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Public perceptions of online learning in developing countries: A study using the ELK stack for sentiment analysis on twitter
Persada S.F.a, Oktavianto A.a, Miraja B.A.a, Nadlifatin R.a, Belgiawan P.F.b, Redi A.A.N.P.c
a Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
b Institut Teknologi Bandung, Bandung, Indonesia
c Universitas Pertamina, Jakarta, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Kassel University Press GmbH.This study explores public perceptions of online learning applications in Indonesia. Many studies about online learning were done in developed countries and only a few in developing countries. Moreover, these studies used a qualitative approach which limits the results to be applied in different settings. While traditional research using a survey to understand people’s perceptions towards an entity requires a lot of time and effort, we used efficient and effective manners to gather opinions and then analyzed its sentiments using the Logstash, Kibana, and Python programming language (ELK stack) and Naive Bayes algorithm. We used the Naive Bayes algorithm for sentiment analysis and ELK stack for storing & gathering tweets from Twitter. With ELK stack, we successfully collected 133.477 tweets related to online learning. From this study, we understood what kind of words that are sentimentally positive and negative tweets. We also gained some insights regarding Indonesia’s student online learning application preferences.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Developed countries,Indonesia,Naive-Bayes algorithm,Online learning,Public perception,Python programming language,Qualitative approach[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Developing country,ELK stack,Online learning,Sentiment analysis,Twitter[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.3991/ijet.v15i09.11579[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]