Examining Students’ Opinions about NDUM e-Learning during COVID-19 is a study to improve the NDUM e-Learning system based on Sentiment Analysis approach. Firstly, surveys are distributed among the students to get feedback related to NDUM e-Learning. From the feedback, useful data can be collected for data analytic process. CRISP-DM framework and Opinion Mining architecture is used for this study. Next, Sentiment Analysis is implemented to classify the student’s feedback into “positive” and “negative” sentiment. Further, two machine learning algorithms (i.e. Decision Tree and SVM) are used to construct the classifier model. Finally, results are displayed in graphical form to illustrate findings of the study. The result provides an overview of the level of satisfaction, suitability, system improvement, suggestions and the overall impact of the use of NDUM E-Learning during the pandemic. first, second, and third level headings (first level heading)

1.
Ahmed
,
A. S. A. M. S.
,
Malik
,
M. H.
,: Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes. In
2020 International Conference on De-cision Aid Sciences and Application (DASA)
, pp.
663
668
,
IEEE
(
2020
).
2.
Akbar
,
Arminditya
S.
,
Harry
P.
,
Panca
O. H.
,
Yudhoatmojo
,
Satrio
:
User Perception Analy-sis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques
.
Jurnal Sistem Informasi
,
17
, pp
33
47
(
2021
).
3.
Auliya
R. I.
,
Jepi
S.
,
Muhammad
P. K.
,:
Implementation of K-Nearest Neighbor (K-NN) Al-gorithm for Public Sentiment Analysis of Online Learning, IJCCS
(
Indonesian Journal of Computing and Cybernetics Systems)
, Vol.
15
, No.
2
, pp.
121
130
(
2021
).
4.
Bhagat
,
K.K.
,;
Mishra
,
S.
,;
Dixit
,
A.
,;
Chang
,
C.-Y.
,:
Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach
, In
Sustainability
, Vol.
13
(
3346
) (
2021
).
5.
Cooper
S.
,
Sahami
M.
,:
Reflections on Stanford’s MOOCs
.
Communications of the ACM
,
56
(
2
), pp.
28
30
(
2013
).
6.
Deraman
,
Noor
B.
,
Alya
G. D.
,
Siti
M.
,:
Mining social media opinion on online distance learning issues during and after movement control order (MCO) in Malaysia using topic modeling approach
. In
Int. Journal of Advanced Tech. and Eng. Exploration
,
8
, pp.
2394
7454
(
2021
).
7.
Dina
N. Z.
,
Yunardi
R. T.
,
Firdaus
A. A.
,:
Utilizing text mining and feature-sentiment-pairs to support data-driven design automation Massive Open Online Course
. In
Int. Journal of Emerging Technologies in Learning (iJET)
,
16
(
01
), pp.
134
151
(
2021
).
8.
Ferreira-Mello
R.
,
André
M
,
Pinheiro
A.
,
Costa
E.
,
Romero
C.
,:
Text mining in education
.
Wiley Interdisciplinary Rev. Data Mining. Knowledge Discovery
(
2019
).
9.
Hongbo
Du
:
Data mining techniques and applications: an introduction
.
Cengage Learning
(
2010
).
10.
Layth
Almahadeen
,
Murat
Akkaya
,
Arif
Sari
:
Mining Student Data Using Crisp-DM Mod-el
. In
Int. Journal of Computer Science and Information Security
, Vol.
15
, No.
2
(
2017
).
11.
Lee
S.-W.
,
Jiang
G.
,
Kong
H.-Y.
,
Liu
C.
,:
A difference of multimedia consumer’s rating and review through sentiment analysis
.
Multimedia Tools and Applications
, Volume
80
, Issue
26-27
Nov (
2021
).
12.
Martin
F. G.
Will massive open online courses change how we teach? Communications of the ACM
,
55
(
8
), pp.
26
28
(
2012
).
13.
Medhat
W.
,
Hassan
A.
,
Korashy
H.
,:
Sentiment analysis algorithms and applications: A sur-vey
.
Ain Shams Engineering Journal
,
5
,
1093
1113
(
2014
).
14.
R.
Watrianthos
,
S.
Suryadi
,
D.
Irmayani
,
M.
Nasution
, and
E. F. S.
Simanjorang
:
Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method
, In
Int. J. Science Tech-nology Res.
, vol.
8
, no.
07
, pp.
786
788
(
2019
).
15.
Rahmawati
P.
,
Larasat
,
A.
,
Farhan
M.
,
Hajji
A. M.
,
Fanani
N. A.
,:
Understanding user feedback on Learning Management System of SIPEJAR by using text mining techniques
.
IOP Con-ference Series. Materials Science and Engineering; Bristol
Vol.
1072
, Issue
1
(
2021
).
16.
Syafrida Hafni
S.
,:
Online learning sentiment analysis during the covid-19 Indonesia pan-demic using twitter data
.
IOP Conf. Ser.,: Mater. Sci. Eng.
1156
012011
(
2021
).
17.
Syahaneim
M.
,
Nur Hidayah
M. D.
,
Zuraini
Z.
,
Omar
Z.
,:
Framework of Knowledge-Based System for United Nations Peacekeeping Operations using Data Mining Technique
,
2018 4th Int. Conf. on Info. Retrieval and Knowledge Mgmt (CAMP), 2018
, pp.
1
6
(
2018
).
18.
Syahriani
,
A. A.
Yana
, and
T.
Santoso
:
Sentiment analysis of Facebook comments on In-donesian presidential candidates using the Naïve Bayes method
. In
Journal of Physics: Con-ference Series
, vol.
1641
, pp.
012012
(
2020
).
19.
Vijay
Kotu
,
Bala
Deshpande
:
Data Science: Concepts and Practice
.
Morgan Kaufman
(
2019
).
20.
Wiemer
,
H.
Drowatzky
,
L.
Ihlenfeldt
:
Data Mining Methodology for Engineering Applica-tions (DMME)—A Holistic Extension to the CRISP-DM Model (Application Science)
Vol.
9
(
2019
).
This content is only available via PDF.