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All Journal East Asian Journal of Multidisciplinary Research (EAJMR) Klabat Accounting Review
Hartini Pop Koapaha
Faculty of Economics and Business, Universitas Klabat
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

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The Impact of Macroeconomics Factors on the Jakarta Composite Index Hartini Pop Koapaha
East Asian Journal of Multidisciplinary Research Vol. 1 No. 10 (2022): November 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/eajmr.v1i10.1898

Abstract

This study aims to assess the relationship between macroeconomics factors on the Jakarta Composite Index. The multiple linear regression is employed to examine the impact of four macroeconomics variables namely GDP growth, inflation, interest rate spread, and broad money (money supply) on the Jakarta Composite Index. The result presents that Jakarta Composite Index is negatively affected by inflation and interest rates, while the increase in money supply boost the Jakarta Composite Index. In this study GDP growth has no significant influence on the Jakarta Composite Index. Investors may take advantage of this circumstance, notably they can purchase blue chips when inflation and interest rates rise and sell their shares when the money supply is expanding.
BAGGING BASED ENSEMBLE ANALYSIS IN HANDLING UNBALANCED DATA ON CLASSIFICATION MODELING Hartini Pop Koapaha; Niel Ananto
Klabat Accounting Review Vol 2 No 2 (2021): Klabat Accounting Review
Publisher : UNKLAB Business School

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.969 KB) | DOI: 10.60090/kar.v2i2.589.165-178

Abstract

The purpose of this study is to Identify the algorithm of each method of handling the unbalanced class based on bagging based on the literature review. This study uses a bagging based ensemble method such as UnderBagging, OverBagging, UnderOverBagging, SMOTEBagging, Roughly Balanced Bagging and the last one is the Bagging Ensemble Variation. The data used is coded from the UCI Repository with 16 data, eight of which have class categories with low imbalance problems, and the rest are categorized as high imbalance problems. The number of classes used in this study amounted to two classes. The class with a small number is made into the minority class and the rest is made up as the majority class. The result of this research is the bagging based method gives better results when compared to classical methods such as the classification tree.
Co-Authors Niel Ananto