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This study examines the effects of Net Profit Margin, audit opinion, and firm size on audit delay among construction companies listed on the Indonesia Stock Exchange from 2019 to 2025. Audit delay is measured as the period between the fiscal year-end and the issuance date of the independent auditor’s report. Timely financial reporting is particularly important in the construction sector due to its complex long-term projects, progress-based revenue recognition, cost estimation, and high financial risks. Using a quantitative approach, the study analyzes secondary data from annual financial statements and independent auditor reports. The sample includes 14 construction companies observed over seven years, producing 98 observations. Panel data regression was conducted using EViews, with the Chow, Hausman, and Lagrange Multiplier tests identifying the Random Effect Model as the most appropriate estimation method. The findings show that Net Profit Margin does not significantly affect audit delay. In contrast, audit opinion and firm size have negative and significant effects, indicating that favorable audit opinions and larger company size are associated with shorter audit completion periods. Collectively, the three variables significantly influence audit delay, although they explain only 15.75% of its variation.

Sukabanjar Village is one of the villages striving to improve basic infrastructure quality, particularly in drainage construction and road repair. However, limited understanding among village officials and communities regarding standardized construction cost estimation methods, specifically the Unit Price Work Analysis (AHSP), has become an obstacle in preparing accurate and efficient Budget Plans (RAB). This community service program (PKM) aims to improve the understanding of village officials, village hall staff, and Sukabanjar Village community regarding AHSP-based construction cost estimation planning as the standard reference for construction work cost calculation in Indonesia. The program involved socialization and training for 20 participants, consisting of village officials (RW/RT), village hall staff, and general public, introducing the basic concepts of AHSP, RAB component elements, and simple simulations of drainage and road work cost calculations. Evaluation was conducted through pre- and post-tests. The results of the activity showed a very significant improvement in participants' understanding. The average pre-test score was 42, which then increased to 75 in the post-test, reflecting an increase of 33 points (78.6%). In conclusion, the AHSP-based cost estimation planning socialization was effective in improving the village community's understanding of construction infrastructure budget management, although continued mentoring is still needed for optimal and sustainable implementation.

This study aims to design a liquid dish soap made from lerak fruit (Sapindus rarak DC.) as an environmentally friendly alternative to synthetic chemical-based soaps. Lerak fruit was selected due to its high saponin content, which functions as a natural surfactant capable of producing foam and removing grease. This research integrates three courses: Product Design and Development using the Quality Function Deployment (QFD) method with House of Quality (HOQ) to identify consumer needs; Work Measurement Analysis using Operation Process Charts (OPC) to design the production process; and Cost Estimation Analysis using the full costing method to calculate production costs and determine the selling price. Results show that the best product concepts are Concept 4 and Concept 8, characterized by natural lerak surfactant, active ingredient concentration above 15%, pH of approximately 6, citrus/lemon scent, and pump bottle packaging. The production process consists of preparation, mixing, boiling, addition of thickener, cooling, pressing, filtering, and packaging, with a total production time of 142.05 minutes for two 500 ml products. Total production cost is Rp65,184, with a cost of goods sold (COGS) of Rp32,592 per unit and a selling price of Rp34,000 per unit.

Evaluating food portions and types in the Free Nutritious Meal (MBG) program is generally still performed manually, which is time-consuming and potentially subjective. This study aims to develop an automated deep learning-based system to efficiently detect food types and estimate their nutritional value. The method used is a quantitative experiment integrating the YOLOv11m architecture for real-time object detection and the Google Gemini 2.5 Flash Large Language Model (LLM) for contextual nutritional estimation reasoning. The model training utilized a dataset of 2,630 food tray images categorized into five classes (fruit, side dish, staple food, vegetable, milk) that had undergone an augmentation process. The results showed that the YOLOv11m model achieved excellent performance with a mean Average Precision (mAP@0.5) of 0.9727 and the highest F1-score of 0.9522 at a confidence threshold of 0.1. Furthermore, validation of the LLM integration demonstrated a high prediction agreement rate of 85%. In conclusion, the combination of the YOLOv11m algorithm and LLM reasoning is capable of detecting and validating nutritional classification quickly and precisely, showing strong potential as an objective nutritional evaluation monitoring solution for large-scale MBG program implementation.

The governance of sand and gravel mining requires an integrated assessment of technical planning, occupational safety, environmental control, reclamation, and regulatory compliance. This study evaluated the governance of CV. Ria Bersaudara, a sand and gravel mining company located in Pasar Surulangun, Rawas Ulu District, Musi Rawas Utara Regency, using the Good Mining Practice framework. The study used a descriptive evaluative approach based on field observation, document review, indicative resource estimation, equipment productivity analysis, occupational safety and environmental risk assessment, and compliance mapping. The results show that the company has a basic legal foundation through an exploration mining permit covering 12.5 ha; however, several governance components require improvement. The indicative prospective area was approximately 2.50 ha, with an effective follow-up area of 1.80 ha and an estimated indicative resource of 21,600 m³ or 35,640 tons. Productivity analysis indicated that excavator capacity reached about 61 m³/hour, while one dump truck only transported about 10.3 m³/hour, creating a haulage bottleneck if the truck fleet is insufficient. Safety implementation was also not optimal, with personal protective equipment compliance estimated at only 55%. The study recommends validating permit documents, strengthening technical exploration data, improving drainage and sediment control, enforcing safety procedures, implementing progressive reclamation, and establishing daily operational records.

The quality of life and well-being of women in Indonesia can be measured through the Female Life Expectancy indicator. Data on Female Life Expectancy from 2022 to 2024 shows a nationally positive trend; however, a significant disparity persists across provinces, particularly between the Western and Eastern regions of Indonesia. The nation still faces challenges in ensuring an equitable quality of life for women, as evidenced by the national Female Life Expectancy (FLE) in 2024 at 74.21 years, which remains lower than ASEAN counterparts such as Singapore at 83.86 years. Furthermore, regional imbalances are reflected in the performance gap between D.I. Yogyakarta (77.4 years) and West Sulawesi (68.28 years). This study aims to analyze the effects of Women's Income Contribution, Access to Clean Water, the Number of Families Receiving Social Assistance, and Women's Mean Years of Schooling on Female Life Expectancy in Indonesia during the 2022–2024 period. The estimation method applied in this research is the Fixed Effect Model (FEM) via a quantitative panel data regression approach, spanning an observation area of 33 provinces (n=99). Based on the analysis, Female Life Expectancy is proven to be positively and significantly influenced by Women's Mean Years of Schooling and Access to Clean Water. These findings indicate that human resource quality and environmental conditions serve as dominant factors in driving up Female Life Expectancy. Therefore, government policy interventions should ideally focus on expanding educational access for women and ensuring the equitable distribution of clean water infrastructure.

This study aims to analyze the effect of the Open Unemployment Rate (OUR), Human Development Index (HDI), and zakat on poverty in Mandailing Natal Regency. The study employed a quantitative approach using the Vector Error Correction Model (VECM) based on quarterly secondary data from 2015 to 2025. The analytical procedures included stationarity testing, optimal lag selection, Johansen cointegration testing, VECM estimation, Granger causality testing, as well as Impulse Response Function (IRF) and Variance Decomposition (VD) analyses. The findings indicate that all variables were stationary at the second difference level, with an optimal lag length of four, and exhibited a long-run cointegration relationship. The VECM estimation reveals that, in the long run, the Open Unemployment Rate has a positive and significant effect on poverty, while zakat has a negative and significant effect in reducing poverty. In contrast, the Human Development Index does not significantly affect poverty, which may be explained by the presence of a development time lag and employment mismatch between workforce competencies and labor market demands. The Granger causality test indicates a one-way causal relationship from the Open Unemployment Rate to the Human Development Index. Furthermore, the Variance Decomposition analysis shows that poverty variation is predominantly explained by its own shocks (96.25%), reflecting the persistence of structural poverty. Therefore, policy synergy through employment expansion and the optimization of productive zakat programs based on business empowerment is recommended to accelerate poverty alleviation in Mandailing Natal Regency.

Tax avoidance practices remain a crucial issue due to their potential to erode state revenue and hinder national development financing. This study aims to analyze and evaluate the effect of fixed capital intensity, Environmental, Social, and Governance (ESG) performance, and political connections on tax avoidance practices. The research method used is quantitative with a panel data analysis approach under the selected Random Effect Model (REM) estimation. The research population covers all companies listed on the Indonesia Stock Exchange (IDX) for the 2023–2024 period. Through the purposive sampling method, a final sample of 259 companies was obtained, resulting in 518 observation data over two years. The partial empirical results demonstrate that fixed capital intensity has a significant negative effect on tax avoidance. Conversely, both ESG performance and political connections are proven to have a significant positive effect on tax avoidance. Simultaneously, the three independent variables significantly influence corporate tax avoidance actions, contributing an Adjusted R-squared value of 10.43%. The practical implication of this study emphasizes the urgent need for tax authorities to increase oversight on companies indicated to be utilizing ESG reporting as a greenwashing strategy or leveraging political protection to avoid taxes. For corporate management, these findings serve as an evaluation to align sustainability commitments with ethical fiscal compliance.

The rapid advancement of digital financial technology has increased the adoption of e-wallets among university students and may influence tax awareness through greater transparency in digital transactions. This study examines the effects of financial literacy and risk perception on tax awareness through e-wallet usage among higher education students in Batam City, with culture included as a control variable. A quantitative survey was conducted involving 247 students who regularly use e-wallet services. Data were analyzed using multiple linear regression with robust standard error estimation in Google Colaboratory. The results indicate that financial literacy has a positive but insignificant effect on e-wallet usage (β = 0.0411, p > 0.05), whereas risk perception has a positive and significant effect (β = 0.5572, p < 0.01). E-wallet usage also positively and significantly affects tax awareness (β = 0.4613, p < 0.01). Furthermore, e-wallet usage significantly mediates the relationship between risk perception and tax awareness but does not mediate the relationship between financial literacy and tax awareness. These findings suggest that e-wallet adoption is driven more by digital lifestyle demands than financial literacy and that improving digital risk literacy may help strengthen students’ tax awareness and responsible use of financial technology.

This study aims to analyze the effect of the Bank Indonesia interest rate on the Indonesian rupiah exchange rate against the United States dollar during the 2021–2026 period. A quantitative approach was employed using monthly time-series data obtained from official sources. The analysis was conducted using multiple linear regression with the assistance of EViews software. The dependent variable in this study was the rupiah exchange rate against the US dollar, while the independent variables included the Bank Indonesia interest rate, inflation, money supply (M2), and the Federal Reserve interest rate (Fed Rate). Prior to regression estimation, all data were tested for stationarity using the Augmented Dickey-Fuller (ADF) test to ensure compliance with time-series analysis requirements. Furthermore, the model was evaluated through classical assumption tests, including normality, heteroscedasticity, multicollinearity, and autocorrelation tests, to verify the validity and reliability of the estimation results. The findings reveal that the Bank Indonesia interest rate has a significant effect on the rupiah exchange rate, with a probability value of 0.0223. This result indicates that changes in the domestic interest rate can influence movements in the rupiah exchange rate against the US dollar. In contrast, inflation, money supply (M2), and the Fed Rate were found to have no significant effect on the rupiah exchange rate during the study period. Moreover, the classical assumption tests confirmed that the regression model satisfied all required criteria, indicating that the estimated results are reliable and can serve as a reference for economic policy formulation.

The Family Hope Program (Program Keluarga Harapan/PKH) is Indonesia's primary conditional cash transfer (CCT) scheme for poverty alleviation. This study examines PKH effectiveness in improving household welfare among beneficiaries in Oebobo District, Kupang City, East Nusa Tenggara (NTT), employing a Three-Stage Least Squares (3SLS) simultaneous equation approach. The analysis integrates a Gender Equality, Disability, and Social Inclusion (GEDSI) perspective to evaluate Access, Participation, Control, and Benefit (APCB) dimensions for vulnerable groups. A mixed-methods explanatory sequential design was applied to 151 beneficiary households selected via proportional sampling across three urban sub-districts. The 3SLS estimations establish that targeting accuracy (X1) and economic empowerment (X3) are the most significant determinants of PKH effectiveness; PKH effectiveness (Y1) strongly shapes household governance (Y2) through the Family Capacity Building Meetings (P2K2) mechanism; and PKH effectiveness is the dominant determinant of household welfare (Y3), with a near-unity coefficient (β=0.97). GEDSI analysis reveals that female-headed households, the elderly, and persons with disabilities face compounding vulnerabilities inadequately addressed by current program design. The study recommends strengthening data verification systems, embedding economic empowerment as a core pillar, and adopting differentiated targeting based on intersectional vulnerability profiles.

Economic growth in ASEAN countries has shown heterogeneous dynamics, particularly in the post-pandemic period. This study aims to analyze the effect of economic complexity, manufacturing value added, and foreign direct investment on economic growth in ASEAN-8 countries during 2015–2024. The study employs a quantitative explanatory approach using panel data regression analysis. The data were obtained from the World Development Indicators (World Bank) and Harvard Growth Lab. Based on the Chow and Hausman tests, the Fixed Effect Model (FEM) was selected as the best estimation model. The results indicate that economic complexity has a negative and significant effect on economic growth, suggesting that increasing economic sophistication does not automatically promote growth when industrial and institutional readiness remain limited. Meanwhile, the manufacturing sector has a positive but insignificant effect on economic growth. In contrast, foreign direct investment has a positive and significant effect on economic growth through capital accumulation and technology transfer. Simultaneously, all independent variables significantly affect economic growth in ASEAN-8 countries. These findings imply the importance of strengthening industrial capacity, institutional quality, and technological readiness to support sustainable economic growth in ASEAN countries.

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.

This study aims to analyze the effect of labor proportion and industrial agglomeration externalities on the Gross Regional Domestic Product (GRDP) of the manufacturing sector in Indonesia. This research employs a quantitative approach using panel data covering 34 provinces over the period 2019–2023. The independent variables include labor proportion and industrial externalities represented by specialization index (MARshall–Arrow–Romer/MAR), competition (Porter), and diversity (Jacobs). The analysis method uses panel data regression with model selection through Chow, Hausman, and Lagrange Multiplier tests, where the Random Effect Model (REM) is selected as the best estimation model. The results show that labor proportion and MAR externalities have a positive and significant effect on manufacturing GRDP, highlighting the important role of labor and industrial specialization in driving regional output growth. In contrast, Porter and Jacobs externalities do not have a significant effect, indicating that industrial competition and diversity have not yet contributed substantially to manufacturing sector performance during the study period. Simultaneously, all independent variables significantly affect GRDP, although the model’s explanatory power remains relatively limited. These findings suggest that the growth of Indonesia’s manufacturing sector is more influenced by labor and industrial concentration rather than competition and diversification dynamics, and that other factors outside the model also play an important role in determining manufacturing sector performance.

Stock market volatility represents a key indicator of financial market uncertainty, particularly in emerging economies where market structures are still evolving and are highly sensitive to global shocks. This study aims to analyze and compare the volatility dynamics of stock markets in four Asian emerging economies: Indonesia, India, Malaysia, and Thailand. The research employs a quantitative approach using daily stock index data from January 2011 to January 2026 obtained from Yahoo Finance. Stock returns are calculated using logarithmic transformation and analyzed using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH(1,1)) model. Prior to model estimation, stationarity and ARCH effect tests are conducted to ensure the validity of volatility modeling. The empirical findings indicate that all return series exhibit non-normal distribution, strong volatility clustering, and significant ARCH effects. The estimation results show that both ARCH and GARCH parameters are statistically significant, with persistence levels close to unity across all markets, implying that volatility shocks tend to persist over a long period. These findings suggest that emerging stock markets in Asia are highly sensitive to external shocks and exhibit long-memory volatility behavior. The results provide important implications for investors and policymakers in designing effective risk management and market stabilization strategies.

 This study was conducted to evaluate the influence of Total Asset Turnover (TATO), Debt to Equity Ratio (DER), and Net Profit Margin (NPM) in predicting stock return fluctuations. The study focuses on retail sector issuers listed on the Indonesian Stock Exchange (IDX) between 2021 and 2024. Through the application of panel data regression analysis, the study determined that the Common Effects Model (CEM) is the most appropriate estimation method. This decision was made based on a series of tests including the Chow Test and the Lagrange Multiplier. Although classical assumption testing showed symptoms of heteroscedasticity, this problem was addressed using the EGLS (cross-sector weighting) Panel method to ensure the validity of the estimates. Based on partial testing, it is found that TATO and NPM variables have a positive and significant contribution to stock returns, while DER is found to have no significant effect. Collectively, all independent variables had a significant effect, with the Adjusted R-Square value reaching 27.80%. This indicates that for investors in the retail sector, profitability and operational efficiency are important indicators in making investment decisions.

Proprioceptive sensor data, including inertial measurement units (IMU), joint encoders, and torque sensors, plays a critical role in state estimation for quadruped robots operating in dynamic and unstructured environments. However, these signals are often degraded by various sources of error, such as high-frequency noise, bias, drift, and contact-induced disturbances, which directly affect estimation accuracy and stability. This study presents a systematic analysis of sensor-specific noise characteristics and evaluates the effectiveness of preprocessing methods tailored to each sensor modality. Specifically, moving average filtering is applied to encoder signals to mitigate noise amplification during differentiation, while first-order low-pass filtering is employed for IMU and torque signals to suppress high-frequency noise. Experimental results on a publicly available quadruped dataset demonstrate that encoder velocity RMSE is reduced by 12.09%, high-frequency energy decreases by 59.63%, and signal-to-noise ratio (SNR) improves by 145.6%. However, variance reductions remain limited (3.39% for IMU and 4.05% for torque), indicating the persistence of impulsive, non-Gaussian noise caused by contact events. These findings highlight that linear preprocessing methods are effective for attenuating high-frequency noise but insufficient for handling non-Gaussian disturbances. The study provides practical insights into the effectiveness and limitations of preprocessing strategies, serving as a foundation for developing more robust signal processing and state estimation frameworks in quadruped robotics.

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.

The Fréchet distribution is one of the commonly used Extreme Value Distributions (EVDs) in statistical modeling and heavy-tailed data analysis, where it plays an important role in describing product lifetimes as well as climatic and financial phenomena. The estimation of its two parameters, namely the shape parameter and the scale parameter, is traditionally based on the Maximum Likelihood Estimation (MLE) method. However, maximizing the likelihood function for this distribution involves numerical difficulties, which necessitates the use of numerical optimization methods. In this study, we propose the use of the Aquila Optimizer (AO), a recent metaheuristic algorithm inspired by the hunting behavior of eagles, as an efficient numerical tool for maximizing the likelihood function of the Fréchet distribution. The objective function was formulated as the negative log-likelihood function (-LogL), and the Aquila Optimizer was employed to obtain the optimal estimates of the distribution parameters. Several simulation experiments with different sample sizes were conducted to compare the performance of the proposed method with a conventional approach represented by the Nelder–Mead method, using the Mean Squared Error (MSE) criterion. The simulation results demonstrated that the Aquila Optimizer outperformed the Nelder–Mead algorithm in many cases, although the superiority was slight. The results also showed that both algorithms were consistent, as their MSE values decreased with increasing sample size. In addition, a practical application was carried out using real data, and the results of the survival function estimation indicated a good fit.

The study investigates the impact of governance on firm value, along with the moderating role of institutional ownership, among companies operating in the consumer cyclicals and consumer non-cyclicals sectors listed on the Indonesia Stock Exchange (IDX) over the 2021-2023 period. Drawing on a sample of 42 companies and 126 observations, a quantitative panel data approach was adopted, with data gathered through purposive sampling. The Bloomberg Governance Score was used to measure governance, Tobin's Q served as a proxy for firm value, institutional ownership was the moderating variable, and firm size, leverage, and profitability were included as control variables. Model selection was conducted using the Chow and Hausman Tests in EViews 14, which indicated that the Random Effects Model (REM) was the most suitable estimation approach. The findings reveal that governance does not exert a significant influence on firm value. Furthermore, institutional ownership is unable to strengthen the relationship between governance and firm value. These results suggest that ESG-based governance signals have not yet been optimally absorbed by investors in the Indonesian capital market; hence, reinforcing governance reporting standards and enhancing investor ESG literacy are considered essential steps toward integrating non-financial information into investment decision-making.