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 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.

This study aims to analyze the effect of renewable energy, energy consumption, and Gross Domestic Product (GDP) per capita on carbon dioxide (CO2) emissions in Indonesia for the period 1995-2024. This study uses secondary data over time (time series) with the Ordinary Least Square (OLS) multiple linear regression analysis method corrected using the Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) approach. The results show that renewable energy does not have a significant effect on CO2 emissions, which is caused by the still low share of renewable energy in the national energy mix which only reaches 10.95% in 2024. Energy consumption has a positive and significant effect on CO2 emissions, where every 1% increase in energy consumption increases CO2 emissions by 84.23%. Gross Domestic Product (GDP) per capita has a positive and significant effect on CO2 emissions. Every 1% increase in GDP per capita increases CO2 emissions by 35.03%, indicating that Indonesia remains on the EKC curve. Simultaneously, all three variables have a significant effect, with an adjusted R-squared value of 53.63%. This finding confirms that Indonesia's energy mix, still dominated by fossil fuels, is a major factor in high carbon emissions. Comprehensive energy efficiency policies, accelerated renewable energy transitions, and greener and more sustainable economic growth strategies are needed.

This study examines the determinants of the Jakarta Composite Index (JCI) based on three main macroeconomic factors namely inflation, the USD/IDR exchange rate, and the SBI interest rate (BI Rate) covering the period January 2020 to December 2025, in the context of post-COVID-19 pandemic recovery and global economic turmoil. A quantitative approach was employed using the Ordinary Least Squares (OLS) method, with 72 monthly observations derived from secondary data sourced from official institutions including Bank Indonesia (BI), the Central Statistics Agency (BPS), the Indonesia Stock Exchange (IDX), and the Financial Services Authority (OJK). Classical assumption tests were applied comprising the Jarque-Bera normality test, Variance Inflation Factor (VIF) for multicollinearity, Breusch-Godfrey for autocorrelation, White Test for heteroscedasticity, and Ramsey RESET for model specification. Partially, inflation, exchange rate, and BI Rate each demonstrate a positive and significant effect on the JCI (p < 0.05). Simultaneously, all three variables exert a significant combined influence on the JCI, with a coefficient of determination R² = 0.4414, indicating that the model explains 44.14% of the variation in the JCI. The remaining 55.86% is attributed to other variables outside the model. Classical assumption test results reveal violations of normality, autocorrelation, and heteroscedasticity assumptions, although the model is free from multicollinearity. These findings confirm that Bank Indonesia's monetary policy has a significant and measurable impact on capital market performance. Further research is recommended using more advanced time series models such as GARCH or VECM to address violations of classical assumptions and improve estimation efficiency.

The growth of aviation activities at I Gusti Ngurah Rai International Airport, Bali, has led to the rapid development of surrounding areas, potentially obstructing protected airspace. Obstacles on the approach surface of Runway 27 have become a critical concern, particularly for precision approach Category II (CAT II) operations, which require obstacle-free approach areas. This study aims to analyze obstacles within the approach area of Runway 27 and develop effective control strategies. Using a descriptive qualitative approach, data was collected through field observations, interviews, and documentation studies. The analysis follows the Obstacle Limitation Surfaces (OLS) standards according to ICAO and national regulations. The findings reveal obstacles such as mangrove vegetation, antennas, and ship activities in the Benoa Harbor area, which are located within the approach surface and could potentially impact the OLS limits. While these obstacles generally comply with existing regulations, their proximity to the threshold may reduce the safety margin of flight operations and limit CAT II implementation on Runway 27. This study proposes technical, operational, regulatory, and preventive strategies to improve obstacle control, enhancing aviation safety and ensuring the readiness for CAT II operations at the airport.

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.

This study investigates the determinants of dividend payout policy in energy sector firms listed on the Indonesia Stock Exchange during the 2020–2024 period. Dividend policy is a critical issue in emerging markets, especially in capital-intensive industries with high investment needs and earnings volatility. The research examines whether profitability and ownership structure—specifically institutional and managerial ownership—significantly influence dividend payout decisions, considering firm characteristics. The study analyzes the effect of profitability, institutional ownership, and managerial ownership on the dividend payout ratio, while controlling for firm size and leverage. A quantitative approach is used, employing pooled ordinary least squares (OLS) regression on 245 firm-year observations. Dividend payout ratio is measured as dividend per share divided by earnings per share, profitability is proxied by return on equity, and ownership variables are expressed as shareholding proportions. Descriptive analysis and classical assumption tests precede hypothesis testing. The results show that profitability positively and significantly affects dividend payout, suggesting that firms with better financial performance tend to distribute higher dividends. Firm size also positively influences dividend policy, while leverage negatively impacts it, reflecting the role of financial capacity and capital structure. However, institutional and managerial ownership do not show significant effects on dividend payout decisions. The findings indicate that dividend policy in Indonesian energy firms is primarily driven by financial performance and structural characteristics rather than ownership-based governance mechanisms. This study offers sector-specific evidence that refines agency and signaling perspectives on dividend policy in emerging markets, with practical implications for managers, investors, and regulators.

Unemployment is a socio-economic problem that can threaten the stability of the Indonesian economy. This study analyzes the effect of minimum wages, exports, foreign investment, and the human development index (HDI) on the unemployment raefrom 1990 to 2023. Using the Ordinary Least Square (OLS) multiple linear regression estimation method, to correct bias in the estimation, the Newey-West HAC standard errors approach is used. Minimum wages and foreign investment have a significant negative effect on the open unemployment rate, confirming that wage increases can boost productivity, foreign investment creates direct jobs through the construction of production facilities and economic multiplier effects in supporting sectors. The most surprising finding of the HDI which has a positive effect and exports which are proven to be insignificant on the unemployment rate, this shows that human capital formation is not in line with existing job opportunities due to rapid technological changes, as well as export-increasing policies which focus more on capital intensity. The study provides important implications for policymakers, maintaining and optimizing minimum wage increases and foreign investment in a measurable manner because they have proven effective in reducing unemployment rates. Reorienting export strategies policy from capital-intensive to labor-intensive, increasing the human development index adjusted to technological developments, especially in the business and industrial world.

Economic growth is the process of increasing a country's production capacity to generate goods and services over a specific period, reflecting the income and well-being of its people. This research aims to analyse the influence of labor, exchange rates, and exports on the Gross Domestic Product (GDP) of the manufacturing sector in Indonesia during the period 2010-2024. The method used is multiple linear regression analysis with the Ordinary Least Square (OLS) approach, using secondary data obtained from the Central Bureau of Statistics (BPS) and Bank Indonesia (BI). The research results indicate that all three independent variables, namely labor, exchange rate, and exports, have a positive and significant impact on the GDP of the manufacturing sector, both partially and simultaneously. The coefficient of determination (Adjusted R2) value of 0.9633 indicates that 96.33% of the variation in industrial sector GDP can be explained by these three variables, while 3.76% is influenced by factors outside the model. This research confirms that increased labour productivity, exchange rate stability, and export growth play an important role in strengthening the performance of the manufacturing sector in Indonesia. Therefore, policies focused on improving the quality of human resources, strengthening export competitiveness, and ensuring macroeconomic stability are needed to support the sustainable and globally competitive growth of the manufacturing sector.

Studi ini berfokus pada perusahaan perbankan yang terdaftar di Bursa Efek Indonesia (BEI) antara tahun 2020 hingga 2023 dan mengkaji bagaimana harga saham dipengaruhi oleh Earnings Per Share  (EPS) sebagai pemoderasi dan rasio keuangan sebagai variabel independennya. Ada dua rasio keuangan yang diteliti yaitu Capital Adequacy Ratio (CAR) dan Return on Assets (ROA). Penelitian ini menggunakan pendekatan Ordinary Least Squares (OLS). Hasil penelitian menunjukkan bahwa ROA berpengaruh terhadap harga saham, sedangkan CAR tidak berpengaruh. Selain itu, telah terbukti bahwa EPS memoderasi hubungan antara return on assets (ROA) dan harga saham. Diharapkan hasil penelitian ini akan berguna sebagai referensi untuk studi mendatang dan mendorong penelitian terkait topik yang sama.

This study aims to analyze the influence of green financing, industrialization level, and fossil energy consumption on carbon emissions in Indonesia for the period 2018–2023. The method used is multiple linear regression with the Ordinary Least Squares (OLS) approach based on time series data. The dependent variable used is carbon emissions, while the independent variables include green financing, industrialization level, and fossil energy consumption. The results of the analysis show that both simultaneously and partially, the three independent variables do not have a significant effect on carbon emissions. The coefficients of green financing and industrialization tend to be positive, while fossil energy consumption is negative, but all are not statistically significant. These findings indicate that green financing policies, industrial development, and fossil energy consumption during the study period have not had a significant impact on carbon emissions in Indonesia. This study recommends the need to extend the observation period, add relevant variables such as the renewable energy mix, and optimize the implementation of green energy and financing policies to support carbon emission reduction in the future.

This study aims to analyze the effectiveness of zakat in reducing income inequality in Indonesia. The data used is secondary data from the National Amil Zakat Agency (BAZNAS) and the Central Statistics Agency (BPS) for the 2015–2024 period. The research method used multiple linear regression with the Ordinary Least Squares (OLS) approach. The results showed that the distribution of zakat had a significant positive effect on the Gini Index (β = 0.0013511; p = 0.002), while the Gross Domestic Product (GDP) per capita had a significant negative effect (β = –0.0000198; p = 0.000). An Adjusted R² value of 0.91 indicates that the model has a very strong ability to explain variations in income inequality. These findings show that although the collection and distribution of zakat has increased from year to year, its impact on reducing inequality has not been optimal. This can be caused by the limited amount of zakat funds collected compared to the real needs of the community and the distribution that is not fully on target. On the other hand, economic growth reflected in the increase in GDP per capita has proven to be still the dominant factor in reducing income inequality in Indonesia. The implications of this study emphasize the importance of optimizing zakat management so that it not only functions as a philanthropic instrument, but also as a sustainable economic instrument. BAZNAS and related institutions are expected to be able to strengthen the zakat distribution system through productive economic empowerment programs, so that zakat can make a real contribution in reducing inequality and supporting more inclusive development in Indonesia.

This study aims to analyze the influence of the money supply (M2), the BI Rate, and the COVID-19 pandemic on the demand for bank credit in Indonesia. Credit demand is an important indicator in describing economic activity and financial system stability. This study uses monthly secondary data from January 2017 to December 2023. The analysis method used is Ordinary Least Squares (OLS), which allows for quantitative estimation of the linear relationship between the independent and dependent variables. The results show that the money supply (M2) has a positive and significant effect on credit demand. This suggests that increased liquidity in the economy encourages increased lending activity by the household and corporate sectors. Conversely, the BI Rate shows a negative and significant effect on credit demand, indicating that an increase in the benchmark interest rate has reduced public interest in accessing financing through banks. This finding is in line with conventional monetary theory, which states that interest rates play a crucial role in controlling aggregate demand, including credit demand. The dummy variable for the COVID-19 pandemic shows a negative but insignificant effect on credit demand. This implies that although the pandemic has had a broad social and economic impact, its impact on credit demand is relatively small when monetary variables such as M2 and the BI Rate are taken into account. Overall, the research findings confirm that monetary policy instruments, particularly controlling the money supply and interest rates, play a significant role in influencing the dynamics of credit demand in Indonesia. Meanwhile, external shocks such as the pandemic tend to be more effectively responded to through medium- and long-term fiscal and structural policies.

The purpose of this study was to determine the effect of labor, domestic investment, and inflation on GRDP in Lampung Province in 1991 – 2020. Data analysis was carried out descriptively quantitatively using the (OLS) Multiple Linear Regression method. Secondary data were obtained from the Central Statistics Agency of Lampung Province. The results of this study indicate that labor has a positive and significant effect on GRDP in Lampung Province. Meanwhile, domestic investment has a positive but insignificant effect and inflation has a negative and insignificant effect on GRDP in Lampung Province.

Aceh’s economy has long been dominated by the oil and gas sector, particularly LNG Arun exports, which contributed more than 40% of regional GDP (GRDP) in the early observation period. However, declining production after 2014 drastically reduced its share to below 20%, and in recent years, to only around 10–12%. This study aims to analyze the contribution of oil and gas (migas) and non-oil and gas (nonmigas) exports to Aceh’s economy and to assess the potential of export diversification as a sustainable development strategy. The research employs a quantitative approach using secondary time series data from 2007–2021 obtained from BPS, Bank Indonesia, and other official sources. Data were analyzed descriptively and through simple econometric models, including OLS regression and cointegration tests. The findings reveal that oil and gas exports are significant in the short term but have weakened in the long term. In contrast, non-oil and gas exports, although relatively small, exhibit stable growth and demonstrate a long-term relationship with GRDP. These results underscore the importance of diversifying exports towards nonmigas commodities, particularly coffee, cocoa, rubber, and fisheries. The practical implication is the urgent need for Aceh’s development strategy to shift from oil and gas dependence towards globally competitive nonmigas industrialization

This research aims to analyze the factors Affecting the productivity of lowland rice  in Anduring Village. This research was using survey methods on sample of 30 peoples through simple random sampling. This research was conducted during the growing season period January-April 2016. Data analysis used the Cobb-Douglas production function with the OLS (Ordinary Least Square) methods. The research results show that the research shows that the factors that have a significant influence on lowland rice productivity in Anduring Village are urea fertilizer with a coefficient value of 30.077, while the variables of land area and pesticide use have a positive but not significant effect with coefficient values ​​of 98.12 and 25.26. Farmers should pay more attention to using fertilizer doses and pesticide doses properly in accordance with the recommendations set by the Ministry of Agriculture.    

Poverty is a crucial issue for a country. Overcoming poverty requires a comprehensive and sustainable approach that encompasses various sectors.  This study aims to determine the effect of the Open Unemployment Rate (TPT), Gross Regional Domestic Product (PDRB), and Labor Force Participation Rate (TPAK) on the number of poor residents.  The data analysis technique used in this research is OLS (Ordinary Least Squares), utilizing time series data on the Open Unemployment Rate (TPT), Regional Gross Domestic Product (PDRB), Labor Force Participation Rate (TPAK), and poverty from 2002-2023 in North Sumatra Province.  The results of this study indicate that the Open Unemployment Rate (IPT) does not affect poverty, whereas the Gross Regional Domestic Product (PDRB) and the Labor Force Participation Rate (TPAK) have a negative and significant impact on poverty.  Meanwhile, simultaneously, the Open Unemployment Rate (TPT), Regional Gross Domestic Product (PDRB), and Labor Force Participation Rate (TPAK) affect poverty in North Sumatra Province from 2002 to 2023.

This study examines the impact of the Central Bank of Nigeria's monetary policies on the Nigerian economy, specifically how these policies can be applied to foster economic growth. The research employs multiple regression models as the primary statistical method to analyze the relationship between key variables: money supply, average price levels, interest rates, labor force, and their effects on the Gross Domestic Product (GDP). Using data from 1981 to 2008, the study applies the Ordinary Least Squares (OLS) method to assess these effects comprehensively. The findings indicate that monetary policy, as reflected by money supply, positively influences GDP growth and improves the balance of payments, while also having a negative impact on inflation rates. The study suggests that the Nigerian money market should introduce a broader range of financial instruments to cater to the growing economy’s needs. Additionally, the recommendations emphasize the importance of designing monetary policies that create a favorable investment climate by adjusting interest rates, currency rates, and liquidity management mechanisms. By fostering a well-regulated and flexible monetary system, the Central Bank can further enhance the economic stability and growth of Nigeria, supporting sustainable development in the long term.

This study investigates the relationship between corporate social responsibility (CSR) and the profitability of businesses in Nigeria, using secondary data from the annual reports and financial statements of ten (10) randomly selected companies over the period from 2019 to 2024. The study aims to explore how CSR practices impact the financial performance of companies, specifically examining the Profit After Tax (PAT) as a measure of profitability. Ordinary Least Squares (OLS) regression analysis is employed to analyze the data and establish the connection between CSR activities and company performance.The findings of the study show that the companies in the sample allocated less than 10% of their annual profits to CSR initiatives. This suggests that while some companies engage in CSR, their contribution remains relatively small in proportion to their overall profitability. The coefficient of determination reveals that changes in CSR activities have a significant impact on the variations observed in the performance of these companies, particularly in terms of PAT. Furthermore, the study highlights the need for stronger regulatory frameworks to enforce CSR practices. It recommends that the Nigerian government introduce laws and regulations that require firms to allocate a portion of their profits to social responsibility, ensure transparency in social accounting, and address social costs effectively. The study emphasizes that by improving CSR engagement, businesses can contribute to national development while enhancing their long-term financial performance.

Economic growth is a crucial pillar of regional development and serves as an indicator of how well development is going as well as a tool for future planning. Development will not proceed easily if the infrastructure is poor, and a nation or region's economic activity will not be sufficient if the infrastructure is insufficient. Throughout the years 2018 to 2024, Probolinggo Regency's economic growth rate development had erratic variations. Therefore, the purpose of this study is to ascertain how Probolinggo Regency's road network, access to clean water, and availability of power affect economic growth. This study's methodology was quantitative and deductive in nature. The information used is secondary data, specifically time series data from the Central Bureau of Statistics. Using multiple linear regression analysis and the OLS (Ordinary Least Square) data analysis method, SPSS 24 was utilized to conduct the study's data analysis. Where the value is significant > 0.05, the study's findings show that infrastructure for roads, water, and power has no bearing on economic growth.

This study aims to determine and analyze the Level of Education, Economic Growth, and Population Density on Quality of Life in Aceh, using a descriptive-quantitative approach in the form of time series data, for 5 years in the form of secondary data selected from the total of Aceh Province. The model used in this study is a multiple linear regression analysis model (Multiple Linear Regression) based on the OLS (Ordinary Least Square) method. Simultaneously, the F test shows that the three independent variables together have a significant effect on quality of life, with a calculated F value = 85.495, sig. = 0.000, and F table = 2.70. The coefficient of determination shows a value of R Square = 0.728, which means that 72.8% of the variation in quality of life can be explained by education level, economic growth and population density, while the rest is influenced by other variables outside the model.