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[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]© 2019 IEEE.Companies want their business to run well and can survive in competition. To achieve these desires, the right strategy is needed in carrying out the business process. One of the initial approaches to implementing this strategy is by analyzing the company’s environmental factors. There are two types of corporate environmental factors, namely internal and external factors. The stage that is difficult to do in the analysis of the company’s environment lies in the analysis of the external environment because to conduct an external environment analysis is required to collect data that is not owned by the company. Strategies for the company’s external factors can use existing methods in competitive intelligence, namely PEST and SWOT analysis. The method that focuses on external factors in SWOT analysis is EFAS metric. To overcome the problem of analyzing external environment, this study focus on determining EFAS metrics by using data from online news titles as a source of determining the company’s external factors. The determination of EFAS metrics is carried out with two main modules, namely the text mining module and priority module using the AHP or TOPSIS algorithm. Titles obtained from online sites are classified using text mining and then given priority weight using the AHP or TOPSIS algorithm. The results show that text mining modules and priority modules applied to EFAS metrics produce 10 errors in classification testing data from a total of 40 data in the EFAS metric. So the results of the questionnaire showed an accuracy value of 75%. While the experimental results of the text mining module produce the SVM algorithm as the best algorithm compared to the Naïve Bayes and J48 algorithms with F-Measure values of 0.782.[/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]Business Process,Environmental factors,External environments,External factors,Internal and external factors,Priority weights,SVM algorithm,Topsis algorithms[/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]AHP,EFAS,Online news,SWOT,Text mining,TOPSIS[/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/10.1109/ICST47872.2019.9166211[/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]