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Developing and developed countries rely heavily on the use of electrical energy to carry out social and economic activities. The purpose of this study is to analyze economic growth and its influencing factors in the period 2004-2023. This research uses time series data. The data method used is the VECM model. The results of this study indicate that there is a one-way causality relationship between variables. In the short term only CO2 emission variables affect economic growth, while in the long term all variables such as electricity and population can affect economic growth.

Social assistance is an important aspect of government and non-government programs that can help on a large scale for the community so that the impact is to lighten life in the short term, but social assistance has several criteria such as income, social conditions, family status and the impact of the economic situation. . Knowing the criteria for social assistance is done by applying data mining to social assistance using the Naive Bayes algorithm procedure which produces accuracy calculations from 100 testing data, obtained good values, namely accuracy of 95.00%, precision of 92.31%, and recall of 97.95%.

Indonesia is the first palm oil producing country and palm oil exporter in the world. In recent years, the volume of Indonesian palm oil exports has decreased. The determinants of the volume of Indonesian palm oil exports are the United States dollar exchange rate, the area of ​​Indonesian palm oil plantations, and the price of CPO. This research aims to analyze the influence of the United States dollar exchange rate, oil palm land area, and CPO prices in the short term and long term, simultaneously and partially on the volume of Indonesian palm oil exports. The type of data in this research is secondary data in the form of quantitative data obtained from the Indonesian Central Statistics Agency (BPS), Bank Indonesia and the World Bank. This research was conducted in Indonesia from 1994-2023, with a total of 30 observations. The data analysis technique used is the Error Correction Model (ECM). The results of the research show that the US dollar exchange rate and the area of ​​Indonesian palm oil plantations in the short and long term have a significant effect, while the CPO price has an insignificant effect on the volume of Indonesian palm oil exports. The United States dollar exchange rate, the area of ​​Indonesian palm oil plantations, and the price of CPO simultaneously influence the volume of Indonesian palm oil exports. The United States dollar exchange rate has a partially negative and significant effect on the volume of Indonesian palm oil exports. The area of ​​Indonesian oil palm land has a partially positive and significant effect on the volume of Indonesian palm oil exports. CPO prices have a negative and partially insignificant effect on the volume of Indonesian palm oil exports.

This study aims to determine the influence of the Influence of Money Supply, Inflation and Rupiah Exchange Rate on Murabahah Financing Margin in Indonesia Sharia Banks throughout Indonesia from 2013-2022. The type of research conducted is quantitative research. The method used in this study used the analysis of the Vector Auto Regression model of the VECM model and a data tool processed using Eviews 10. The data used is secondary data taken through the official website of the Central Statistics Agency (BPS) for a period of 10 (ten) years from 2013-2022. Based on the results of the study, it is known that the results of the Vector Error Correction Model (VECM) test of this study show that only the variables of the Money Supply and the Rupiah Exchange Rate have a positive and significant impact, while the influence of the Inflation variable on Murabahah Financing Margin (Case Study of Bank Syariah Indonesia in 2013-2022) in the short term has a positive and insignificant impact. The Effect of Money Supply and Inflation on Murabahah Financing Margin has a positive and significant impact, while the influence of the Rupiah Exchange Rate variable on Murabahah Financing Margin in the long term has a negative and insignificant impact. 

This study aims to determine the effect of Economic Growth Rate, BI Rate , Inflation, Exchange Rate and Oil Price on Bond Yields, especially Government Bonds of the Republic of Indonesia for the period 2010-2023. The method used is a quantitative method with econometric analysis of Error Correction Model (ECM) regression. The data used in this study were obtained from statistical reports on the website of the Financial Services Authority of the Republic of Indonesia (OJK-RI), Bank Indonesia, the Central Statistics Agency and several related agencies. The results of the study indicate that economic growth and oil prices are negatively correlated but do not significantly affect bond yields in the long run. The variables of BI Rate and Exchange Rate are positively correlated and significantly affect bond yields. The variable of Inflation is positively correlated but does not significantly affect Bond Yields. While for the short term, partially the variables of BI Rate and Exchange Rate are positively correlated and significantly affect Bond Yields. The variables of inflation and oil price are positively correlated but do not significantly affect bond yields. The Economic Growth Rate variable is negatively correlated but does not have a significant effect on bond yields. Together, the independent variables consisting of Economic Growth Rate, BI Rate, Inflation, Exchange Rate, and Oil Prices have an effect on bond yields, both in the long term and the short term.

This research examines the impact of the use of cloud storage by companies on various types of security breaches, both in the short and long term. Adoption of cloud storage is associated with an increase in external breaches and unintentional internal errors in the short term. However, over time, the positive relationship between use of Cloud storage and external infringement becomes less significant in the long term. Research findings show that use of cloud Storage can reduce the risk of unexpected internal error in the longer term, but does not have a significant relationship with harmful internal breach. This research confirms the importance of companies allocating resources wisely to manage security risks in using cloud storage for both short- and long-term purposes. The researchers contributed to an understanding of the security of cloud storage by applying the theory of attention, which highlighted the importance of paying attention to temporal aspects in the management of enterprise IT security through various attention mechanisms.

With regard to the ratio of fiscal decentralization, effectiveness of PAD, regional financial efficiency, and compatibility of regional spending, this study intends to analyze the regional financial performance of Magelang Regency. This study examines the financial position of the Magelang Regency Government from 2018 to 2022 using quantitative descriptive analysis. According to the decentralization degree ratio analysis, the Magelang Regency Government Budget generates high revenue figures, with an average decentralization rate of 97.50% between 2018 and 2022. According to the variance analysis, the expenditure performance of the Magelang district government in general can control the budget, so that it can achieve an average surplus of Rp. 755,037,801,786.6 (remaining after budget calculation) between 2018 and 2022. According to the variance analysis, the expenditure performance of the Magelang district government in general can control the budget, so that it can achieve an average surplus of Rp. 755,037,801,786.6 (remaining after budget calculation) between 2018 and 2022. The Magelang Regency Government spent 93.09% of its total budget on direct expenditures in 2018–2022, while only 1.39% was allocated for indirect expenditures. Based on the analysis of expenditure efficiency, the efficiency level of the Magelang Regency Government for the 2018–2022 fiscal year can be assessed as efficient, with an average of 98.85%. Annual operating expenses for the short term are still prioritized by the Magelang Regency Government over capital expenditure for long-term growth in its regional budget.

This research aims to determine the effect of debt policy on the financial performance of manufacturing companies in the food and beverages sector from 2019 to 2020 which are listed on the Indonesia Stock Exchange.  Debt policy is proxied by short term debt (STD), long term debt (LTD), and total debt (TD), while financial performance is used by return on equity (ROE). The method in qualitative research in determining the sample uses a purposive sampling method. The sample used was 9 companies.  The analysis technique used is multiple linear regression. Based on the research results, it shows that the table shows that the results of the t test (partial) are that the short term debt variable is 0.456 > 0.05 so that H0 is accepted and Ha is rejected, which means that this independent variable partially has no significant effect on the Return on Equity variable. The long term debt variable is 0.019 < 0.05 so that H0 is rejected and HA is accepted, which means that this independent variable partially has a significant effect on the Return on Equity variable, while the total debt variable is 0.002 < 0.05 so that H0 is rejected and Ha is accepted, which means  This means that this independent variable partially has a significant effect on the Return on Equity variable. Companies included in the Food and Beverages sector manufacturing company group listed on the Indonesia Stock Exchange (BEI) for the 2019-2020 period. Potential investors who want to invest must consider the company's existing debt policy.  You need to pay attention to the debt strategy used, because long-term debt will affect the company's financial performance.

The aim of the research is to evaluate the effect of using non-cash payment instruments on the demand for currency in Indonesia. Time series data is used in the time period 2009 to 2023 with monthly observations. The analysis approach uses the Error Correction Model (ECM) to assess the correlation between variables in the short and long term. The estimation results confirm that the use of ATM/debit cards has a positive and significant impact on the need for currency, both in the short and long term. In contrast, credit card use shows a negative and significant impact on the need for currency in both time periods. Meanwhile, the use of e-money does not have an impact in the short term, but has a positive impact in the long term on the need for paper money in Indonesia. Therefore, this research suggests that Bank Indonesia should encourage more use of electronic payment tools to encourage people to reduce the use of currency, and promote the concept of a society that uses less cash (less cash society).

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.