by giving recommended products through the digital intelligence approach. But this key objective has a challenge in terms of the deliverable process. Study case from an e-commerce in Indonesia stated that given seven projects, only one projects or system still being used until the end, two projects were decommissioned, and also a project was halted due to facing a lot of issues. Hence, this applied research was initiated to identify the rank of success factors in the field of machine learning projects. Qualitative combined by quantitative methods were used sequentially to get the critical factors and measure the rank based on hierarchical analytics using AHP. Six projects' success criteria and fifteen success factors criteria evaluated by the four experts on high level position both developers and project managers. The highest rank of the projects' success criteria is user's satisfaction by 27.7%, meet the quality standard by 22.8%, and meet the time by 18.8%. In terms of critical success factors, the top three are clarity on the project's goal and objective by 15.25%, project manager's capability by 9.37%, and followed by clear communication by 8.82%.
AB - A research about software development project revealed that only 31% projects were successfully conducted and the entire was challenged. On the other report, 62% IT projects failed in terms of delivery time, 49% over the cost, and 47% projects obtained higher maintenance cost. In the era of the machine learning also digital intelligence as now, e-commerce located in Indonesia competes to win a market acquisition by giving recommended products through the digital intelligence approach. But this key objective has a challenge in terms of the deliverable process. Study case from an e-commerce in Indonesia stated that given seven projects, only one projects or system still being used until the end, two projects were decommissioned, and also a project was halted due to facing a lot of issues. Hence, this applied research was initiated to identify the rank of success factors in the field of machine learning projects. Qualitative combined by quantitative methods were used sequentially to get the critical factors and measure the rank based on hierarchical analytics using AHP. Six projects' success criteria and fifteen success factors criteria evaluated by the four experts on high level position both developers and project managers. The highest rank of the projects' success criteria is user's satisfaction by 27.7%, meet the quality standard by 22.8%, and meet the time by 18.8%. In terms of critical success factors, the top three are clarity on the project's goal and objective by 15.25%, project manager's capability by 9.37%, and followed by clear communication by 8.82%.
KW - AHP
KW - e-commerce
KW - machine learning
KW - project management
KW - success factor ranking
UR - http://www.scopus.com/inward/record.url?scp=85123861960&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS53237.2021.9631353
DO - 10.1109/ICACSIS53237.2021.9631353
M3 - Conference contribution
AN - SCOPUS:85123861960
T3 - 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021
BT - 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2021 through 26 October 2021
ER -