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Automatic Multi-label Classification for GDP Economic-phenomenon News
Junardi W.a, Khodra M.L.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, 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]© 2020 IEEE.GDP is a measure of a country’s economy. One of the variables used in the process of compiling GDP is the news analysis of economic phenomena. Briefly, the GDP figures are getting better if they are in line with the economic phenomena that occur. Analysis of this economic phenomenon can also be a supporting component of GDP publicity. This study aims to investigate the best classification model for economic phenomenon news to speed up the analysis process. We use a multi-label classification method because each news item has one or more categories. The label in this study corresponds to the GDP by Industry which refers to the Indonesian Standard Industrial Classification (KBLI). We use the Problem Transformation approach in combination with several single label classification algorithms. Label Power-set method combined with Linear SVC showed a better result among others, it reaches 75% for F-Measure and 0.021 for Hamming Loss in 5-fold cross-validation. However, the model performance increase to 84% in testing mode.[/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]Analysis process,Classification algorithm,Classification models,Cross validation,Industrial classifications,Model performance,Multi label classification,Problem transformations[/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]Indonesian Economic News,Industrial Classification,Multi-label Classification,Problem Transformation[/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]We appreciate the BPS-Statistic of Indonesia for providing the data to support this research. Our gratitude also addressed to Institut Teknologi Bandung for giving research environment and education. Also, Our gratefulness to the Ministry of Information and Communication (Kemkominfo) for the scholarship granted.[/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/ICISS50791.2020.9307579[/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]