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This study aims to analyze the influence of Regional Original Revenue (PAD), General Allocation Fund (DAU), Special Allocation Fund (DAK), and Tax Revenue Sharing Fund (DBHP) on Regional Expenditure, as well as to analyze whether the flypaper effect phenomenon occurs in Regencies/Cities in Jambi Province during the 2017-2023 period. The data used in this study is panel data that combines time series data for 7 years and cross-section data from 11 Regencies/Cities in Jambi Province. The analysis method used is panel data regression with the selected model Fixed Effect Model (FEM). The results of the study show that simultaneously (F Test), the variables PAD, DAU, DAK, and DBHP have a significant effect on Regional Expenditure. Partially (t Test), PAD and DBHP do not have a positive and significant effect on Regional Expenditure, while DAU and DAK show a positive and significant effect on Regional Expenditure. This study also found a flypaper effect in regencies/cities in Jambi Province. This indicates that regional governments in Jambi Province tend to be more responsive in increasing regional spending using transfer funds from the central government rather than optimizing their own potential Regional Original Revenue (PAD).

This study aims to analyze demand patterns and determine the most accurate forecasting method for the Kawachi KL 6167 A+ Emergency Lamp product to support inventory control decision-making. The data used in this study consist of product demand over 12 periods, showing an increasing trend with slight fluctuations in certain periods. The forecasting methods applied in this research include the Linear Trend Method, Quadratic Trend Method, and Moving Average (MA), while forecasting accuracy was evaluated using Mean Squared Error (MSE). The results indicate that the linear trend method provides a more suitable forecasting model compared to the quadratic trend method. The MSE value of the linear method is 69.31, whereas the quadratic method produces an MSE of 81.50, indicating that the linear method is more accurate due to its lower forecasting error. In addition, a 3-period Moving Average (MA) method was applied to forecast demand from period 13 to period 24. The forecasting results show that demand tends to stabilize within the range of 276–277 units, with the forecast for period 24 reaching 276.66 units, rounded to 277 units. Based on the findings, it can be concluded that the demand pattern for the Kawachi KL 6167 A+ Emergency Lamp demonstrates a relatively stable upward trend, making the linear trend method the most appropriate forecasting approach for predicting future demand. These forecasting results are expected to serve as a reference for companies in optimizing inventory planning to minimize the risks of stock shortages and overstocking.

The increasing demand for electrical energy in military education facilities necessitates an efficient, reliable, and sustainable energy solution. This research aims to design a Solar Power Plant (PLTS) system to meet the street lighting needs in the Wangi-Wangi Complex of the Indonesian Naval Academy (AAL). The research method used is quantitative descriptive with an engineering design approach thru the stages of site survey, collection of solar energy potential data in the Surabaya area, calculation of electricity energy needs, calculation of solar panel capacity, calculation of battery capacity, and design of battery connection configuration. The research results show that the energy requirement for street lighting is 1,920 Wh/day, sourced from 8 units of 20 Watt LED lamps with an operating time of 12 hours per day. Based on the average solar radiation potential in Surabaya of 5 kWh/m²/day, the designed system requires 3 units of 200 Wp monocrystalline solar panels with a total area of approximately 4.89 m². For energy storage, 4 units of Yuasa N200 12 V 200 Ah batteries are used, configured in a series-parallel arrangement, capable of providing an effective energy of around 3,600 Wh with an estimated operating time of 22.5 hours. The research results indicate that the proposed solar power plant design is feasible to implement as an environmentally friendly, efficient alternative energy source that supports the green defense concept in the AAL environment.

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.

This community service activity aims to enhance public understanding of the role of infrastructure in supporting quality of life, particularly through the utilization of the Sub PPKBD Tambakkemerakan Community Hall in Krian as a social and educational space. The method used is Participatory Action Research (PAR), which emphasizes active community involvement in every stage of the activity, from problem identification to program implementation. The activities were carried out through a series of events that combined elements of togetherness, religiosity, and education, such as communal iftar (breaking the fast), congregational prayers, and an offline webinar featuring student speakers.The results indicate an improvement in community understanding of the importance of utilizing social infrastructure not only as a gathering place but also as a medium for learning and empowerment. In addition, the activity strengthened social interaction, increased community solidarity, and encouraged active participation in utilizing available facilities more productively. Despite several challenges, such as time constraints and differences in participants’ levels of understanding, the activity overall had a positive impact in promoting the optimization of social infrastructure functions. Therefore, the continuation of similar programs is necessary to support sustainable improvements in community quality of life.

This community service activity aims to enhance public understanding of the role of infrastructure in supporting quality of life, particularly through the utilization of the Sub PPKBD Tambakkemerakan Community Hall in Krian as a social and educational space. The method used is Participatory Action Research (PAR), which emphasizes active community involvement in every stage of the activity, from problem identification to program implementation. The activities were carried out through a series of events that combined elements of togetherness, religiosity, and education, such as communal iftar (breaking the fast), congregational prayers, and an offline webinar featuring student speakers.The results indicate an improvement in community understanding of the importance of utilizing social infrastructure not only as a gathering place but also as a medium for learning and empowerment. In addition, the activity strengthened social interaction, increased community solidarity, and encouraged active participation in utilizing available facilities more productively. Despite several challenges, such as time constraints and differences in participants’ levels of understanding, the activity overall had a positive impact in promoting the optimization of social infrastructure functions. Therefore, the continuation of similar programs is necessary to support sustainable improvements in community quality of life.

This literature study examines the process of self-regulation in transforming compliance with school rules originating from external pressure into behavioral regularity emerging from personal awareness, and its impact on the effectiveness of student learning outcomes. Using a qualitative approach with content analysis method, this study synthesizes relevant literature to build a theoretical framework on how self-regulation facilitates the internalization of disciplinary values. The findings reveal that self-regulation occurs through a series of interconnected stages including goal setting, strategy planning, self-monitoring, evaluation, and adjustment. The success of this process is determined by internal motivation, appropriate environmental support, positive direct experiences, and healthy emotional management. Strong self-regulation directly impacts learning outcome effectiveness through improved cognitive strategies, strengthened intrinsic motivation, enhanced time and environment management, and developed capacity to constructively cope with failure. Learning outcomes achieved through self-regulation processes are characterized by lasting understanding, knowledge transfer ability, and the formation of lifelong learning dispositions. Schools and teachers play strategic roles in strengthening self-regulation through curriculum design that supports autonomy, formative feedback, role modeling, and collaboration with parents. This study contributes theoretically by positioning self-regulation as the central mechanism bridging external influences and internal disposition formation.

This research is motivated by the problem of the open unemployment rate in Maluku Province which is still fluctuating, where the influence of human capital indicators such as the Average Years of Schooling and the Gross Enrollment Rate of Senior High Schools as well as economic policies such as the Provincial Minimum Wage often show results inconsistent with theory, thus requiring further empirical studies to determine their influence in the region. The purpose of this study is to analyze and determine the partial and simultaneous effects of the average years of schooling, the gross enrollment rate of senior high schools, and the provincial minimum wage on the open unemployment rate in Maluku Province. The method used is quantitative with secondary data in the form of time series from 2015 to 2024 sourced from the Central Statistics Agency (BPS) of Maluku Province, and analyzed using multiple linear regression techniques. The results show that the average years of schooling have a negative and significant effect, while the gross enrollment rate of senior high schools and the provincial minimum wage each have a positive insignificant and negative insignificant effect on the open unemployment rate. Simultaneously, the three variables also have no significant effect. The implications of these findings confirm that increasing the average length of schooling is a key factor in reducing unemployment, but policies related to minimum wages and high school participation need to be reviewed and combined with other policies to be more effective in addressing unemployment in Maluku Province.  

The criterion for students who have been able to think critically is when they can think systematically, besides having awareness in thinking, then they can see the difference between mistakes and truths. The purpose of this research is to measure the extent of students' ability to think critically on the completion of Higher Order Thinking Skill (HOTS) questions, especially in the subject of mathematics and social arithmetic material for grade VII students. This research is classified as qualitative descriptive research. The subjects involved were 6 students in grade VII D SMPN 3 Ngawi. The method used is qualitative descriptive with data analysis techniques using models from Miles & Huberman. Meanwhile, at the time of data collection, the instrument was in the form of 2 HOTS questions that were in accordance with the research subject. In data collection using tests and interviews, tests are used to measure cognitive competence while interviews are used as a follow-up to explore in depth related to students' answers. Based on a series of research processes, it can be proven that the students studied are divided into 3 categories of ability, namely low, medium, and high.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

The dynamic fluctuations in stock prices present a major challenge for investors in making informed decisions. To anticipate such uncertainties, forecasting methods that can provide accurate predictions are required. This study compares two time series forecasting methods Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (Holt) in predicting the stock prices of PT Telkom Indonesia (TLKM). The dataset consists of monthly closing prices from January 2018 to December 2023. The performance of each model is evaluated using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the ARIMA(1,1,1) model yields higher predictive accuracy than the Holt method, with MAE of 787.71, MSE of 771,844.2, and RMSE of 878.55. In contrast, the Holt method records a MAE of 837.19, MSE of 878,393.4, and RMSE of 937.23. These findings confirm that ARIMA is superior in capturing the complex patterns of stock price movements and is more effective in volatile market conditions such as the stock exchange.

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

Business is an economic activity carried out by individuals or organizations to produce and sell goods or services with the aim of making a profit. The NSH Group Store is a business that sells carpets, pillows, bolsters, and dolls located in the Sei Dadap I/II Plantation, Sei Dadap District, Asahan Regency, North Sumatra 21225. The NSH Group Store was established in 2016 and is owned by Mrs. Siti Komariah Siregar. Among the challenges faced by the NSH Group Store owner are irregular stock procurement. Sales transaction processes still use conventional methods, reducing efficiency and time effectiveness, and potentially leading to data errors. Supply Chain Management is a series of approaches used to efficiently integrate suppliers so that goods can be distributed in the right quantities, locations, and at the right time, with the aim of minimizing overall system costs. A bolster pillow is a pillow that can function as both a pillow and a bolster. Bolster pillows are oval and long, so they can be hugged while sleeping. The benefits of a bolster pillow include maintaining a proper sleeping position, reducing pressure on joints, helping reduce aches, improving sleep quality, and improving overall health. Therefore, by implementing Supply Chain Management (SCM), data processing will be faster and more accurate.

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

This study aims to analyze the impact of world oil prices on Indonesia's macroeconomic stability represented by the BI Rate, the rupiah exchange rate, and inflation during the 2018–2025 period. Fluctuations in world oil prices, acting as a source of external shocks, have the potential to affect the economy through changes in production costs, distribution, and economic activity. The study employs a quantitative approach using monthly time-series data comprising 96 observations. The Vector Autoregression (VAR) method is used to identify dynamic relationships among the variables. The results indicate no long-term relationship between world oil prices, the BI Rate, the rupiah exchange rate, and inflation. VAR estimates suggest that world oil prices do not significantly affect the BI Rate, the rupiah exchange rate, or inflation. Impulse Response Function analysis reveals that the responses of these three variables to world oil price shocks are temporary, with the variables eventually returning to equilibrium. Forecast Error Variance Decomposition results further show that the contribution of world oil prices to explaining variations in the BI Rate, the rupiah exchange rate, and inflation is relatively small. These findings indicate that Indonesia's macroeconomic stability during the study period was influenced more by domestic factors than by fluctuations in world oil prices. The study's results can serve as input for economic policy formulation in the face of global uncertainty.

Government expenditure is a great reason in economic stability and its impact on the balance of payments is dire. In this light, this paper seeks to use the time series analysis method and the ARDL model to investigate the association between the balance of payments of Iraq and the public spending within the 2004-2023 period. The Eviews 13 software was used to analyse it. The findings show that there is a positive association between spending by the people and balance of payment especially at the short run. The latter findings indicate that the efficiency of government expenditure reform is a necessary tool to accomplish the expansion and close the balance of payments deficit. This study highlights the importance of strategic fiscal policies and government spending in achieving a balanced economy and sustainable growth. Additionally, it emphasizes the need for continuous monitoring and adjustment of public spending to ensure its alignment with national economic objectives. The findings contribute to the understanding of fiscal policy implications in developing economies, especially in the context of Iraq’s economic challenges.  

This study aims to analyze the influence of macroeconomic variables in the form of the dollar exchange rate, inflation, and Gross Domestic Product (GDP) on the Composite Stock Price Index (JCI) in Indonesia for the period 2010–2024. The research method used is a quantitative approach with multiple linear regression analysis using time series data obtained from Bank Indonesia, the Central Statistics Agency (BPS), and the Indonesia Stock Exchange (IDX). The data analysis technique was carried out through classical assumption tests and hypothesis testing to determine the relationship between variables. The results of the study show that partially GDP has a significant effect on the JCI, while inflation and the dollar exchange rate tend not to have a significant effect. However, simultaneously these three variables have a significant influence on the JCI. These findings show that macroeconomic stability is very important in maintaining the performance of the capital market in Indonesia and can be a reference for investors in making investment decisions. In addition, the results of the study confirm that national economic growth is the main indicator that market participants pay attention to in assessing investment prospects. Therefore, the government needs to maintain economic stability through effective and sustainable fiscal and monetary policies.