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Accurate sales forecasting is essential for stakeholders to make strategic decisions. This study aims to compare the performance of two deep learning models, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), in forecasting domestic motorcycle sales produced by AISI member manufacturers. The forecast is based on historical data from January 2021 to December 2024. The model was trained using time series data and the forecasting results for the period January to March 2025 were evaluated using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model produces lower MAE and MAPE values than CNN, which shows its superiority in providing more accurate and consistent predictions. On the other hand, the CNN model has lower RMSE and MSE values, thus being able to reduce large prediction errors. By comparing the results of LSTM, CNN, and actual data forecasting, the LSTM model is more suitable for forecasting motorcycle sales in Indonesia

Tobacco is one of the economic crops that has an important role in the global and national economy. In the production and selling price of tobacco is not only influenced by internal factors, such as cultivation methods and agricultural techniques and also external factors, In an effort to overcome this challenge One promising approach is the use of statistical methods and machine learning to combine data on these diverse factors. Autoregressive Integrated Moving Average (Arima) method. In 2010-2021, North Sumatra Province had 12 regencies that had the potential for tobacco production consisting of 12 years with 5 regencies in North Sumatra that had the potential to produce tobacco. Based on the forecast results, there was a significant increase in the amount of higher tobacco production in even years or it can be said to be an increase in the following 2 years. This can be a reference for producers to increase productivity in odd years to meet stable market needs.

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Financial reports are vital for investment and financial decision-making, as they reflect a company's financial performance and provide key insights for investors and institutions. However, challenges such as accounting errors, financial manipulation, and non-compliance with accounting standards can impact the quality of financial reports (QFR). Auditors play a crucial role in ensuring QFR by examining and verifying financial statements, enhancing investor confidence, financial stability, and economic growth. This study examines the role of auditors in improving QFR in commercial banks listed on the Iraq Stock Exchange. Using a descriptive analytical approach, the research reviews prior studies and analyzes data from 15 commercial banks operating in the Iraq Stock Exchange from 2015 to 2021. Hypotheses were tested using Eviews-12 software. Findings indicate that auditors influence QFR through corporate governance, particularly via the board of directors, which plays a crucial role in ensuring sound auditing practices. Board independence and management ownership significantly reduce financial manipulation, aiding informed investment decisions. The study recommends increasing awareness of the auditor’s role, strengthening corporate governance mechanisms, and enhancing financial analysts' and auditors' effectiveness in reporting and forecasting. Training and development programs are also suggested to improve financial report quality in commercial banks.

This study aims to analyze the variable contribution of the interaction of monetary policy variables (COURSE, GDP, INFLATION, CONSUMPTION and INTEREST). This study uses secondary data or time series from the first quarter of 2014 to the first quarter of 2024. The data analysis model in this study is the Structural Vector Autoregression (SVAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the SVAR analysis show that the past variable (t-1) contributes to the current variable both to itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between variables where all variables, namely monetary policy variables (GDP, INFLATION, CURRENCY, CONSUMPTION and INTEREST) contribute to each other.

This research aims to identify the nature of the cash flow statement, methods of preparing it and its indicators. Identify the nature of profitability and explain its indicators, shed light on the topic of predicting the financial distress of economic units, the causes of distress and ways to treat it, and use cash flow and profitability indicators to help predict the financial distress of Iraqi industrial companies listed on the Iraq Stock Exchange in the second and third years preceding the financial distress. The research community is represented by the industrial companies listed on the Iraq Stock Exchange, which number (21) companies until January 2023, while the study sample is the Iraqi Engineering Works Company in order to apply the current research in it. The research reached several conclusions, the most important of which was that the increase in cases of financial distress to which Iraqi industrial companies are exposed is due to the lack of instructions or directives specific to the industrial sector and the failure to use financial indicators through quantitative methods and methods to predict financial distress before it occurs, and to determine what the financial position will be in the future.

This study aims to analyze the application of Deep Neural Networks (DNN) as an artificial intelligence approach in processing weather data to support more accurate and stable climate predictions. Increasingly unpredictable and fluctuating weather patterns demand modern analytical methods capable of capturing non-linear relationships among atmospheric variables. DNN is utilized due to its ability to learn complex data structures through multilayer representations that extract deeper features from input variables. Weather data such as temperature, humidity, rainfall, air pressure, and wind speed are processed through several preprocessing stages to ensure optimal model performance. This research employs a descriptive qualitative method based on literature studies to examine the role of DNN in weather prediction systems. The findings indicate that DNN demonstrates strong generalization abilities, robustness to fluctuating data, and more stable predictive outputs compared to conventional statistical approaches. Thus, DNN is considered a promising component for the development of early warning systems and modern data-driven climate analysis, offering improved reliability in understanding and forecasting atmospheric conditions.

The forecasting of advertisement tax payments at the Medan City Revenue Agency aims to support planning and decision-making regarding advertisement tax revenue from 2021 to 2023, covering the period from January to December. In this process, historical data on advertisement tax payments is analyzed to determine the most suitable ARIMA model by considering the Autoregressive (AR), Differencing (I), and Moving Average (MA) parameters. The research indicates that the ARIMA model can provide accurate predictions of advertisement tax payment trends, thereby serving as a tool to enhance the effectiveness of local tax management. For the period from January to October 2024, it is estimated that 1,141 individuals will make advertisement tax payments, with the lowest forecasted number occurring in January 2024 at 1,128 individuals.

This study aims to determine the forecasting of stock return volatility of energy companies listed on the Indonesian Sharia Stock Index (ISSI) using the ARCH/GARCH method. This study uses purposive sampling method and uses secondary data in the form of daily stock returns from January 2022 to June 2024 on 10 selected stocks. Data processing is done using Stata software. The results showed that of the 10 selected stocks, only 6 stocks, namely BYAN, ADRO, GEMS, PTBA, AKRA, and BSSR, were suitable for analysis using the ARCH/GARCH model. Meanwhile, PGAS, ITMG, PTRO, and HRUM do not show ARCH effect or do not contain heteroscedasticity. Statistical evaluation of volatility prediction shows that the selected models provide good predictions. Among the six stocks analyzed, ADRO, PTBA, and BSSR show high volatility, while BYAN, GEMS, and AKRA show low volatility. Therefore, investors should consider investment risk when evaluating stocks with different levels of volatility.

This research compares three forecasting methods, namely Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES), in analyzing the realization of the Medan City Regional Budget (APBD) for the 2019-2024 period. This study aims to find the most accurate method in forecasting the budget, so that it can help optimize the use of APBD by local governments. The APBD realization data was analyzed using Minitab software, and the accuracy of the method was measured based on Mean Absolute Percentage Error (MAPE). The results showed that TES has the smallest MAPE value of 0.12%, compared to SES (12%) and DES (14%). Thus, TES is the best method to predict the budget realization in the following year, producing a forecasting value of 5,500.86 million rupiah. This research is expected to support the government in making more precise and efficient budget decisions.

The interest rate in the Iraqi economy represents an active and important element in the management of monetary policy in the Iraqi economy, as it is used by the monetary authority represented by the Central Bank of Iraq to influence the money supply, as well as the impact of this also by allocating the available resources for savings among foreign investments to achieve the central goal of the monetary authority of achieving stability in prices such as the interest rate and various prices and values of investments together and thus achieve balance at the economic and financial levels. This research analyzes the relationship between interest rate changes (IRC) and foreign direct investment (FDI) in the Iraqi economy during the period from (2004-2023). Multiple analytical tools were used, including descriptive statistics, correlation analysis, time series analysis, and prediction models using ARIMA and Prophet. The results showed an association between the two variables under consideration, with the ability of the ARIMA and Prophet models to provide accurate forecasts of future   FDI trends. A quantitative methodology that includes descriptive statistics, correlation analysis, time series models, and forecasting tools has been adopted to clarify the relationship between the two variables and draw conclusions that support economic decision-making.

Forecasting the number of marriages is a prediction of marriages that will occur in the future based on current and past data.  The total population of the married population is continuous, that is, its growth continues without a break. The model used for continuous population is the logistic model. This study aims to see the growth of marriage in the period 2027 using the logistic model growth. Judging from the data obtained from BPS (Central Bureau of Statistics) of North Sumatra Province from 2020 to 2023, the capacity limit (C) =  . The logistic model that can be used to parameterize the marriage rate in North Sumatra province is with a value of k = -0.25019918023 with the formula  .Based on the logistic model, the predicted marriage rate in North Sumatra province for 2027 is 64305.93339.

Iraq needs to correct public finances to achieve stability and rebuild financial reserves, by adopting a program to measure and analyze the current situation and forecast macroeconomic policies to eliminate the imbalance between domestic demand and aggregate supply, which is usually manifested in the problems of the balance of payments, high inflation, and low output growth, and financial programming is an essential tool for managing policies to achieve stability and rebuild financial reserves.Analyze the current situation and forecast macroeconomic policies to address economic imbalances. However, there is a difficulty in applying the financial programming tool because of  the lack  of accurate information systems to estimate the rate of inflation, unemployment, economic growth, exchange rate, balance of payments and the general budget, in addition to irrational fiscal policy that depends on excessive expansion of government spending, with the sovereignty and control of the public sector over the macroeconomy, compared to the weak and weak role of the private sector. As a result of the policies pursued by successive governments and thus constitute weaknesses for the application of financial programming.

This study evaluates the effectiveness of MonsoonSIM in enhancing the understanding of students in the International Trade Program for the ASEAN & China Region at Politeknik APP Jakarta regarding international trade concepts. A quantitative method was employed, using purposive sampling to select two student groups: Group A (semester 1), who had not used MonsoonSIM, and Group B (semester 3), who had experienced the use of MonsoonSIM. Data were collected through a quiz consisting of ten questions covering essential aspects of international trade, such as business management, supply chain, marketing, and demand forecasting. The results were analyzed using a paired t-test at a 5% significance level. The analysis revealed a t-value of 8.25, significantly exceeding the critical t-table value of 2.262, indicating a substantial improvement in students' understanding after using MonsoonSIM. Furthermore, semester 3 students demonstrated better comprehension compared to semester 1 students, confirming that MonsoonSIM effectively enhances not only theoretical understanding but also analytical and decision-making skills. This study concludes that MonsoonSIM is a superior simulation-based learning tool compared to traditional theoretical methods, making a vital contribution to the practical and applicable development of international trade education.

With the rising demand in the food sector, particularly for ready-made spices, CV. Peduli Pangan, which focuses on producing sachets of pepper, has a notable chance for expansion. To address this demand, accurate forecasting of production is essential for guiding choices related to production planning. Forecasting is an important tool for decision-making that underpins various manufacturing and service sectors.This research aims to estimate the ideal production quantity of pepper sachets over the next 12 periods. Regression analysis is utilized to identify the best-fitting model based on the gathered data, facilitating an exploration of how important factors affect production. The resulting regression formula shows that the production levels are impacted by the cost of raw materials (HBB), product defects (PG), and workforce (TK). The constant figure of 11,109.687 signifies the fundamental production level when these factors are not considered. If all other factors are ignored, a decrease in production volume occurs when the raw material price (X1_HBB) is -0.15 and the defect rate (X2_PG) is -0.617. On the other hand, production volume rises if the labor factor (X3_TK) is valued positively at 37.317. This forecasting model is designed to aid CV. Peduli Pangan in making informed and precise production choices.

Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

In the increasingly competitive Industry 4.0 era, companies need to forecast product demand to meet consumer needs and improve operational efficiency. CV Mamifood Sukses Abadi, an MSME that produces milk and cheese-based foods, has faced sales fluctuations in the last two years, thus requiring accurate forecasting to plan production strategies and resource management. This research aims to forecast demand using the Fuzzy Mamdani method and the POM-QM application. Fuzzy Mamdani was chosen for its ability to handle decision-making with multiple criteria and balanced weights, while POM-QM was used to validate predictions through quantitative methods. Product sales data for the years 2022 and 2023 were analyzed to produce accurate forecasts. The methods used include Moving Average for forecasting and evaluation of the results using MAPE. The analysis results show that the Moving Average method with N = 2 produces a MAD value of 402.523 and a MAPE of 22.155%, while the results of Fuzzy Mamdani show that product demand in the next period tends to decrease. This research is expected to provide insight for CV Mamifood Sukses Abadi in planning a more efficient production strategy.

This study aims to determine the effect of fintech, the amount of money in circulation, interest rates and economic growth on the analysis of digital economic trends on monetary policy in Indonesia. There are four variables in this study, namely fintech, the amount of money in circulation, interest rates and economic growth. The analysis method used is Vector Autoregression with the Impluse Response Function test or abbreviated as IRF and the Forecast Error Variance Decomposition test commonly abbreviated as FEVD, stationarity test, cointegration test, lag structure stability test and optimal lag length test. There is a contribution from each variable to the variable itself and other variables, according to the results of the Vector Autoregression study with a lag basis of 2. In addition, the results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the current variable both to the variable itself and to other variables. The results of the analysis show that there is a reciprocal relationship between the variables. By using response function analysis, we can see if there is a response from other variables to changes in one variable in the short, medium, or long term. In addition, we know that the stability of all variables is formed in the short, medium, and long term. According to the Variance Decomposition Analysis, factors such as Fintech and Money Supply contribute the most to the variable itself. However, other variables that have the greatest influence on the variable itself and are supported by other variables in the short, medium, and long term are economic growth and interest rates are most influenced by Fintech.

This community service activity aims to increase the understanding and ability of business groups in Kassi Village, Rumbia District, Jeneponto Regency in making effective business projections. This program is expected to provide strategic provision for business actors in managing finances, planning business development, and facing market challenges. The technique of implementing this activity includes three stages starting from the preparation stage, the implementation stage and the activity evaluation stage. The results of the training showed that traditional business owners have difficulty in applying business forecasting techniques to their businesses because sales records are sometimes incomplete or non-existent. After conducting the simulation, participants were able to understand the basis of sales forecasting for future sales forecasts. In addition, business owners can know their business forecast with certainty. Knowledge of business forecasting is expected to improve business performance and competitiveness. The limitation of this training is that the implementation method does not use instructional techniques so that knowledge about business forecasting can be implemented in business activities.

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.