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All Journal Jurnal Diferensial Eduvest - Journal of Universal Studies
Adrian, Ferry
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Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Mathematics Neuroscience Public Health Social Sciences

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Analysis of Bank Efficiency in Indonesia Based on Financial Ratios Adrian, Ferry
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.51270

Abstract

This research aims to analyze the efficiency of banks in Indonesia based on financial ratios in the Group of Banks Based on Core Capital (KBMI) 3 and 4 for the 2021-2024 period. The Network Data Envelopment Analysis (Network DEA) approach is used to measure operational efficiency and profitability comprehensively, with an asset approach to understand the bank's resource optimization. The results of the analysis show that PT Bank Mandiri (Persero) Tbk has the highest operational efficiency, while PT Bank Permata Tbk has the lowest. In terms of profitability efficiency, PT Bank Central Asia Tbk recorded the highest value, while PT Bank Maybank Indonesia Tbk recorded the lowest. Overall, PT Bank Central Asia Tbk was the most efficient, while PT Bank Maybank Indonesia Tbk was the lowest.  The panel regression model shows that the ratio of Operating Costs to Operating Income (BOPO), Capital Adequacy Ratio (CAR), and Loan to Deposit Ratio (LDR) has a significant effect on Return on Assets (ROA). This research is expected to be a reference for banks in improving efficiency and competitiveness.
GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION MODELING AT THE OPEN UNEMPLOYMENT RATE IN WEST KALIMANTAN Adrian, Ferry; Yundari, Yundari; Aprizkiyandari, Siti
JURNAL DIFERENSIAL Vol 5 No 2 (2023): November 2023
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v5i2.12063

Abstract

Geographically Weighted Ridge Regression (GWRR) is a development of ridge regression by adding a weighting element as additional information. GWRR is used to overcome spatial data containing local multicollinearity by modifying the ridge regression and GWR. The purpose of this study was to construct the GWRR model and analyze the effectiveness of the GWRR model in overcoming multicollinearity problems in spatial data. The research data used consists of response variables, namely the value of the open unemployment rate $(Y)$ in districts/cities of West Kalimantan Province in 2021 and the explanatory variables used are district/city minimum wage $(X_1)$, population $(X_2)$, percentage of poor population $(X_3)$, labor force $(X_4)$, and average years of schooling $(X_5)$. The analysis process was first carried out by using multiple linear regression modeling and GWR modeling. After that, centering and scaling transformations were carried out on the data, followed by GWRR modeling to overcome the problem of multicollinearity in spatial data. After all the models were obtained, a model comparison was made in terms of the MSE value. Based on the results of the study, 14 GWRR models were obtained by dividing into three regional groups based on the factors that affect the open unemployment rate for each district/city in West Kalimantan Province. In the comparison of the models used, when viewed from the MSE value, the GWRR model has the highest MSE value compared to the linear regression model and the GWR model. Even though the MSE value in the GWRR model is the largest among the three models, in the GWRR model the problem of multicollinearity can be resolved.
Co-Authors Aprizkiyandari, Siti Yundari, Yundari