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This study aims to estimate changes in the Indonesian Sharia Stock Index (ISSI) from 2020 to 2025 using the Autoregressive Integrated Moving Average (ARIMA) model. The growth of the Islamic stock market in Indonesia has increased rapidly, driven by public awareness of investments that follow sharia principles, as well as changes in macro and microeconomic conditions, especially during the COVID-19 pandemic which has had a significant impact on the financial market. This study relies on monthly ISSI data taken from official sources and analyzed with a quantitative approach using the time series method using EViews version 13 software. Statistical analysis and stationarity tests indicate that the ISSI data exhibits an increasing trend pattern and quite high volatility, so that a differentiation process is necessary to achieve stationarity. Based on the results of model testing and the selection of optimal information criteria, the ARIMA (1,1,1) model was selected as the most appropriate to capture the autocorrelation pattern and produce accurate short-term predictions. Projections indicate a stable growth trend until the end of 2025, with an estimated index of more than 8.3 million. The findings of this study indicate that the ARIMA model is an effective tool for forecasting ISSI movements and can be a strategic consideration for investors, financial institutions, and policymakers in developing sustainable investment strategies in the Indonesian Islamic stock market.

This study aims to analyze and project consumer prices of cabbage commodities at four levels: Ngawi Regency, Pacitan Regency, East Java Province, and nationally, using the additive Holt–Winters forecasting model. Monthly price data for the period January 2020–December 2024 were used to capture the dynamics of levels, trends, and seasonal patterns that affect price fluctuations. Model performance was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) indicators. The results showed differences in model accuracy between regions. East Java Province produced the best performance with the lowest MAE and RMSE values, indicating a more stable price pattern that was easier for the model to capture. In contrast, Ngawi Regency showed the highest volatility, resulting in greater forecasting errors. Pacitan Regency displayed a relatively consistent seasonal pattern with moderate accuracy, while national data showed smoother fluctuations due to the aggregation effect. Overall, the additive Holt–Winters model is effective for short-term projections in regions with low to moderate variability, but is less optimal in regions with highly volatile price dynamics.

Rubber prices experience significant and prolonged fluctuations, which impact farmer incomes and management decisions. Understanding historical patterns and price predictions is considered crucial for production planning, marketing, and farmer protection policies. This study aims to identify the characteristics of rubber price time series in Lahat Regency and develop a reliable forecasting model to support short- to medium-term decision-making. This study uses secondary data on monthly average producer prices for the period January 2019–December 2023. The analysis includes the Augmented Dickey–Fuller stationarity test to determine the need for transformation, differencing, and/or logarithmic transformation when necessary, identification of autocorrelation patterns using ACF/PACF, model estimation on the processed data, and evaluation of residual diagnostics (Ljung–Box, normality test) and forecasting accuracy metrics (RMSE, MAE, MAPE, Theil). The level data shows non-stationarity and becomes stationary after the first differencing; The model on log-transformed data had significant parameters and higher explanatory power than the model on de-differenced data, with RMSE and MAPE values ​​within a reasonable range. Forecast confidence intervals widened at longer time horizons, indicating increased projection uncertainty. Conclusion: Validated forecasts can inform farmers and policymakers to manage price risk and design market interventions.

This study aims to forecast beef prices in Palembang City and at the national level in Indonesia using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are the monthly average beef prices for the period January 2019 to December 2024. The analysis involves stationarity tests using Augmented Dickey-Fuller (ADF), model identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, parameter estimation with Maximum Likelihood Estimation (MLE), and residual diagnostics with the Ljung-Box and Jarque-Bera tests. The results show that beef prices at both regional levels are not stationary at the level but become stationary after the first differencing (I(1)). The best ARIMA models obtained are ARIMA(0,1,1) for Palembang City and ARIMA(1,1,0) for the national level. Both models successfully predict price fluctuations with a low error rate and show a moderate price increase trend. These findings provide practical implications for price stabilization policy making and beef-related business planning. The forecast results state that beef prices in Palembang City and nationally are predicted to tend to rise in 2025 from January to December.  

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

Effective management of fixed assets plays a crucial role in maintaining the reliability and transparency of a company’s financial reporting. Errors in the capitalization process can lead to misstatements in financial statements and affect investment decisions. This study aims to analyze and forecast asset capitalization trends using the Autoregressive Integrated Moving Average (ARIMA) model. The research utilizes monthly recap data of asset capitalization recorded during the Settlement to Fixed Asset process from January 2021 to August 2025. The data were processed through several stages, including stationarity testing, model identification, parameter estimation, and model accuracy evaluation. The findings indicate that the data are stationary without differencing (d = 0). From several candidate models, ARIMA(0,0,3) was identified as the best model based on the lowest AIC value of 39.76. The selected model was then applied to predict asset capitalization values for the next ten periods, resulting in forecasts ranging from 1.12 to 1.56 trillion rupiah. Model evaluation showed a MAPE of 29.01%, which implies a moderate forecasting accuracy. Consequently, the ARIMA model can be considered a suitable analytical tool for monitoring and forecasting asset capitalization quantitatively.

This study aims to analyze investment efficiency in Bandung Regency from 2011 to 2024 and project it for the years 2025 to 2030. Investment efficiency is measured using the Incremental Capital-Output Ratio (ICOR) based on data from Gross Regional Domestic Product (PDRB) and Gross Fixed Capital Formation (PMTB) at constant 2010 prices. Forecasting is performed using the Autoregressive Integrated Moving Average (ARIMA) model. The analysis results show fluctuating ICOR values, reflecting annual variations in investment efficiency. Projections for 2025–2030 indicate a potential decline in efficiency, which signals important considerations for regional development planning. The findings highlight the need for the Investment and Integrated One-Stop Service Office (DPMPTSP) to use ICOR as a key performance indicator when formulating more effective and efficient investment policies to support quality economic growth in Bandung Regency. This study recommends improving future investment policies by utilizing the ICOR indicator to monitor and evaluate the effectiveness of regional investments.

This study analyzes the dynamics of Non-Performing Loans (NPLs) in the Indonesian banking sector by examining both internal and external factors affecting financial stability. The variables included in the research are NPL, Loan to Deposit Ratio (LDR), lending interest rate, inflation, Household Debt to Income (HDTI), fintech lending, and Capital Adequacy Ratio (CAR). Using annual secondary data from 2005 to 2024, sourced from the World Bank and Statistics Indonesia (BPS), the study employs a Vector Autoregression (VAR) method. This method includes stationarity tests, optimal lag selection, cointegration tests, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The results show that most variables demonstrate a dominant contribution from their own shocks, although interactions between variables remain significant. The IRF analysis reveals that CAR and HDTI are relatively stable and quickly return to equilibrium, while fintech lending, inflation, and NPLs show more volatile responses, making them more susceptible to external shocks. LDR and lending interest rates are sensitive in the short term but tend to stabilize over the long run. FEVD further indicates that inflation plays a significant role in driving NPL variations, while fintech lending is closely associated with CAR in the long term. The study concludes that the stability of Indonesia’s banking sector is influenced by both internal factors like CAR and LDR, as well as external factors such as inflation, fintech lending, and household debt. Thus, a coordinated approach involving monetary policy, macroprudential measures, and financial supervision is crucial to enhance the resilience of the banking sector against global and domestic economic shifts.

This study explores employment development strategies aimed at bolstering economic growth in North Sumatra Province using the Vector Autoregression (VAR) model and an eighteen-year time series dataset. The variables analyzed include the Human Development Index (HDI), total population, Gross Regional Domestic Product (GRDP), Labor Force Participation Rate (LFPR), and Open Unemployment Rate (OUR). The estimation results reveal dynamic interrelationships among these variables over short, medium, and long-term periods. The VAR analysis with a lag of 2 illustrates how each variable contributes to both itself and the other variables. It also shows that past variables (t-1) significantly impact current variables. Furthermore, the response function analysis identifies how a change in one variable is responded to by others across different time horizons. Stability analysis confirms that all variables maintain medium-to-long-term stability over a five-year period. The Forecast Error Variance Decomposition (FEVD) highlights HDI, population, and GRDP as the most influential variables in shaping the employment system and economic development overall. The VAR model used meets the stability test criteria, making the findings a reliable basis for policy research.

The research entitled Production Planning of Tenun Ikat Petra Cilik in Kupang City aims to find out and explain the production planning of Tenun Ikat Petra Cilik in Kupang City. Data collection techniques in this study are observation, interviews, documentation and questionnaires. While data analysis techniques use forecasting and Break Event Point (BEP).The results showed that the amount of sales forecast for sarongs at Tenun Ikat Petra Cilik in 2024 was 174 sheets, in 2025 as many as 202 sheets and 2026 as many as 219 sheets. For blankets in 2024 as many as 107 pieces, in 2025 as many as 110 pieces and in 2026 as many as 113 pieces. For sashes on Tenun Ikat Petra Cilik shows that in 2024 there were 199 sheets, in 2025 there were 201 sheets and in 2026 there were 204 sheets. The results of the Break Event Point (BEP) analysis show that if Tenun Ikat Petra Cilik in Kupang City produces 101 pieces of sarong or Rp.152,000,000, for blankets producing 162 pieces or Rp. 162,857,142 and sling producing 1,380 pieces or Rp.411.940.298, then Tenun Ikat Petra Cilik will not make a profit or not suffer a loss because at that point Tenun Ikat Petra Cilik is in a state of basic return. And if the company produces below the BEP point, the company will experience a loss, and vice versa if the company produces above the BEP point, the company will experience a profit. Based on the results of the study, it is recommended that it be taken into consideration for the company in relation to making decisions on determining the number of orders and good planning for the supply of woven raw materials in order to smooth the production process in the company. And for the company, Tenun Ikat Petra Cilik needs to make a production plan or the amount of production to be produced appropriately in order to provide maximum profit. Produksi Keywords :Planning,Production    

This research is a case study with the object of research at UMKM Liwut Sari The purpose of this study is to determine and explain the production planning of herbal drinks at UMKM Liwut Sari. Data collection techniques in this research are observation, interviews, and documentation while data analysis techniques use forecasting and Break Even Point (BEP).  The results of the sales forecasting analysis of red ginger herbal drink and temulawak herbal drink at Liwut Sari UMKM in January are predicted to sell 263 packs of red ginger herbal drinks and 262 packs of temulawak herbal drinks, in February red ginger herbal drinks is 271 packs and temulawak herbal drinks is 270 packs, in March red ginger herbal drinks is 279 packs and temulawak herbal drinks is 278 packs. The results of the Break Even Point (BEP) analysis show that if UMKM Liwut Sari produces 86 packs of red ginger herbal drinks or Rp 4,289,855 and 81 packs of temulawak herbal drinks or Rp 4,054,794, then UMKM Liwut Sari does not make a profit or suffers a loss because at that point the company is in a state of principal return.

The development of the digital ecosystem has disrupted the transportation sector. Traditional transportation businesses have shifted to online transportation. This study aims to analyze Blue Bird's strategy in facing the ride-hailing disruption in Indonesia. The research was conducted using a descriptive qualitative approach. The data was sourced from digital searches and observations. The results show that the digital transformation implemented by PT Blue Bird Tbk has improved operational efficiency and competitiveness in the highly competitive transportation market. The My Blue Bird application, with real-time tracking and cashless payment features, has streamlined the booking process and strengthened customer loyalty. The data indicates an increase in app usage and a reduction in operational costs, supporting the effectiveness of the company's digital strategy. Strategic collaboration with ride-hailing platforms has also significantly contributed to market expansion and increased fleet occupancy. The success of this strategy is reflected in the rise in booking volume and overall customer satisfaction. As a further step that has not been fully implemented, it is recommended that Blue Bird explore the application of AI-based predictive models to optimize fleet scheduling and route dynamics. The use of this technology can provide more accurate demand forecasts and support strategic decision-making in resource allocation. Additionally, the development of a customer feedback system integrated with digital analytics will allow the company to respond to consumer trends and preferences more effectively. These measures, supported by enhanced digital infrastructure and cross-sector collaboration, are expected to further boost Blue Bird's efficiency and growth in the digital disruption era.

This study aims to analyze the influence of the Islamic financial sector on economic growth in Indonesia during the period 2014–2022. The Islamic financial components examined include Islamic stocks, sukuk (Islamic bonds), Islamic mutual funds, third-party funds from Islamic banking, and assets of Islamic non-bank financial institutions (IKNB). Economic growth is measured using Gross Domestic Product (GDP) as the dependent variable. The analysis employs a quantitative approach using the Vector Error Correction Model (VECM), complemented by Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to assess both short-term and long-term relationships. The results reveal that Islamic stocks and sukuk have a significant and positive effect on GDP in both the short and long term. Third-party funds from Islamic banks also contribute positively in the long run, although their short-run impact is insignificant. Conversely, Islamic mutual funds and IKNB assets show no statistically significant influence on economic growth. These findings highlight the strategic importance of strengthening Islamic capital market instruments and improving financial intermediation to foster sustainable economic development in Indonesia.

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 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.

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.

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.