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This research aims to analyze village financial management in terms of efficiency and effectiveness based on the performance of the village head in Banjarejo Village, Batanghari District, East Lampung. Village officials should be transparent in managing the village budget APBD in accordance with the principles of accountability, participation and budget responsibility. Village financial management includes planning, implementation, management and reporting stages. The village head's performance is measured using indicators of productivity, service quality, participation, responsibility and accountability. The number of research methods used is explained, and the research subject is the Head of Banjarejo Village. Primary data was obtained through direct interviews with village heads, while secondary data was obtained from books. Good and fair forecasts are made based on budget figures and determination of village income and expenditure in 2022 and 2023. The research results show that productivity reaches 100% in 2022 and 2023, which means the village head's performance is effective. The efficiency level which reaches 100% in 2022 shows good performance, but falls to 89% in 2023 which shows good performance. This situation shows that village fund management must be improved for the success of village funds.

Research on forecasting new student admissions at SD Negeri 10 Koto Tinggi Surian employs data analysis using simple linear regression to predict the number of new students based on previous years and formulate it into a mathematical model. In its calculation process, this approach can adopt both quantitative and qualitative methods. The prediction error rate for the next year is very low. Out of 41 predicted students, only one differs from the actual number. The study's data spans student admissions from 2019 to 2023. The research findings demonstrate the effectiveness of conventional linear regression techniques. The prediction for 2023 indicates an intake of 42 students. Therefore, it can be concluded that simple linear regression accurately predicts new student admissions at SD Negeri 10 Koto Tinggi Surian with high accuracy.    

Dynamic programming is a mathematical technique for optimizing decisions in stages by breaking a problem inton stages mthat solve recursively. It display the quantity and arrangement of invemtory to maintain production and ,businness revenue efficiently when used in inventory control management. This production planning uses a dynamic programming method based on the results of advanced calculations start from stage  1 and move forward to stage 12. the dynamic programming approach in this writing is deterministic because the pattern of the number of requests for bread is known absolutely. Decisions involving a problem are simplified little by little rather than directly. This research uses linear regression with a mathematical formula that is:  followed by the defect percetage formula  to forecast demand for the next 12 months and apply a dynamic program to achieve the minimum total quantity.

This research utilizes the Markov Chain method to analyze daily weather data in the city of Medan. The main objective of this study is to forecast weather changes in the future based on the weather conditions of the previous day. Daily weather data was collected from the nearest weather station over a specific period of time. The analysis results indicate that the Markov Chain model provides good estimates of the likelihood of weather changes from one day to the next. The steady state probabilities demonstrate the dominance of partly cloudy and clear weather in the long term. This research provides valuable insights for various sectors related to weather, such as agriculture, transportation, and tourism.

Indonesia, as a country with complex geological conditions due to the convergence of various tectonic plates, is highly susceptible to natural disasters such as earthquakes, tsunamis, and volcanic eruptions. The city of Semarang, as the capital of Central Java Province, also frequently faces disasters such as floods, landslides, and earthquakes. Predicting the occurrence of natural disasters becomes crucial to mitigate the negative impacts they cause. This study uses the Markov chain method to predict natural disasters in the city of Semarang based on disaster data from 2018-2022. The prediction results indicate a 16% chance of floods, 34% chance of landslides, 10% chance of tornadoes, 22% chance of fires, and 17% chance of falling trees in 2023. Validation of the predictions against actual data for 2023 shows a relatively good match for floods and fires, but there are significant differences in the predictions for tornadoes and falling trees. These results indicate that the Markov chain method has potential in predicting disaster occurrences, but accuracy improvements are needed to account for weather variability and dynamic environmental factors. This research is expected to assist the government and society in enhancing disaster preparedness and mitigation in the future.

Universities' development hinges significantly on student admissions, necessitating accurate predictions for effective planning. This study applies the Monte Carlo simulation method to forecast new student arrivals at the Faculty of Mathematics and Natural Sciences (FMIPA) at Universitas Negeri Medan (UNIMED). Utilizing data from 2021 to 2023 sourced from the PDDikti website, the research employs PHP programming for implementation. The Monte Carlo algorithm's numerical prowess ensures precise statistical data simulation, comprising data collection, probabilistic distribution computations, cumulative distribution determinations, random number generation, and simulation analyses. Simulation results for 2022, 2023, and 2024 exhibit consistent trends, projecting an average of 860 to 930 new students per program. This methodology surpasses manual estimations, offering robust insights for university resource allocation and strategic management. Despite its effectiveness, study limitations, such as model assumptions, warrant continuous validation with actual data. This research advances predictive modeling in higher education, providing a foundation for future enhancements and comprehensive prediction integrations.

Behavioral economics has highlighted how cognitive biases influence financial decisionmaking, often leading to suboptimal outcomes. This paper explores the impact of behavioral biases such as overconfidence, loss aversion, and herding behavior on accounting and economic forecasting. By reviewing empirical evidence from market behavior, the study assesses how these biases affect financial reporting, auditing practices, and economic predictions. The paper concludes with recommendations for accountants and economists to incorporate behavioral insights into their practices to improve decisionmaking and forecasting accuracy.

This research aims to determine the effect of the government expenditure model and green growth based on green energy consumption in Indonesia which has 4 variables, namely carbon emissions, energy consumption, economic growth and government expenditure. The analytical method used in this research is the Vector Auto Regression (VAR) model with the Impulse Response Function (IRF) test, Forecast Error Varince Decomposition (FEVD), stationarity test, cointegration test, structural lag stability test, and optimal lag length test. . The results of the Vector Autoregression research using lag 1 as the basis show the contribution of each variable to the variable itself and other variables. The results of the Vector Autoregression analysis also show that the past variable (t-1) contributes to the current variable, both the variable itself and other variables. From the results of the analysis, there is a reciprocal relationship between one variable and another variable. Response Function Analysis shows the response of other variables to changes in a variable in the short, medium and long term, and it is known that the stability of the response of all variables is formed within a period of 5 years or the medium term. and long term. Variance Decomposition Analysis shows that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as CO2, EC, and GOV. Meanwhile, another variable that has the greatest influence on the variable itself in the short, medium and long term is CO2 which is strongly influenced by GOV and GDP.

Furniture raw materials are still a major challenge in the industry, in line with the wishes of consumers to get good quality raw materials and soaring export demand, so there is a need for a control process to monitor the value of products using forecasting. The purpose of this study was to predict gross domestic product in the furniture industry in Indonesia in 2022. This study used secondary data on the quarterly trend of gross domestic product in the furniture industry in Indonesia 2010-2021 taken from the research industry data processed by BPS and Bank Indonesia, The method used is Double Exponential Smoothing-Holt. The results of the calculation using the double exponential smoothing-holt method obtained a value of α of 0.658 and β of 0.008 where the forecasting results for the 2022 period, namely the 1 quarter of 7.602 billion rupiah, quarter 2 of 7.676 billion rupiah, quarter 3 of 7.749 billion rupiah, and quarter 4 of 7.822 billion rupiah. Where the MAPE value is 0.737% which means forecasting has very good results.

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

This article investigates recent innovations in industrial engineering and mechanical systems, emphasizing how technological advancements impact manufacturing efficiency, sustainability, and cost-effectiveness. Through a review of current technologies, such as additive manufacturing, automation, and smart materials, the study assesses the key challenges industries face and forecasts emerging trends in mechanical engineering and industrial innovation. The article also discusses the potential for artificial intelligence and machine learning to revolutionize industry standards and drive forward engineering solutions.

This study examines the analysis of raw material inventory forecasting and buffer stock of vaname shrimp at one of the fishery companies in Makassar Industrial Estate (PT Bogatama Marinusa Makassar). The analysis method used to determine the supply of raw materials needed by the company is the ARIMA Box-Jenkins method. This method is used to forecast raw material inventory on time series data. Determination of buffer stock is done using standard deviation and policy factors. Vaname shrimp (Littopenaeus vannamei) raw material data was obtained from 2020 to 2022 (156 weeks). The results showed that the highest amount of raw material inventory occurred in October week 147 in 2022, amounting to 152,792 tons, while the lowest amount of raw material inventory occurred in May week 122 in 2022, amounting to 13,102 tons. The best vaname shrimp raw material inventory model is the ARIMA (1,1,1) model which has a Sum Square Error (SSE) value of 1.70250, Mean Square Error (MSE) value of 0.0112007. This model is used to forecast raw material inventory for the next 48 weeks. The forecasting results show that there will be a decrease in week 1 to week 6 and a relative increase in raw materials in week 7 to week 48 with a MAPE value of 4.54%. The amount of buffer stock that must be owned by the company is 37.311 tons.

The estimated rice sales at Perum Bulog Sub Divre Medan is crucial information in planning and managing rice supplies. In this study, we apply the Double Exponential Smoothing forecasting method to estimate rice sales at Perum Bulog Sub Divre Medan. This approach allows us to identify complex sales patterns and generate accurate forecasts to aid in informed decision making. We use historical rice sales data to train a forecasting model and evaluate its performance. Experimental results show that the Double Exponential Smoothing method can provide reliable estimates of rice sales, with a satisfactory level of accuracy. The implications of these findings are discussed in the context of inventory management and operational planning of Perum Bulog Sub Divre Medan.

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.  

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.

Renewable energy microgrids integrated with smart control systems are emerging as a sustainable solution for electrifying rural industrial zones, offering substantial improvements in energy efficiency and reductions in carbon emissions. This study explores the implementation of hybrid renewable energy systems, combining solar and wind energy, and the integration of Internet of Things (IoT) sensors to optimize energy consumption in real-time. The findings highlight that the combination of solar and wind energy in microgrids leads to up to a 30% increase in energy efficiency, with a significant reduction in CO₂ emissions, reaching up to 50% compared to traditional grid systems. IoT sensors play a crucial role in load forecasting, optimization, and system stability, enabling real-time monitoring and proactive adjustments to energy distribution. Additionally, the implementation of these systems in rural industrial zones not only provides reliable, clean energy but also reduces reliance on fossil fuels, making them economically viable and environmentally sustainable. However, challenges such as high initial investment costs, integration complexities, and the need for skilled technicians remain. Despite these barriers, the long-term benefits of reduced energy costs, improved energy security, and lower carbon footprints make renewable energy microgrids a promising solution. The study suggests that these systems can be scaled to other rural regions facing similar challenges in energy access and carbon emissions, offering a path to sustainable development. Further research is recommended to explore alternative renewable energy combinations and advancements in IoT applications to improve system scalability and efficiency.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

Profit growth is important for businesses as it can be used to forecast future business plans. Earnings growth is difficult to separate from the company's financial performance as measured by financial ratios. This study aims to determine the effect of accounts receivable turnover ratio, current ratio, debt to equity ratio, and inventory turnover on profit growth. This study has a population of 17 companies over three years (per quarter), and a sample of 204 collected through purposive sampling method, and this study uses data analysis methods, namely multiple regression analysis and panel data with Eviews 12 software. The analysis shows that ITR has a positive and significant effect on earnings growth (Ln), while RTR, CR, and DER have no effect on earnings growth (Ln). All independent variables, namely RTR, CR, DER, and ITR, affect earnings growth (Ln) simultaneously. Earnings growth (Ln) is influenced by the four independent variables by 9%. This shows the capability of financial ratios in anticipating profit increases and can influence investor investment decisions.

Technology develops from time to time influenced by human activities and needs that continue to increase as well. Technological developments also play a role in helping the agricultural sector. Through technological developments, many problems in agriculture can be resolved. One of the problems is in agricultural extension activities, where farmer participation in extension activities is low in some areas. This research aims to build UI/UX design for an agricultural mobile application called Sobatani which is useful for connecting farmers with agricultural extension workers.  This research uses the Design Thinking method which consists of five stages including empathize, define, ideate, prototype, and testing. The test results using the Single East Question (SEQ) method obtained a total average of 6.6, indicating that the UI design is fairly effective and successful. Although there are some misclicks, so it is necessary to iterate on the Log in and Weather Forecast pages. The use of the Design Thinking method has proven effective in understanding user needs and designing solutions that can provide these needs.