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This study aims to analyze the effect of capital structure, asset growth, and firm size on firm value in plastic and packaging sub-sector companies listed on the Indonesia Stock Exchange during the 2020–2024 period. Firm value is proxied by Price to Book Value (PBV), capital structure is measured using the Debt to Equity Ratio (DER), asset growth is measured by the asset growth ratio, and firm size is measured using the natural logarithm of total assets. This research employed an explanatory quantitative approach using secondary data obtained from financial statements, annual reports, and stock price data. The sample consisted of 11 companies observed over five years, resulting in 55 panel data observations. Data were analyzed using panel data regression through the Common Effect Model, Fixed Effect Model, and Random Effect Model, with model selection based on the Chow, Hausman, and Lagrange Multiplier tests. The results indicate that capital structure, asset growth, and firm size have no significant effect on firm value, either partially or simultaneously. These findings suggest that firm value in the plastic and packaging sub-sector is not sufficiently explained by financing structure, asset expansion, or company size. Investors are more likely to consider other factors, such as profitability, operational efficiency, cash flow, sales growth, raw material risk, and sustainability prospects. Therefore, companies should improve financial performance, asset efficiency, cost control, and sustainable innovation to enhance firm value.

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.

This paper examines the determinants of income inequality within the SDGs framework in Riau Province. Utilizing BPS secondary data from 2018–2025 across 12 regencies/cities, data were estimated using te random effect modelapproach applied to panel regresiion in EViews 12. F-test finding indicate that-HDI, LN_GRDP, and our jointly and  significant influences on the Gini Ratio (R2 = 39.68%). Partially (t-test), HDI has a significant positive effect due to labor polarization in modern sectors. Conversely, LN_GRDP has a significant negative effect, confirming a trickle-down effect in reducing inequality. Meanwhile, OUR has no significant effect due to high labor absorption in Riau's informal agricultural and plantation sectors.

The development of sustainability concepts in the banking sector has encouraged companies to implement Environmental, Social, and Governance (ESG) principles and develop Green Investment initiatives as part of their long-term business strategies. This study aims to examine the effect of ESG implementation and Green Investment on the firm value of banking companies listed on the Indonesia Stock Exchange during the 2022–2025 period. The research employed a quantitative approach using panel data regression analysis. Model selection was conducted through the Chow, Hausman, and Lagrange Multiplier tests, which indicated that the Random Effect Model (REM) was the most appropriate model for the analysis. The results reveal that ESG has a positive and significant effect on firm value, indicating that better implementation of Environmental, Social, and Governance practices enhances a company's value in the perception of investors and the market. Conversely, Green Investment has a negative and significant effect on firm value. This finding suggests that Green Investment is still perceived as a costly activity and has not yet generated direct economic benefits in the short term. Simultaneously, ESG and Green Investment significantly influence firm value, with the model explaining 35.1% of the variation in firm value. The findings imply that banking companies should continuously improve the quality of ESG implementation and optimize the management of Green Investments to create greater firm value and support long-term business sustainability.

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.

This study examines the effect of Environmental, Social, and Governance (ESG) performance on firm value in the Consumer Staples and Consumer Discretionary sectors listed on the Indonesia Stock Exchange during 2021–2023. The study is motivated by inconsistent empirical findings regarding the relationship between ESG and firm value, as well as differences in sectoral characteristics that may influence ESG effectiveness. The objective of this study is to analyze the impact of ESG on firm value in both sectors and compare the differences between them. This research employs panel data analysis using samples of 23 Consumer Staples companies and 19 Consumer Discretionary companies over a three-year period, resulting in 69 and 57 observations respectively. The Fixed Effect Model was applied to the Consumer Staples sector based on the Hausman test result, while the Consumer Discretionary sector used the Random Effect Model. Firm value was measured using Tobin’s Q, ESG performance was proxied by Bloomberg ESG Score. The findings indicate that ESG has no significant effect on firm value in the Consumer Staples sector, while ESG has a significant negative effect in the Consumer Discretionary sector. These results suggest that the relevance of ESG depends on sector characteristics and market perceptions of ESG investment in Indonesia.

Motor Vehicle Tax (PKB) is a key pillar of Regional Original Revenue (PAD) that supports development funding. However, seasonal fluctuations in payment realization create uncertainties in local budget planning. This study aims to address the limitations of the standard Random Forestalgorithm, which suffers from extreme prediction failures on time-series data due to its inability to capture temporal transitions between months. The proposed solution implements feature engineering using a Cyclical Encoding approach (Sine and Cosine transformations) and Lagged Variables. The dataset comprises historical records of motor vehicle tax potential and realization from January 2021 to November 2025. The baseline model evaluation without feature engineering yields highly inaccurate predictions with a Mean Absolute Percentage Error (MAPE) of 203.47% (accuracy of -103.47%). Conversely, after integrating Cyclical Encoding and Lagged Variables, the proposed model's performance improves drastically, achieving a MAPE of 14.40% (an accuracy rate of 85.60%), an MAE of 9,317 units, and an RMSE of 12,638 units. Feature Importance analysis confirms that the cyclically encoded month feature contributes the highest weight to the model's decisions with a score of 0.5031, followed by the potential feature at 0.1798. This study demonstrates that time-based feature engineering effectively optimizes Random Forestfor precise tax revenue forecasting.

This study aims to analyze the influence of social support and soft skills on the work readiness of the 2022 cohort students in Tasikmalaya City. Work readiness is an essential aspect that students must possess to face increasingly competitive labor market demands. This research employed a quantitative approach using a survey method. Data were collected through an online questionnaire distributed to 110 respondents selected from a population of 150 students using the Slovin formula with a 5% margin of error and a simple random sampling technique. Data analysis was conducted using multiple linear regression with the assistance of IBM SPSS Statistics 25. Prior to hypothesis testing, validity and reliability tests were performed to ensure the quality of the research instruments. In addition, classical assumption tests, including normality, multicollinearity, heteroscedasticity, autocorrelation, and linearity tests, were conducted to verify the suitability of the regression model. The findings indicate that both social support and soft skills have a positive and significant effect on students’ work readiness. Support from family, peers, and the academic environment enhances students’ confidence in preparing for employment. Furthermore, communication skills, teamwork, problem-solving abilities, and responsibility as components of soft skills strengthen students’ readiness to enter the professional workforce. These findings highlight the importance of developing soft skills and strengthening social support to improve students’ work readiness.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

This study investigates the effect of total risk on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Total risk is operationalized through the systematic risk proxy (Beta/β), estimated via the Capital Asset Pricing Model (CAPM) framework as the covariance between individual stock returns and the market return divided by the variance of market returns, using the Jakarta Composite Index (JCI) as the market benchmark. Firm performance is measured through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is operationalized following Gerson et al. (2025) as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies, with model selection guided by the Chow and Hausman specification tests. The Fixed Effect Model (FEM) is adopted for ROA, ROE, and SG, while the Random Effect Model (REM) is applied for Tobin’s Q. Results indicate that systematic risk exerts a significant negative effect on ROA (β = −0.312; p < 0.01) and ROE (β = −0.278; p < 0.01), but is statistically non-significant for Tobin’s Q, suggesting that capital market pricing in Indonesia does not fully incorporate systematic risk information. Critically, systematic risk exerts the largest and most significant negative effect on sustained growth (β = −0.347; p < 0.01), revealing a dual transmission mechanism through which risk suppresses ROE while simultaneously inducing more conservative dividend policies, both of which constrain long-run growth sustainability. These findings carry important implications for corporate risk management strategy and empirically enrich the literature on risk, performance, and growth in emerging capital markets.

This study examines the effect of working capital on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Working capital is operationalized through three distinct proxies derived from Akgün and Memiş Karatəs (2021): the Cash Holding Level (CHL), which measures the proportion of cash and cash equivalents relative to total assets; the Cash Interactive Effect (CIE), which captures the efficiency of converting revenue into operating cash flow; and the Gross Working Capital Ratio (GWCR), which reflects the share of current assets within total assets. Firm performance is assessed through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is measured using the model proposed by Gerson et al. (2025), expressed as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies. The study employs the Fixed Effect Model (FEM) for ROA and ROE, and the Random Effect Model (REM) for Tobin’s Q, as determined by the Hausman specification test. The findings reveal that CHL and CIE exert significant positive effects on ROA and ROE, while CIE is the only proxy to produce a statistically significant positive effect on Tobin’s Q. With respect to sustained growth, CHL and GWCR demonstrate significant negative effects, whereas CIE shows a significant positive effect, indicating that operational efficiency dimensions of working capital actively support long-term growth sustainability. These results reinforce the liquidity management theory and contribute empirical evidence that the structure and efficiency of working capital are strategic determinants of both short-term financial performance and long-term growth sustainability in Indonesia’s consumer goods manufacturing sector.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

This study examines how solvency, profitability, liquidity, and operating costs are affected by book-tax differences (BTD) among property and real estate companies listed on the Indonesia Stock Exchange from 2022 to 2024. One key indicator of financial reporting transparency is BTD, which reflects the difference between accounting and taxable income. This is particularly relevant for the property sector, which contributes Rp185 trillion to national tax revenue. The results of the study, conducted using the Random Effects Model panel data regression method with 93 observations from 31 companies, show that solvency (DER) has a significant effect on BTD, while profitability (ROA) also has a significant effect, indicating that companies with high profits tend to engage in more aggressive tax planning practices and financial reporting strategies. On the other hand, liquidity and operating costs do not have a significant impact on corporate tax reporting behavior. 98% of the variation in BTD can be explained by the model.

This study aims to determine the effect of tax planning and capital structure on firm value, with firm size as a moderator. The population in this study is all non-cyclical consumer companies in the food and beverage subsector listed on the Indonesia Stock Exchange (IDX) for the 2020-2024 period. The type of research used in this study is quantitative associative with secondary data. The research sample was determined using a purposive sampling technique. Based on this technique, 24 companies were obtained that met certain criteria. The panel data regression technique used in this study is the Random Effect Model. Testing of panel data regression and moderation regression uses the E=views 13 application. The results of this study indicate that tax planning partially has no effect on firm value, while capital structure does affect firm value. The results simultaneously show that tax planning and capital structure affect firm value. The results of this study also indicate that firm size weakens the relationship between tax planning and firm value, and firm size is also unable to moderate or weaken the relationship between capital structure and firm value.

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.

This study aims to analyze the effect of direct learning on engine components toward students' vehicle diagnostic abilities in vocational education. Employing a quantitative approach with an ex post facto design, the sample consisted of 80 students selected using simple random sampling from a population of 100 students. Data were collected using validated and reliable questionnaires. The data analysis techniques included descriptive statistics, classical assumption tests, and simple linear regression analysis. The results show that direct learning has a positive and significant effect on students’ vehicle diagnostic abilities, with a significance value of 0.000 < 0.05 and a coefficient of determination (R²) of 0.264. This indicates that direct learning contributes 26.4% to the improvement of students' diagnostic abilities. The findings emphasize the importance of practical, hands-on learning approaches in vocational education. These approaches not only enhance students' understanding of automotive theory but also improve their diagnostic skills. The study highlights that systematic learning and direct interaction with engine components are key factors in developing students' competencies, making them more effective in real-world vehicle diagnostics.

This study aims to analyze the influence of egalitarian leadership, responsibility, and teacher work discipline on teacher performance with the moderation of psychological empowerment at Qur'anic Education Institutions (TPQ) in Bengkong District, Batam. A quantitative approach was employed using Structural Equation Modeling Partial Least Square (PLS-SEM) via SmartPLS software. The population comprised all TPQ teachers in Bengkong District, with a sample of 84 teachers selected by proportionate random sampling. Teacher performance was assessed by TPQ principals to enhance objectivity. Results revealed that work discipline is the strongest predictor of teacher performance (coefficient 0.454; T=4.370; p=0.000), followed by responsibility (coefficient 0.254; T=2.557; p=0.011), and psychological empowerment (coefficient 0.232; T=1.992; p=0.047). Egalitarian leadership showed no significant direct effect (p=0.720). Psychological empowerment did not significantly moderate the relationships between the three independent variables and teacher performance. The model demonstrated very high predictive power with R Square of 0.929. This study implies that strengthening work discipline and responsibility are priority strategies for improving TPQ teacher performance.

This study aims to analyze the development of regional financial performance and its effect on economic growth in Jambi Province during the 2019–2023 period. Regional financial performance is measured using the Regional Financial Independence Ratio and the Fiscal Decentralization Degree (FDD), while economic growth is proxied by Gross Regional Domestic Product (GRDP). This research employs a quantitative approach using secondary data obtained from the Central Statistics Agency (BPS), the Directorate General of Fiscal Balance (DJPK), and regional government financial reports covering 11 regencies/cities in Jambi Province. The analytical method applied is panel data regression using the Random Effect Model (REM). The results indicate that both regional financial performance and economic growth in Jambi Province experienced fluctuations throughout the study period. Partially, the Fiscal Decentralization Degree (FDD) has a positive and significant effect on GRDP, with a probability value of 0.0138. In contrast, the Regional Financial Independence Ratio does not have a significant effect on GRDP, as indicated by a probability value of 0.8843. Simultaneously, FDD and the Regional Financial Independence Ratio significantly affect GRDP, with an F-statistic probability value of 0.0390. The coefficient of determination (R²) is 0.1173, indicating that 11.73% of GRDP variation can be explained by the two variables, while the remaining 88.27% is influenced by other factors outside the research model. This study concludes that strengthening regional fiscal capacity through the optimization of local own-source revenue and fiscal decentralization policies is essential for promoting sustainable economic growth in Jambi Province.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

This study aim to examines effectiveness concerning feedback based upon motor model inside of improving free throw accuracy inside of basketball plus its impact upon retention concerning skill among female student. Significance concerning this research lie inside of essential role concerning feedback inside of motor learning, particular when supported by way of accurate performance model that help learner to builds clear mental representation concerning movement execution.Experimental design were employed, involving sample concerning female student random assigned into two group. Experimental group receive feedback supported by way of motor model, including video demonstration concerning ideal plus corrected performance, while control group follow traditional instruction relying primary upon verbal explanation. Post-test were then administered to measures improvement, along alongside delayed retention test to evaluates persistence concerning learning over time. Result show statistical significant difference inside of favor concerning experimental group inside of both post-test plus retention outcome. This indicate that motor model-based feedback effective enhance performance plus support long-term skill retention. Addition, student exposed for motor model demonstrate greater ability to recognizes plus corrects error plus show deeper understanding concerning correct technique.Study recommend integrating motor modeling strategy plus technology-based feedback into sport education to accelerates learning plus improves outcome across educational context.