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The maintenance of non-aviation defense equipment (main weapon system) is a critical aspect in maintaining operational readiness. However, the Maintenance, Repair, and Overhaul (MRO) system in Indonesia still faces limitations due to manual reporting, inefficiency in spare parts management, and the lack of integration of the Life Cycle Cost (LCC) approach. This study aims to design and develop the Integrated Cavalry Monitoring and Maintenance System (ICMMS) based on a web application that integrates sensors, real-time data analytics, and LCC calculation. The prototyping method was used, involving design, development, integration, and testing phases on the Maung Tactical Vehicle and Anoa Armoured Personnel Carrier at PT Pindad. The results of the prototype implementation showed a significant increase in maintenance efficiency: damage reporting time decreased from ±3 hours to ±1 minute, critical component identification became 95% faster, and maintenance scheduling shifted from reactive to predictive. Additionally, the integration of the LCC algorithm allows for more accurate maintenance cost estimation, supporting technical and strategic decision-making. This study demonstrates that ICMMS based on LCC can be an innovative digital solution to enhance MRO effectiveness and operational readiness of non-aviation defense vehicles in Indonesia. It is expected that this system will improve the resilience and cost-effectiveness of managing Indonesia’s military vehicle fleet.

The rapid development of information technology has significantly impacted various sectors, including education. SDN 21 Bantan, an elementary school in Bengkalis Regency, faces challenges in managing student grades manually, which is time-consuming and prone to errors, particularly in compiling report cards. This study aims to design a web-based student grade management information system using the Scrum methodology, which is agile and adaptive. The system is designed to assist teachers in processing student grades quickly and generating computerized report cards. The Scrum method is implemented through stages such as Product Backlog, Sprint Planning, Daily Scrum, and Sprint Retrospective. User requirements are identified using the MoSCoW technique, while task estimation is conducted with Planning Poker. The system supports three main types of users: admin, teacher, and principal, each with specific access rights based on their needs. The results show that the developed system facilitates efficient student data management, grade processing, and report card generation. As a web-based system, it allows users to access and manage data anytime and anywhere with an internet connection. This system is expected to improve the effectiveness and efficiency of student grade management at SDN 21 Bantan and serve as a reference for developing similar systems in other educational institutions.

The role of forensic anthropology is to identify the unknown skeletal remains to assiss in criminal investigation. Age estimation is one of the essential aspects of individual identification. Geometric morphometric is a technique to quantify the morphological of an object using the Cartesian coordinates of anatomical landmarks. There were no studies doing on the T12 vertebra for identification purposes using geometric morphometric techniques. This is an analytic observational study with a retrospective cross sectional study design. Samples were taken from 100 CT scan images at Radiology departemetnt of Dr Kariadi hospital. The age groups as independent variable, while both centroid size which represent the size and Principal component (PCs) which represent the size as the dependent variable. The differences between age group were analyzed using one way ANOVA test. There was a significant difference between age groups in the size of the T12 vertebra with p value = 0.003 (p<0.05). There was no significant difference in size between age groups in size, with p value = 0,149 (p>0,05). Using the Geometric morphometric approach, the vertebra T12 showed significant difference in size.  

This study aims to compare the results of student living cost estimates over a four-year study period using two approaches in financial mathematics, namely the discrete model and the continuous model. The background of the study is based on the need for students to manage their personal finances effectively amidst rising living costs due to inflation. The discrete model is used to predict expenses at certain time intervals, while the continuous model assumes that changes in the value of money occur continuously at all times. This study uses a quantitative descriptive-comparative method with controlled simulations on 100 student data with variations in monthly living costs between Rp2,000,000–Rp4,000,000 and a random inflation rate of 0%–20%. The data were analyzed using discrete and continuous growth formulas, then a Paired Sample t-Test was performed to determine significant differences between the two models. The results show that both models produce very similar living cost estimates with an average difference of only about 1–3% of the total four-year costs. The continuous model produces slightly higher results than the discrete model due to its exponential and continuous nature of calculations. However, the statistical test results showed a p-value > 0.05, indicating no statistically significant difference between the two. Practically, both approaches can be used equally in student financial planning, with the discrete model being more appropriate for short-term projections and the continuous model being more appropriate for long-term projections.

Coal is one of the main energy sources and the largest contributor to national revenue; however, its management faces challenges related to limited availability and accuracy in reserve estimation. An essential aspect of mining management is monitoring the Run of Mine (ROM) volume, which plays a critical role in crushing, washing, and blending processes. This study aims to compare the accuracy of ROM volume measurements using Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle (UAV) methods in the production area of PT FAD, Berau Regency, East Kalimantan. A quantitative descriptive approach was employed, involving field data acquisition, three-dimensional modeling, and volume analysis using specialized software. The results show that ROM volume measured with TLS was 1,407.669 lcm, while UAV produced 1,387.357 lcm, with a difference of 20.312 lcm or 1.45%. This deviation is within the ASTM D6172-98 tolerance limit (<2%), indicating that both methods are valid. Although TLS offers higher accuracy, UAV is more effective and efficient in terms of measurement time, making it a reliable alternative for modern mining monitoring. This study provides practical insights for the mining industry in selecting ROM volume measurement methods that are not only accurate but also efficient in supporting sustainable operations and data-driven decision-making.

This study aims to explain and also prove the hypothesis regarding the effect of total asset turnover (TATO), debt to equity ratio (DER), current ratio (CR), and net profit margin (NPM) on profit changes in coal sub-sector companies listed on the Indonesia Stock Exchange in 2020-2021. The study uses quantitative research using purposive sampling techniques, the population is coal sub-sector companies and a sample of 32 financial reports from 8 companies in 2020-2023. The analytical techniques used are descriptive analysis, panel data estimation methods, classical assumption tests, t-tests (partial), F-tests (simultaneous), and coefficients of determination (R2). The results of the research that has been conducted, it can be concluded that total asset turnover partially does not have a significant effect on profit changes. The debt to equity ratio variable partially has a positive and significant effect on profit changes. The current ratio variable partially has a positive and significant effect on profit changes. The net profit margin variable partially has no effect on profit changes with. Simultaneously, the variables total asset turnover, debt to equity ratio, current ratio, and net profit margin influence changes in profit.

This study investigates the influence of economic growth, investment, and minimum wage on the open unemployment rate across districts and cities in East Kalimantan Province. The research employs a quantitative descriptive approach with panel data regression analysis, processed using EViews 13 software. Model selection was carried out through the Chow and Hausman tests, which identified the Fixed Effect Model (FEM) as the most appropriate estimation technique. The study utilizes secondary data from 2018 to 2024, obtained from the Central Bureau of Statistics (BPS) and the Investment and One-Stop Integrated Service Office of East Kalimantan Province. The empirical findings demonstrate that economic growth exerts a negative and statistically significant impact on the open unemployment rate, indicating that higher economic growth effectively contributes to reducing unemployment in the region. Foreign Investment (PMA) is found to have a negative but insignificant effect, suggesting that inflows of foreign capital alone do not directly translate into job creation unless accompanied by supporting policies and local labor absorption capacity. In contrast, Domestic Investment (PMDN) shows a positive yet statistically insignificant relationship with unemployment, reflecting the possibility that domestic investments may not always generate sufficient employment opportunities in the short term due to structural constraints or sectoral imbalances. Furthermore, the minimum wage variable has a negative and significant effect on the open unemployment rate, implying that increases in the regional minimum wage can stimulate greater employment absorption and improve labor market conditions. Overall, the results highlight the importance of fostering sustained economic growth and designing investment policies that are more labor-intensive to optimize employment creation. Additionally, the findings emphasize the strategic role of minimum wage policy in supporting job opportunities while safeguarding workers’ welfare.

Accurate battery modeling is crucial for the development of battery-based energy storage systems, especially for real-time control and energy management applications. This study proposes a dynamic parameter modeling approach for LiFePO₄ batteries using a first-order Thevenin equivalent circuit model. Parameter estimation is performed to obtain the internal battery parameters based on the Thevenin model, and the parameter dynamics are derived using the Euler numerical method to represent battery behavior during charging and discharging processes. Model validation is conducted by comparing the predicted terminal voltage with actual measurements using root mean square error (RMSE) and mean absolute error (MAE) as evaluation metrics. The results show that the model accurately captures the battery dynamics, with an RMSE of 0.233 and an MAE of 0.047. Therefore, the proposed model is suitable for real-world applications that require accurate and dynamic estimation of internal battery parameters.

This study aims to determine whether there is a significant difference in stock returns before and after stock splits among companies listed in the LQ-45 Index on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. A stock split is a corporate action believed to provide a positive signal to investors. This research uses a quantitative approach with an event study method. The sample consists of 14 companies that carried out stock splits while being listed in the LQ-45 Index. Stock returns are calculated using an 11-day event window and a 60-day estimation period. The data analysis technique employed is the paired sample t-test to examine the difference in returns. The results show a significant difference, with a p-value of 0.006 < 0.05. However, the difference is negative, as most companies experienced a decline in stock returns after the stock split. This decrease may be caused by investors engaging in profit-taking after the stock split euphoria, or due to the short observation period, which may not have fully captured the market’s response. The author recommends that companies carefully consider the timing and implications of stock splits and ensure transparent communication with investors.

Economic growth in Bali Province is predominantly driven by the tourism, service, and agricultural sectors, which play a vital role in the region’s development and income generation. Over the 2014–2024 period, however, the province has experienced a fluctuating and generally declining growth trend, influenced by both internal and external economic dynamics. This study aims to examine the influence of unemployment, district minimum wage (Upah Minimum Kabupaten/Kota—UMK), and education on economic growth across the nine regencies and cities in Bali Province. Employing a quantitative approach with an associative research design, the study utilizes panel data that combines cross-section data (9 regencies/cities) and time-series data spanning 2014–2024, yielding a total of 99 observations. To ensure robust estimation, panel data regression analysis was conducted, with the Random Effect Model (REM) selected as the most appropriate method based on the results of the Hausman test. The empirical findings reveal that, simultaneously, unemployment, UMK, and education have a significant influence on regional economic growth in Bali. Partially, education exerts a positive and significant effect, indicating that improvements in educational attainment and quality can drive higher productivity and foster sustainable economic development. Conversely, UMK demonstrates a negative and significant impact, suggesting that increases in the minimum wage, while beneficial for workers’ welfare, may impose financial burdens on businesses—particularly small and medium-sized enterprises—thus potentially slowing economic activity. Similarly, unemployment has a negative and significant effect, underscoring its detrimental role in limiting economic output and household income. These results emphasize the need for policymakers to strike a balance between enhancing workforce welfare through wage regulations and ensuring that such measures do not hinder economic competitiveness. Furthermore, strengthening education policies, improving access to quality learning, and aligning educational outcomes with labor market demands are crucial for supporting long-term economic growth in Bali.

This article explains the influence of intellectual performance efficiency (MVAIC) and the moderating effect of innovation capital on company performance. The research population includes manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2024. This study used a purposive sampling method, and 18 companies meet the criteria, resulting in 108 observations. The MVAIC method was chosen because it encompasses RCE and INCE, and research on this topic in Indonesia is limited. Panel data regression was used for estimation, and Sequential Residual Centering (SRC) was applied to address multicollinearity. The study findings indicate that CEE, HCE, and SCE enhance profitability, while MVAIC, CEE, SCE, and RCE improve productivity. An important finding in this study is the moderating effect of INCE. INCE provides the appropriate environment and mechanisms to enable HC to effectively generate new ideas and improve ROA. Excessive investment in INCE can disrupt the optimization of the company's internal systems, processes, and infrastructure (SC), thereby affecting profitability. Excessive innovation priorities can divert resources from developing and maintaining strong external relationships (RC), thereby hindering productivity. The results of this study contribute to the understanding of potential trade-offs in IC investment, showing that excessive INCE can hinder financial performance derived from SC and RC. The implication, companies need to balance the allocation of IC resources to achieve holistic performance, rather than focusing solely on innovation.

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.

This study aims to examine the influence of financial performance ratios—namely, the liquidity ratio (Current Ratio/CR), solvency ratio (Debt to Asset Ratio/DAR), and activity ratio (Total Asset Turnover/TATO)—on the return on assets (ROA) of companies in the automotive sector listed on the Indonesia Stock Exchange (IDX) during the period 2020–2023. Employing a quantitative research approach with purposive sampling, the study focuses on automotive sector companies that met specific criteria over the observed time span. Data analysis was conducted using EViews version 13 software, and the methodology included descriptive statistics, panel data estimation, classical assumption tests, panel data regression analysis, t-tests (for partial effects), F-tests (for simultaneous effects), and coefficient of determination (R²) tests. The partial test results reveal that the liquidity ratio (CR) has a negative but statistically insignificant effect on ROA, indicating that higher liquidity does not necessarily enhance profitability. Similarly, the solvency ratio (DAR) demonstrates a negative and insignificant effect on ROA, suggesting that increased debt levels are not significantly associated with lower returns. In contrast, the activity ratio (TATO) has a positive and significant effect on ROA, implying that better asset utilization contributes positively to profitability. When tested simultaneously, the combination of CR, DAR, and TATO shows a positive and significant influence on ROA, indicating that these financial ratios collectively impact the profitability of automotive companies. These findings contribute to a deeper understanding of how internal financial indicators relate to profitability in the automotive sector and can inform management decisions and investor evaluations.

This study examines how Environmental, Social, and Governance (ESG) performance influences financial distress, incorporating cost of debt as a moderating variable. Financial distress is proxied by the Interest Coverage Ratio (ICR), reflecting a firm’s capacity to satisfy interest payments. The empirical sample consists of 655 firm-year observations of non-financial companies listed on the Indonesia Stock Exchange from 2014 to 2023. Panel regression with fixed effects and heteroskedasticity-consistent estimation (Panel EGLS with cross-section weights) is employed to analyze the data. Results indicate that ESG performance exerts a positive and statistically significant effect on ICR (β = 0.1189; p < 0.01), implying that firms with robust ESG practices are better able to service their debt and thus face lower financial distress. Additionally, the interaction term between ESG and cost of debt yields a negative and significant coefficient (β = −0.9714; p < 0.05), suggesting that elevated financing costs attenuate the beneficial impact of ESG on financial resilience. These findings are consistent with stakeholder theory, which advocates that proactive engagement with stakeholders enhances corporate stability, and trade-off theory, which underscores the necessity of balancing debt advantages against financial risk. This research contributes to the literature by demonstrating the conditional effect of cost of debt on the ESG–financial distress nexus. From a managerial perspective, the study underscores the importance of integrating ESG initiatives with cost-efficient funding strategies to mitigate financial distress risk and foster sustainable, long-term value creation.

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

This study explores employment development strategies aimed at bolstering economic growth in North Sumatra Province using the Vector Autoregression (VAR) model and an eighteen-year time series dataset. The variables analyzed include the Human Development Index (HDI), total population, Gross Regional Domestic Product (GRDP), Labor Force Participation Rate (LFPR), and Open Unemployment Rate (OUR). The estimation results reveal dynamic interrelationships among these variables over short, medium, and long-term periods. The VAR analysis with a lag of 2 illustrates how each variable contributes to both itself and the other variables. It also shows that past variables (t-1) significantly impact current variables. Furthermore, the response function analysis identifies how a change in one variable is responded to by others across different time horizons. Stability analysis confirms that all variables maintain medium-to-long-term stability over a five-year period. The Forecast Error Variance Decomposition (FEVD) highlights HDI, population, and GRDP as the most influential variables in shaping the employment system and economic development overall. The VAR model used meets the stability test criteria, making the findings a reliable basis for policy research.

Profitability, measured by Return on Asset (ROA), is a key indicator for assessing the performance and resilience of the banking sector. During the 2019–2023 period, the Indonesian banking sector faced significant pressure from the COVID-19 pandemic, which impacted asset quality and financial performance. This study aims to analyze the simultaneous and partial effects of Non-Performing Loan (NPL), the BI Rate, inflation, Net Interest Margin (NIM), and Capital Adequacy Ratio (CAR) on the ROA of commercial banks in Indonesia. This research employs a quantitative approach using monthly secondary data from 2019 to 2023. The analysis was conducted using Robust Least Squares (RLS) with M-estimation, a Wald test for simultaneous significance, and a z-statistic for partial tests. The results indicate that, simultaneously, the five independent variables have a significant effect on ROA with a significance value of 0,000 and a coefficient of determination of 67,1 percent. Partially, NPL has a significant negative effect on ROA, while NIM, CAR, and inflation have significant positive effects. The BI Rate shows no significant influence. The implications of these findings highlight the managerial importance of strengthening credit risk management to control NPL, enhancing intermediation efficiency to maintain a healthy NIM, and preserving capital adequacy. From a policy perspective, these results justify the continued strengthening of prudential supervision over banks' internal ratios by financial authorities. Furthermore, the insignificance of the BI Rate suggests that the monetary policy transmission to bank profitability is indirect, necessitating a focus on internal factors to maintain the stability of the banking sector.

The purpose of this research is to examine how production costs factor into producer choices, particularly those pertaining to output volume, selling price estimation, and company growth strategies.  The research examined micro and small business players in Kediri Regency, East Java, employing a descriptive quantitative technique.  The findings demonstrate that the production cost structure, particularly the preponderance of variable costs, considerably affects the quantity of output and the margin for price adjustment.  It has additionally been demonstrated that saving money encourages manufacturers to take risks and grow their companies.  Moreover, management accounting-based cost tracking, waste reduction, and sourcing local raw materials are all cost management tactics that impact a company's strength and ability to compete.  Producers' long-term strategic decision-making is therefore influenced by effective production cost management, which in turn affects operational sustainability.

Most people are exposed to sudden death as a result of viral hepatitis infection, especially at an advanced age, so we need to know the continuation of the disease until death in a sample of hospitals to know the differences in the services provided by those hospitals. The research aims to estimate a function for the Rayleigh distribution (RD) using several methods (maximum likelihood function, White, s, weighted least square) and choose the best method for estimation using the mean square error (MSE) and compare the survival rates of patients admitted to (Al-Kadhimiya, Al-Karkh, and Yarmouk) hospitals, The results of the research showed in the experimental aspect that the maximum likelihood method for all sample sizes is the best, followed by the weighted least squares method and finally the White method. In the applied aspect, on a sample consisting of (63) patients (liver cirrhosis + liver cancer) for the first quarter of 2025, it was found that their survival rate until death is (33%) for a time average of (2) months and (15) days.

This study aims to analyze the dynamic relationship between population density, Gross Regional Domestic Product (GRDP) per capita, and regional economic inequality in Indonesia over the period 1995–2024. Regional inequality is measured using the Gini Ratio as a key indicator. A quantitative method with a time series approach is employed using the Vector Autoregression (VAR) model. The analysis includes unit root testing, optimal lag selection, VAR estimation, impulse response function (IRF), variance decomposition, and Granger causality testing. The results show that population density has a positive effect on regional inequality, while GRDP per capita has a negative effect. However, both variables are statistically insignificant. The impulse response analysis indicates that a shock in population density tends to increase inequality in the short term, whereas a shock in GRDP per capita tends to reduce inequality. The Granger causality test reveals that population density regional inequality, while GRDP per capita does not have a significant causal effect. Overall, the findings suggest the importance of equitable economic development and population control policies in reducing regional disparities in Indonesia.