TY - GEN
T1 - Comparison between rule-based expert support system and machine learning expert support system in KM
AU - Ompusunggu, Louis Dwysevrey
AU - Sensuse, Dana Indra
AU - Wahbi, Andi
AU - Mahdalina, Rahmatul
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Machine Learning is gaining popularity nowadays, but we found some key activities involving the business core of the companies still rely on Rule-Based. Therefore, in terms of Knowledge Management, we tried to figure out how Rule-Based and Machine Learning contribute to the Knowledge Management of business (such as providing insights on business intelligence) and e-learning, specifically through their capability as Expert Support Systems. Eventually we are to figure out the comparison between Rule-Based and Machine Learning Expert Support System in the KM. While it can be arguable for the results if we only rely on the qualitative perspective gained from literature study, we then involve AHP to bring the comparison become real, quantitatively. But for reader's knowledge enhancement, we take the chance to also demonstrate quantitatively in even more real case using Orange. This research shows that ML is better than Rule-Based for some points, but there are also points in which Rule-Based is even better. Therefore, even though ML is a new trend with its undisputed capability, Rule-Based is still need; it is even not a bad idea to consider having hybrid Expert Support System in which both ML and Rule-Based exist. Ultimately, this research should bring the insights about the current usage of both through reading this paper, as well as the understanding about the comparison of both to wisely decide which one is to choose to support future business and or e-learning endeavor.
AB - Machine Learning is gaining popularity nowadays, but we found some key activities involving the business core of the companies still rely on Rule-Based. Therefore, in terms of Knowledge Management, we tried to figure out how Rule-Based and Machine Learning contribute to the Knowledge Management of business (such as providing insights on business intelligence) and e-learning, specifically through their capability as Expert Support Systems. Eventually we are to figure out the comparison between Rule-Based and Machine Learning Expert Support System in the KM. While it can be arguable for the results if we only rely on the qualitative perspective gained from literature study, we then involve AHP to bring the comparison become real, quantitatively. But for reader's knowledge enhancement, we take the chance to also demonstrate quantitatively in even more real case using Orange. This research shows that ML is better than Rule-Based for some points, but there are also points in which Rule-Based is even better. Therefore, even though ML is a new trend with its undisputed capability, Rule-Based is still need; it is even not a bad idea to consider having hybrid Expert Support System in which both ML and Rule-Based exist. Ultimately, this research should bring the insights about the current usage of both through reading this paper, as well as the understanding about the comparison of both to wisely decide which one is to choose to support future business and or e-learning endeavor.
KW - Business
KW - E-learning
KW - Expert support system
KW - Knowledge management
KW - Machine learning
KW - Rule-based
UR - http://www.scopus.com/inward/record.url?scp=85114862772&partnerID=8YFLogxK
U2 - 10.1109/ICSCEE50312.2021.9498112
DO - 10.1109/ICSCEE50312.2021.9498112
M3 - Conference contribution
AN - SCOPUS:85114862772
T3 - 2021 2nd International Conference on Smart Computing and Electronic Enterprise: Ubiquitous, Adaptive, and Sustainable Computing Solutions for New Normal, ICSCEE 2021
SP - 114
EP - 120
BT - 2021 2nd International Conference on Smart Computing and Electronic Enterprise
A2 - Abu Bakar, Zainab Binti
A2 - AI-Sammarraie, Najeeb Abbas
A2 - EI-Ebiary, Yousef Abu Baker
A2 - Al Moaiad, Yazeed
A2 - Yusoff, Fakhrul Hazman
A2 - AI-Khasawneh, Mahmoud
A2 - Bamansoor, Samer
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021
Y2 - 15 June 2021 through 16 June 2021
ER -
