Predicting the Readiness of Indonesia Manufacturing Companies toward Industry 4.0: A Machine Learning Approach
This research discusses Indonesia's readiness to implement industry 4.0. We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and redu...
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Veröffentlicht in: | Jurnal Teknik Industri (Surabaya) 2021-06, Vol.23 (1), p.1-10 |
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description | This research discusses Indonesia's readiness to implement industry 4.0. We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and reduced them using principal component analysis into 11 variables. Using clustering analysis on the reduced dataset, we found three clusters representing the readiness level in implementing industry 4.0. Finally, we used the decision tree for analysing the classification rules. As the finding of this study, Total book value of the machine is the variable that defined the readiness of a company in industry 4.0. The bigger those values are, the more ready a company to compete in industry 4.0. The other measures, i.e., Total cost of revenue by total revenue; Direct labor cost; Total revenue/Total employee and Transportation cost/Total revenue, will define the readiness of a manufacturing company to transform into industry 4.0. or not ready to transform into industry 4.0. |
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We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and reduced them using principal component analysis into 11 variables. Using clustering analysis on the reduced dataset, we found three clusters representing the readiness level in implementing industry 4.0. Finally, we used the decision tree for analysing the classification rules. As the finding of this study, Total book value of the machine is the variable that defined the readiness of a company in industry 4.0. The bigger those values are, the more ready a company to compete in industry 4.0. The other measures, i.e., Total cost of revenue by total revenue; Direct labor cost; Total revenue/Total employee and Transportation cost/Total revenue, will define the readiness of a manufacturing company to transform into industry 4.0. or not ready to transform into industry 4.0.</description><identifier>ISSN: 1411-2485</identifier><identifier>EISSN: 2087-7439</identifier><identifier>DOI: 10.9744/jti.23.1.1-10</identifier><language>eng</language><publisher>Surabaya: Universitas Kristen Petra / Petra Christian University</publisher><subject>Annual reports ; Artificial intelligence ; Automation ; Book value ; Classification ; Cluster analysis ; Clustering ; Cost analysis ; Datasets ; Decision trees ; Direct labor costs ; Economic summit conferences ; Employees ; Industry 4.0 ; Machine learning ; Macroeconomics ; Manufacturing ; Operating costs ; Principal components analysis ; Production costs ; Raw materials ; Revenue ; Stock exchanges ; Variables</subject><ispartof>Jurnal Teknik Industri (Surabaya), 2021-06, Vol.23 (1), p.1-10</ispartof><rights>2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Tanjung, Sean Yonathan</creatorcontrib><creatorcontrib>Yahya, Kresnayana</creatorcontrib><creatorcontrib>Halim, Siana</creatorcontrib><title>Predicting the Readiness of Indonesia Manufacturing Companies toward Industry 4.0: A Machine Learning Approach</title><title>Jurnal Teknik Industri (Surabaya)</title><description>This research discusses Indonesia's readiness to implement industry 4.0. We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and reduced them using principal component analysis into 11 variables. Using clustering analysis on the reduced dataset, we found three clusters representing the readiness level in implementing industry 4.0. Finally, we used the decision tree for analysing the classification rules. As the finding of this study, Total book value of the machine is the variable that defined the readiness of a company in industry 4.0. The bigger those values are, the more ready a company to compete in industry 4.0. The other measures, i.e., Total cost of revenue by total revenue; Direct labor cost; Total revenue/Total employee and Transportation cost/Total revenue, will define the readiness of a manufacturing company to transform into industry 4.0. or not ready to transform into industry 4.0.</description><subject>Annual reports</subject><subject>Artificial intelligence</subject><subject>Automation</subject><subject>Book value</subject><subject>Classification</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Cost analysis</subject><subject>Datasets</subject><subject>Decision trees</subject><subject>Direct labor costs</subject><subject>Economic summit conferences</subject><subject>Employees</subject><subject>Industry 4.0</subject><subject>Machine learning</subject><subject>Macroeconomics</subject><subject>Manufacturing</subject><subject>Operating costs</subject><subject>Principal components analysis</subject><subject>Production costs</subject><subject>Raw materials</subject><subject>Revenue</subject><subject>Stock exchanges</subject><subject>Variables</subject><issn>1411-2485</issn><issn>2087-7439</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNotkEtLw0AURgdRsNQu3Q-4Trzzziyl-ChUfND9MJmHTrFJnUmQ_nsT9H6L-y0O98JB6JpArRXnt_sh1ZTVpCYVgTO0oNCoSnGmz9GCcEIqyhtxiVal7GEaCQ1ItUBvrzn45IbUfeDhM-D3YH3qQim4j3jT-X7qyeJn243RumHMM7juD0fbpVDw0P_Y7GdwLEM-YV7DFbqI9quE1f9eot3D_W79VG1fHjfru23lpKQV44xEJ0G2QjgefVAaCOVRtNY2MqomgNO-tSryqFvlvQxOcKm1YG2rKGNLdPN39pj77zGUwez7MXfTR8Og0SBEQ-hEVX-Uy30pOURzzOlg88kQMLM3M3kzlBkyB9gvz3xgnw</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Tanjung, Sean Yonathan</creator><creator>Yahya, Kresnayana</creator><creator>Halim, Siana</creator><general>Universitas Kristen Petra / Petra Christian University</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20210601</creationdate><title>Predicting the Readiness of Indonesia Manufacturing Companies toward Industry 4.0</title><author>Tanjung, Sean Yonathan ; 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We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and reduced them using principal component analysis into 11 variables. Using clustering analysis on the reduced dataset, we found three clusters representing the readiness level in implementing industry 4.0. Finally, we used the decision tree for analysing the classification rules. As the finding of this study, Total book value of the machine is the variable that defined the readiness of a company in industry 4.0. The bigger those values are, the more ready a company to compete in industry 4.0. The other measures, i.e., Total cost of revenue by total revenue; Direct labor cost; Total revenue/Total employee and Transportation cost/Total revenue, will define the readiness of a manufacturing company to transform into industry 4.0. or not ready to transform into industry 4.0.</abstract><cop>Surabaya</cop><pub>Universitas Kristen Petra / Petra Christian University</pub><doi>10.9744/jti.23.1.1-10</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Annual reports Artificial intelligence Automation Book value Classification Cluster analysis Clustering Cost analysis Datasets Decision trees Direct labor costs Economic summit conferences Employees Industry 4.0 Machine learning Macroeconomics Manufacturing Operating costs Principal components analysis Production costs Raw materials Revenue Stock exchanges Variables |
title | Predicting the Readiness of Indonesia Manufacturing Companies toward Industry 4.0: A Machine Learning Approach |
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