Analisis faktor-faktor yang memengaruhi nilai ekspor impor Indonesia menggunakan metode Partial Least Square dan metode Principal Component Analysis

Mariana, Enny (2023) Analisis faktor-faktor yang memengaruhi nilai ekspor impor Indonesia menggunakan metode Partial Least Square dan metode Principal Component Analysis. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Kegiatan ekspor dan impor merupakan kegiatan yang memiliki peranan sangat penting dalam perekonomian di suatu negara. Seiring berjalannya waktu kegiatan ekspor dan impor di suatu negara akan semakin besar untuk memenuhi kebutuhan negara tersebut dikarenakan adanya faktor-faktor ekonomi seperti kurs mata uang asing, harga crude oil, Gross Domestic Bruto (GDP) dan Indeks Harga Perdagangan Besar Indonesia (IPHB). Multikolinearitas akan terjadi ketika adanya korelasi pada hubungan dua atau lebih variabel bebas. Masalah ini dapat menyebabkan regresi tidak dapat dijelaskan dengan baik. Oleh karena itu akan dilakukan penelitian untuk menghilangkan multikolinearitas tanpa menghilangkan variabel-variabelnya dengan menggunakan metode Partial Least Square dan Principal Component Analysis. Penelitian ini akan menggunakan data dari Januari 2012 hingga Agustus 2022. Metode Partial Least Square dan Principal Component Analysis pada penelitian ini berhasil menghilangkan multikolinearitas. Kemudian akan dibandingkan hasil Root Mean Square Error (RMSE) pada model PLS dan PCA untuk melihat metode yang paling baik. / Export and import activities have a vital role in the country's economy. Export and import activities in a country will gradually increase in order to fulfill the needs due to economic factors such as foreign currency exchange rates, crude oil prices, Gross Domestic Product (GDP) and Indonesia's Wholesale Trade Price Index (IWTPI). When there is a correlation in the relationship of two or more independent variables, it will cause multicollinearity which leads to an unexplained regression. Therefore, research will be performed to eliminate multicollinearity without eliminating the variables using the Partial Least Square and Principal Component Analysis methods. This study will use data collected from January 2012 to August 2022. The Partial Least Square and Principal Component Analysis methods in this study are eliminating multicollinearity successfully. Then the results of Root Mean Square Error (RMSE) on the PLS and PCA models will be compared to determine the best method.

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