Abstract:Medical disposable is one of important support tools in medical operational and must not be out of stock in order to deliver excellent service in hospital. The pharmacy department has to forecast the demand to supply information…
ormation for decision making in budgeting. In this paper, is comparing double moving average and double exponential smoothing method for 3 ml spuit for time series 01 January to 30 June 2017. The accuracy of forecasting is the most important and it can be measure with MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Value). The smallest value of MAPE and RMSE is having the high accuracy of forecasting. The double moving average method has the smallest MAPE = 0.353 and RMSE = 95.8 compare to Exponential Smoothingand be the best option to use as method to forecast the medical disposable supply demand.
Abstract:Tourism is one of the most important factors for the revenue of an area. To attract the interest of tourists takes some supporting factors, one of which is the hotel or the guesthouse. In the process of prediction of tourists…
rists visit, required data the number of tourists staying in order to predict for the next time. The prediction method used in this system is a method of Backpropagation Neural Network based on time series data forecasting component variansi random or random process beginning with autocorrelation for determination of input variables. Method of Backpropagation itself is known quite well used in the forecasting of time series data. The result of the method of Backpropagation is a number of predictions that tourists visit can be a reference for related officials in taking decisions for the period ahead.
Abstract:The demand for electrical energy continues to rise with the progression of time. This growth must be matched by a reliable and cost-effective supply of electricity, requiring power systems that are both dependable and economical.…
onomical. Since the amount of electricity consumed by users cannot be precisely predicted, balancing generation with consumption necessitates accurate electrical load forecasting. This study focuses on load forecasting using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The forecast developed targets daily peak loads, which fall under short-term load forecasting. The data used for this forecasting consists of historical daily peak loads from January 1, 2017, to June 9, 2022. The forecasting process involves parameters such as radius, squash factor, accept ratio, reject ratio, and epoch. The forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results are then compared with PLN’s load forecasting, which employs the load coefficient method. The ANFIS-based forecasting achieved a MAPE of 1.879%, using networks Jaringan_24 and Jaringan_25. This MAPE value is slightly lower than PLN’s load forecasting MAPE of 1.917%, indicating better accuracy by the ANFIS method.
Abstract:This paper explores the role of Artificial Intelligence (AI) in enhancing decision-making processes within multinational corporations. The primary issue addressed is how AI can be integrated effectively across diverse global…
obal markets, considering factors like regulatory frameworks, cultural diversity, and market dynamics. The research proposes a framework for AI implementation that ensures both operational efficiency and ethical soundness. The study employs a mixed-methods approach, combining qualitative interviews and quantitative surveys from key stakeholders in multinational corporations. Preliminary findings suggest that AI significantly improves decision-making speed and accuracy, particularly in data analysis, market trend prediction, and consumer behavior forecasting. However, challenges remain in adapting AI systems to various cultural and regulatory environments, highlighting the need for customization and local adjustments. This study contributes to understanding how AI can be applied more effectively and ethically across international markets, offering insights for future implementations in diverse business contexts.