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Integrated social media knowledge capture in medical domain of Indonesia
Surendro K.a, Satya D.P.a, Yodihartomo F.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung (ITB), Bandung, 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]© 2018 Universitas Ahmad Dahlan.The Social Media Platforms, as the one of largest part of today data traffic on the Internet, disseminate a vast volume of information, including medical information in it. Knowledge management system (KMS) approach is applied with purpose to capture, maintain, and manage tacit or explicit knowledge available and collected within the social media platforms, organization’s database, knowledge base, or document repository. By adding Indonesian Natural Language Processing (InaNLP), Machine Learning and Data Mining approach, our research has proposed a framework which is theoretically designed to improve the previous research related to social media knowledge capture model and enhance its accuracy and reliability of knowledge retrieved compared to previous knowledge capture model. This system mainly aimed for medical practitioner to give a quick suggestion of the diseases regarding to the early diagnose which has been taken in the first place. On this current research state, the pre-processing phase of the framework implementation and knowledge presentation is our main concernto maximize the information value for the knowledge users and also to reduce the language issues in texts such as ambiguity, inconsistency, use of slang vocabulary, etc.According to this research’s goal, we have designed an algorithm to extract feature from dataset.[/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][/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]Data mining,Knowledge management system,Machine learning,Medical knowledge,Natural language processing[/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]This study is financially supported by the Institut Teknologi Bandung Program Penelitian, Pengabdian kepada Masyarakat, dan Inovasi (P3MI).[/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/10.12928/TELKOMNIKA.v16i4.8320[/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]