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This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.

Investment is the activity of placing funds in one or several investment objects for a certain period of time to obtain future profits. The important role of investment in supporting economic growth can be seen from its ability to channel funds to sectors in need, thereby increasing productivity, creating jobs, and increasing income. Stock investment has become one of the most popular instruments because of its profit potential, both from dividends and rising stock prices, although it is accompanied by high risk. Technological developments and easy access to information have attracted more people to invest in stocks, especially through stock indices such as the BISNIS27 Index which reflects the performance of the best performing companies. The selection of this index is important as it reflects the overall market performance and provides a strong reference for investors. To analyze stock performance, fundamental and technical analysis approaches are used, which complement each other in providing a complete picture for investors. In addition, forecasting and ECM (Error Correction Model) methods are used to project future stock prices and analyze the long-term relationship between economic variables. The result of this study is that the best forecasting method of ICBP stock is single exponential smoothing with an alpa value (α = 0.7) because it has the lowest error rate and ICBP stock meets all assumptions of stationarity, cointegration, multicollinearity and IIDN.

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.

CV Wangun Mandiri is a manufacturing company that produces tapioca flour. In order to achieve maximum profit targets and smooth production activities, the company is faced with problems relating to the amount of tapioca flour products due to the uncertainty of its demand that tends to fluctuate and the imbalance of machine capacity. Therefore, it is required to plan the amount of production using the forecasting and fuzzy inference system approach as an effective method to determine the optimal production level. This research relies on the availability of datasets to determine the appropriate forecasting method and fuzzy method. The Fuzzy Mamdani method concludes that CV Wangun Mandiri can produce 82.9 tons to maximize existing demand and the capacity of its machines. 

XYZ is a manufacturing company that produces Yamaha brand motorbike parts such as electrical switches, electrical sockets, lever assembly and horns. Many of the products sent were returned by customers because they did not meet quality standards, for example in March 2024 there were 1,420 pcs out of a total of 19,900 pcs that were returned. This means that companies have to increase production time and costs to replace defective products that are returned because they do not have safety stock. Forecasting is needed to control the production system so that it does not experience over stock and safety stock shortages. This research aims to provide recommendations for forecasting methods that companies can use to minimize the occurrence of production excesses and shortages in the company. Forecasting is done based on historical company sales data for 12 months. The method used is the exponential smoothing method which is then tested whether this method can be used in the future. This calculation uses the help of POM-QM (Production Operation Management – Quantitative Method) software. Calculations are carried out by testing the MAD, MSE, and MAPE values to obtain calculation error values. The results obtained by forecasting the main sw srtg lock assembly product using the exponential smoothing method were 15,708 for demand for the next period, MAD 3.38, MSE 22.84 and MAPE value 26%. Based on these results, the exponential smoothing method can be a recommendation for companies to forecast future demand. This is because the value of forecasting accuracy or MAPE is reasonable. The smallest percentage of MAPE values has a fairly minimal possibility of error in forecasting results.

Operational management is part of the work process, implementation, organization, or overall management in an organization or company. Our country has experienced difficulties since the implementation of PSBB. This also has an impact on the economy in Indonesia. Every company will compete to be at the forefront to meet consumer needs in the market in order to survive in the midst of competition. The type of research conducted in the discussion of this article is by using literature studies or literature reviews, The data sources used in this study are secondary data which are types of data that come from documents or other secondary sources Several supporting aspects for the company, Project Management, Process Strategy, Quality Management, Forecasting Management, and Strategic Location.

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria

Every company is required to plan the need for goods as effectively as possible in order to maximize profits. Bintang Makmur Building Shop is a building shop that provides building materials, especially cement. Cement is one of the basic materials for buildings. The need for cement has recently continued to increase due to the large number of developments, both housing projects and road construction. In addition to the increasing demand for cement, cement prices also experienced price volatility which tended to fluctuate. This is done so that there is no stockpiling or even a shortage of cement. With prices that tend to go up and down if there is too much stock, it will cause losses if there is a price decrease. Vice versa if there is a shortage of cement stock, it can cause disappointment to customers. To deal with the above, it is necessary to build a prediction system that can predict cement needs in prosperous shops. The system that will be built uses an Artificial Neural Network (Artificial Neural Network) which is part of the science of artificial intelligence which has been widely used to solve various kinds of problems related to prediction or forecasting by utilizing the Backpropagation Method. The system is designed with the MATLAB programming application. From the results of the research that has been carried out, it was found that the total demand for Andalas cement for January of the following year is 0.2532 or 2532, thus the predicted total demand for Andalas cement is 2532 sacks.

MSMEs are productive economic businesses run by individuals or small business entities to grow and develop their businesses in order to build the economy, so that MSMEs become the most important pillars in the Indonesian economy. The large number of competitors in the business world requires entrepreneurs to find strategies that can increase the sales cycle and fulfill the number of requests. Solusi Cash & Kredit is a company engaged in cash and credit sales of smartphones, furniture, and electronics. According to the owner of Solusi Cash & Kredit, Vivo smartphones are one of the best-selling brands on the market. The phenomenon that occurred in the company during August 2023 to June 2024 was the occurrence of fluctuations or instability in product sales levels. The purpose of this study was to determine the results of the comparison of sales forecasting with the Exponential Smoothingi and Least Square methods. The results obtained were that the exponential smoothing alpha 0.3 method had a MAPE value of 18.27% with a forecast value for the next period of 34 units per month. Alpha 0.5 had a MAPE value of 14.74% with a forecast value for the next period of 36 units per month. Alpha 0.7 had a MAPE value of 12.64% with a forecast value for the next period of 36 units per month. Alpha 0.9 had a MAPE value of 11.7% with a forecast value for the next period of 37 units per month. The least square method had a MAPE value of 7.2% with a forecast value for the next period of 41 units per month.

This research aims to find out the planning of the Gowa Regency Regional Revenue and Expenditure Budget Report (APBD) in 2024 and 2025 using the least squares trend technique and wants to know how well the Gowa Regency Regional Financial Management Agency (BPKD) Financial Performance will be in 2024 and 2025 the future will be based on the regional financial independence ratio, the degree of decentralization ratio and the growth ratio. The type of research used is descriptive quantitative. The data processed is the Regional Revenue and Expenditure Budget Realization Report for 2020 to 2022. The data collection technique used in this research is documentation techniques. The data analysis technique is forecasting using the Trend least square formulation and the regional financial independence ratio, the degree of decentralization ratio and the growth ratio. The results of the research show that the projected regional income and expenditure budget (APBD) report for Gowa Regency in 2024 and 2025 will experience a deficit of IDR. (114,637,699,688.89) and Rp. (166,991,785,688.33). Meanwhile, the financial performance of the Gowa Regency Regional Financial Management Agency (BPKD) in 2024 and 2025 is poor, seen from the results of calculating the regional financial independence ratio, it is classified as very low and in the relationship pattern (Instructive) category, with details: in 2024 it is 19% and in 2025 will be 20%, then the decentralization degree ratio is classified as low, with details: in 2024 it will be 16% and 2025 it will be 17%, and the growth ratio is relatively low, with details: in 2024 it will be 5% and in 2025 it will be 4%.

This research discusses the application of the sales budget as a tool for planning and controlling finished goods inventory. The aim of this research was carried out based on the application of a sales budget to find out how to implement a sales budget which can be used as a planning and control tool for finished goods inventory to optimize profits at CV Wecono Asri. The type of research used in this research is descriptive quantitative, namely providing a picture of the actual situation of the object under study based on facts, by collecting data processing such as collecting related data, namely raw material costs, labor costs and factory overhead costs. The data examined in this research is from 2021 to 2022 as a basis for calculating sales forecasts in 2023 which will then be prepared for implementing the sales budget. From the results of the research that has been carried out, it shows that the implementation of the sales budget at CV Wecono Asri is more controlled compared to before the sales budget was prepared because the resulting acquisition costs also increase due to the difference between the profit and loss budget report and the profit and loss realization report, which produces results The final difference in profit and loss was IDR 16,660. So with these results it is recommended that CV Wecono Asri prepare a sales budget, production budget, raw materials budget, labor budget and factory overhead budget, the company will get a greater profit than the previous year.  

This study aimed to know the prediction of rice sales for Employee Cooperatives Republic of Indonesia Bina Warga Benjeng in the following month. Rice sales are often difficult to predict market demand. When consumer demand increases, rice supplies sometimes suffer from shortages. If consumer demand decreases, stock builds too much and results in a decrease in rice quality. In order for the rice sales process to run smoothly, it is necessary to have a sales prediction so that there are no excesses or shortage in rice supplies. The method of discussion used to predict in this study using the Single Moving Average method which is a prediction method that uses new actual data requests to raise the predictive value of the next month’s demand. The best results were using the Single Moving Average methods using rice sales data variant 25 kg variant were successfully implemented with an RMSE value of 9.3% which means this prediction accuracy of 90.7% accurate.

Increasingly advanced technology currently encourages people to make transactions using electronic money (e-money). Currently, more and more Indonesia people are using electronic money, this can be seen from the volume of electronic money transactions which is increasing from year to year. People prefer to make transactions using electronic money because it is easier to transact with traders. This research aims to analyse the variables of interest rates, inflation, money supply, e-money and electronic money transactions in Indonesia. The type of research is quantitative analysis using secondary data from 2011 to 2021 with quarterly annual data take from Bank Indonesia (BI), World Bank and BPS (Central Statistics Agency). The analysis method used is VAR (Vector Autoregressive) and refined with Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that there is a contribution to the variable itself and other variable. From the estimation results, it turns out that there is a reciprocal relationship between one variable and another variable, or in other words, all variable, namely the variable Inflation, JUB, E-Money, Interest Rate and Elecronic Money Transaction contribute to each other. The results of the FEVD analysis show that not all variables contribute to the variable itself.

The restaurant industry in Indonesia has experienced significant growth, driving the need for data-driven strategies to remain competitive. This study aims to apply and compare time series methods in forecasting sales at "Nasi Goreng Bacot" restaurant. The methods used are Simple Moving Average (SMA), Weighted Moving Average (WMA), and Single Exponential Smoothing (SES), with a focus on sales data from the year 2023.The research results indicate that SMA provides the most accurate predictions, with a Mean Absolute Error (MAE) value of 296.67, Mean Squared Error (MSE) of 129055.6, and Mean Absolute Percentage Error (MAPE) of 3.02%. WMA and SES, although useful in certain data conditions, show higher error rates in this case. This study confirms the effectiveness of SMA in the context of stable and less fluctuating restaurant sales data. With these results, restaurants can plan their inventory of raw materials and workforce more efficiently, reduce waste, and improve customer satisfaction.      

the external environment has an important influence on the sustainability of an organization. The purpose of this research is to analyze and describe the role of the external environment in educational institutions. The research method used in this research is a literature study approach, in other words, the research was conducted without a research location, but all data was taken through previous studies in the form of books and scientific articles relevant to the research theme. Data collection techniques in this study used documentation, with data analysis techniques in the form of data reduction, data presentation, and conclusion drawing. The results found that the external environment helps educational institutions to understand external factors that can affect their performance and success. SWOT is used as a tool to evaluate the strengths, weaknesses, opportunities, and threats of educational institutions. The external environment in organizations is divided into two: micro (small scope) involving competitors and partnerships, and macro (general scope) involving economic, political/legal, socio-cultural, technological, and global factors. The steps used in the analysis process involve scanning, monitoring, forecasting, and assessing.

Forecasting demand for screen-printed clothing products at the UMKM "D'mitz Screen Printing" in Sobrah Village, Wungu District, Madiun Regency helps with production control planning to maximize supply chain management for screen-printed clothing products. To predict future product demand, it is very important for UMKM to forecast market demand. Forecasting future demand is very important to avoid sales prediction errors that can cause waste, such as increased production costs due to sales predictions being too large, or stock outs due to sales predictions being too small, which results in customers having to wait longer to get the goods they want. Based on this problem, the UMKM "D'mitz Screen Printing" carried out a demand forecasting analysis for screen printed clothing with the aim of reducing waste and maximizing value. Forecasting demand for screen printed clothing for the next five months using time series analysis and moving average methods. Forecasting results for the period March 2022 to February 2023 show sequential forecasting values of 3266.67; 3300; 3250; 3283.33; 3233.33; 3316.67; 3333.33; 3372.22; 3305.56; and 3272.22. From the Mean Absolute Error (MAE) and Mean Square Error (MSE) calculations that have been carried out, the MAE value is 94.44 and the MSE value is 16018.593.

Sales forecasting is a technique that companies use to predict future sales volumes based on previous sales data. This research aims to help UMKM  XYZ determine the optimal production amount to maximize profits, by using forecasting methods in planning mocaf flour production. The methods used include the Time Series model with Moving Average, Exponential Smoothing, and Trend Analysis, which are calculated using POM QM Windows software. The analysis results show that the Trend Analysis method is the most accurate for forecasting, with the smallest error value, namely MAD of 76.997, MSE of 8161.672, and MAPE of 6.02%. The smaller the error value, the more accurate the forecasting results. Therefore, the Trend Analysis method is recommended for forecasting mocaf flour sales in XYZ UMKM in 2024, with the production of 15,100 kg to avoid excess stock and dead stock in meeting consumer demand.    

Problems that still occur in the current condition of PT MASSINDO, which is located at Jalan Gatot Subroto no 23, Block 9, Semarang City, in the process of selling springbeds, still often experience fluctuations in sales of several types and types of springbeds which are influenced by the number of goods to be sold which do not match the number of sales, thus causing a lot of losses. Therefore, it is necessary to carry out an analysis process on sales that will occur in the coming period to increase the company's sales turnover and make cash turnover more stable. The method that will be used as a consideration for companies to stabilize sales of goods is the Trend Moment method.

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.