scholarly journals Using Principal Component Analysis for Factor Analysis in Indonesian Automotive Industries

2020 ◽  
Vol 8 (2) ◽  
pp. 62
Author(s):  
Ulil Hamida
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Automotive Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Influential Factors ◽  
Analysis Method ◽  
Vehicle Ownership ◽  
Principal Component Analysis Algorithm

Policies related to the automotive industry have become significant for the Ministry of Industry. The problem in determining these policies is the determination of important factors for the automotive industry so that the policies formulated are right on target. The search for these important factors can be done by using the factor analysis method. So far, no studies have been conducted to examine the factors that influence the growth of the automotive industry. In this study, factor analysis is performed on factors in the automotive industry using the principal component analysis algorithm. The algorithm seeks to describe independently the aspects that become the main factors in determining the automotive industry. Based on an analysis of factors in the automotive industry production, the most influential factors are foreign investment, vehicle ownership ratios, and at last the change in GDP.

scholarly journals Driving Factors of Consumer to Purchase Furniture Online on IKEA Indonesia Website

2018 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Trisha Gilang Saraswati
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Perceived Risk ◽  
Online Shopping ◽  
Principal Component ◽  
Influential Factors ◽  
Driving Factors ◽  
Analysis Method ◽  
Long Time

The tendency of consumers in shopping for furniture is very different from shopping for other goods or services, because furniture is expected to be stored and used in a long time. Considering that, consumers tend to want to shop directly to offline stores in order to see, feel and check quality directly because of many aspects that are assessed such as the quality of materials, models, colors and more. Behind the trend to shop furniture offline, IKEA continues to innovate to improve the performance of its website so that consumers can shop online. Therefore, this study intends to analyze what factors are driving consumers in shopping for furniture online especially on the website of IKEA Indonesia. From two grounded theory employed on this research, there are 14 factors that can influence consumers to buy online. This data analysis uses Principal Component Analysis (PCA), a factor analysis method that extracts factors by using total variance in the analysis. From data calculation, it is known that there are 8 driving factors of consumer to purchase furniture online on IKEA Indonesia’s website: Enjoyment, Perceived Risk, Efficiency, Service & Merchandise Quality, Ease of Navigation, Price Attractiveness, Flexibility and Reliability. By knowing what factors can affect consumers in doing online shopping for furniture, companies in this case IKEA Indonesia can optimize the use of its website in accordance with influential factors.

Determination of Agarwood oil’s significant chemical compounds using principal component analysis

2020 ◽  
Author(s):  
Mohamad Hushnie Haron ◽  
Mohd Nasir Taib ◽  
Nurlaila Ismail ◽  
Nor Azah Mohd Ali ◽  
Saiful Nizam Tajuddin
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Chemical Compounds ◽  
Component Analysis ◽  
Significant Chemical

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

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