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PANEL DATA REGRESSION ANALYSIS OF PORT SERVICES SERVICES TOWARDS RATE ACCEPTANCE NOT TAXES (PNBP) (Case Study: At the Class IV Harbor Authority and Kuala Tungkal Port Authority)

Mathematical Sciences and Applications Journal · Vol. 1 No. 1 (2020) · hlm. 31–41

ISSN
2988-6481
Terbit
2020-10-27
Penerbit
Department of Mathematics, Faculty of Science and Technology Universitas Jambi
Bahasa
en

Abstrak

on-Tax State Revenue (PNBP) is the receipt of the central government that is not derived from taxation. One source of PNBP is direct services by the state, such as the use of services at the port. This type of service is used in the related sector activities of the shipping company. The more port activities are carried out, the service at the port is increasing, so that PNBP received will increase. PNBP data in this study is a combination of time series data and cross section data called panel data. This study aims to determine the type of service that significantly influences PNBP using the Panel Data Regression Analysis method. From the analysis it is concluded that the best regression model estimation is the Fixed Effect Model (FEM) with dummy variables. FEM model states that the regression coefficient for navigation variables is 0.090824, PUJK variable is 0.267160 and the number of ships (JK) variable is 0.472592. These three variables are positive which means they have a significant effect on the level of PNBP. The PUP variable has a negative value of -0.061411, which means that the PUP variable does not significantly influence the level of PNBP. The variability of PNBP level in FEM model can be explained by the Navigation Services, Shipping Services (PUP), Port of Services (PUJK) and Number of Vessels (JK) variables of 88.75%. Keywords: Fixed Effect, Panel Data Regression, Port Services

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Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/msa/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">PANEL DATA REGRESSION ANALYSIS OF PORT SERVICES SERVICES TOWARDS RATE ACCEPTANCE NOT TAXES (PNBP)   (Case Study: At the Class IV Harbor Authority and Kuala Tungkal Port Authority) </dc:title>
	<dc:creator xml:lang="en">mawarni, iga</dc:creator>
	<dc:creator xml:lang="en">Kamid</dc:creator>
	<dc:creator xml:lang="en">kholijah, gusmi</dc:creator>
	<dc:description xml:lang="en">on-Tax State Revenue (PNBP) is the receipt of the central government that is not derived from taxation. One source of PNBP is direct services by the state, such as the use of services at the port. This type of service is used in the related sector activities of the shipping company. The more port activities are carried out, the service at the port is increasing, so that PNBP received will increase. PNBP data in this study is a combination of time series data and cross section data called panel data. This study aims to determine the type of service that significantly influences PNBP using the Panel Data Regression Analysis method. From the analysis it is concluded that the best regression model estimation is the Fixed Effect Model (FEM) with dummy variables. FEM model states that the regression coefficient for navigation variables is 0.090824, PUJK variable is 0.267160 and the number of ships (JK) variable is 0.472592. These three variables are positive which means they have a significant effect on the level of PNBP. The PUP variable has a negative value of -0.061411, which means that the PUP variable does not significantly influence the level of PNBP. The variability of PNBP level in FEM model can be explained by the Navigation Services, Shipping Services (PUP), Port of Services (PUJK) and Number of Vessels (JK) variables of 88.75%.
&amp;nbsp;
Keywords: Fixed Effect, Panel Data Regression, Port Services</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Faculty of Science and Technology Universitas Jambi</dc:publisher>
	<dc:date>2020-10-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/msa/article/view/10133</dc:identifier>
	<dc:source xml:lang="en">Mathematical Sciences and Applications Journal; Vol. 1 No. 1 (2020): Mathematical Sciences and Applications Journal; 31-41</dc:source>
	<dc:source>2988-6481</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/msa/article/view/10133/6535</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Mathematics Science and Application Journal</dc:rights>
</oai_dc:dc>