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Amidst the growing competition in the industry, CV Berkah Jaya Klaten faces challenges in planning the production capacity of cleaning tools to meet market demand. This study aims to provide solutions to production capacity planning issues by applying the Rough Cut Capacity Planning (RCCP) method using the Capacity Planning Using Overall Factors (CPOF) technique and a system simulation approach. The planning process begins with demand forecasting using IBM SPSS Statistics 25 software, which produces the smallest Mean Absolute Percentage Error (MAPE) value using the Simple Seasonal method. These forecasting results are used to determine the Master Production Schedule (MPS). Processing RCCP data with the CPOF method requires MPS data, processing time for each workstation, and historical proportions calculated from standardized processing times. The system simulation of production capacity planning is conducted to model real conditions and evaluate various production scenarios. The simulation results reveal that the required production time capacity each month always exceeds the available time capacity, indicating the need for capacity adjustments to avoid bottlenecks and improve efficiency. With this approach, CV Berkah Jaya Klaten can plan production capacity more efficiently and effectively, ensuring product availability in accordance with customer demand.

The advancement of information technology, particularly in data analytics, has significantly impacted management accounting practices and strategic decision-making. This study aims to analyze the role of data analytics in management accounting and its impact on strategic decision-making in competitive markets. This study proposes a new framework that integrates data analytics into management accounting, focusing on its application in improving decision-making accuracy, competitive advantage, and operational efficiency. The methodology employed is a literature-based review, examining relevant journal articles, books, and industry reports. Secondary data is used to assess the impact of data analytics in management accounting and strategic decision-making. The findings reveal that the implementation of data analytics in management accounting improves budgeting and forecasting accuracy, provides a competitive advantage through faster market responses, and enhances operational efficiency by reducing costs and streamlining processes. The analysis indicates that while there are challenges such as technological barriers, organizational resistance, and ethical issues, the opportunities presented by data analytics in management accounting are significant. Proper implementation can support more accurate and efficient strategic decision-making, improving a company’s competitive positioning. Future research could focus on exploring the application of emerging technologies like artificial intelligence (AI) and machine learning in management accounting. Additionally, further investigation into the impact of data privacy regulations and ethical challenges is needed.

Human resources as a resource in a company have a strategic role compared to other resources. Humans play a very important role in the development of a company. If a company has higher quality human resources than its competitors, then its performance will definitely increase. Human resource and competency planning in a company plays an important role in improving human resources, especially in terms of employee performance, employee performance to help achieve company goals. In this case, employee performance is a result that can be achieved by an individual or group of people in a company organization according to quantitative or qualitative aspects, considering employee compensation. Apart from that, a mismatch between an employee's competency (the employee's educational background and skills) and their job can also make the employee uncomfortable with what they are doing. This of course greatly affects the level of work productivity in the company. Competencies can clarify work standards and expectations that the company wants to achieve and can make it easier for companies to align work behavior with organizational values. Apart from that, HR planning as an activity is a process of how to meet current and future workforce needs for an organization. In meeting current workforce needs, the HR planning process means efforts to fill/cover workforce shortages both in quantity and quality. HR planning emphasizes forecasting efforts regarding workforce availability based on needs in accordance with future business plans.

In the face of the digital era's rapid evolution, human resource planning has become increasingly crucial for organizations to maintain competitiveness and sustainability. This abstract explores strategic human resource planning (HRP) as a pivotal approach to navigating the complexities posed by digital transformation. The advent of digital technologies has revolutionized industries, requiring organizations to adapt swiftly to remain relevant and efficient. Strategic HRP involves aligning workforce capabilities and organizational goals with the demands and opportunities presented by digital advancements. Key considerations include forecasting future workforce needs, identifying critical skill gaps, and implementing robust talent acquisition and development strategies. Moreover, effective HRP entails leveraging data analytics and predictive modeling to anticipate HR requirements and optimize workforce planning initiatives.The challenges of the digital era necessitate proactive measures in talent management, such as upskilling current employees and recruiting digitally savvy talent. Organizations must foster a culture of continuous learning and adaptability to thrive in a digitally disrupted environment. Strategic HRP also encompasses developing agile HR policies and practices that can accommodate rapid technological changes and evolving employee expectations. Furthermore, integrating technologies like artificial intelligence and automation into HR processes can streamline operations and enhance decision-making capabilities.In conclusion, strategic human resource planning is indispensable for organizations seeking to harness the opportunities presented by the digital era while mitigating its inherent challenges. By prioritizing agility, foresight, and innovation in HR practices, organizations can build a resilient workforce capable of driving sustainable growth and competitive advantage in the digital age.    

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 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 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 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 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.

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.

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.