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This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

The increasing intensity of business competition requires companies to maintain strong financial conditions to avoid financial distress that may disrupt business continuity. This study aims to assess the financial stability and predict the potential bankruptcy of PT Sido Muncul Tbk for the 2022–2024 period using the Altman Z-Score model. A descriptive quantitative approach was applied, utilizing secondary data obtained from annual reports published by the Indonesia Stock Exchange and the company’s official website. Five key ratios in the Altman model were used as indicators to evaluate the company’s financial position and resilience. The results show Z-Score values of 4.74 in 2022, decreasing slightly to 4.66 in 2023, and rising again to 4.79 in 2024. These scores are significantly above the safe threshold of 2.675, indicating that the company is in a healthy financial state with a very low risk of bankruptcy. Overall, PT Sido Muncul Tbk demonstrates stable financial performance, supported by a strong capital structure and consistent operational results. The Altman Z-Score model also proves to be an effective early-warning tool for identifying potential financial problems.

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.

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.

Mental health has become a major global issue, particularly after the COVID-19 pandemic, which significantly increased the prevalence of psychological disorders. Early detection of stress and other mental health problems remains a major challenge, as traditional methods are generally subjective and unable to provide real-time results. This study aims to design and test a wearable sensor based on Heart Rate Variability (HRV) as a physiological indicator for detecting stress levels. The research employed an experimental approach through the development of a wearable sensor prototype equipped with a stress detection algorithm based on HRV analysis, including both time-domain and frequency-domain parameters. The prototype was tested on 100 respondents with varying stress levels under controlled conditions. Instruments used in this study included the HRV sensor prototype, psychological questionnaires, and standard validation devices. Data were analyzed by comparing the sensor detection results with respondents’ psychological data and calculating prediction accuracy. The findings showed that the wearable sensor was able to predict stress conditions with an accuracy rate of 80%. The distribution of sensor detection results was generally consistent with psychological data, especially in the low-stress category, although slight deviations were observed in moderate and high-stress categories. These results demonstrate that an HRV-based wearable sensor can serve as a practical and non-invasive tool to monitor mental conditions in real time. The implications of this research highlight the potential of wearable technology as an innovative solution for mental health monitoring, both for individual use and as support for healthcare systems. Therefore, this study contributes to the development of adaptive and responsive health technologies in addressing global mental health challenges.

This study aims to compare and analyze the accuracy levels of four financial distress prediction models—Altman Z-Score, Springate, Grover, and Zmijewski—in anticipating the potential bankruptcy of companies subjected to delisting from the Indonesian Stock Exchange (IDX). The delisting phenomenon, which is strongly linked to severe financial deterioration, provided the core motivation for identifying the most reliable predictive instrument, utilizing secondary data from the annual financial reports of delisted companies during the 2019-2023 observation period. Descriptive analysis techniques were employed to calculate the accuracy rate and Type Error for each model. The comparative results consistently indicate that the Springate Model is the most effective, consistent, and accurate model for predicting financial distress in delisted firms, achieving an accuracy rate of 89% in both the first and second years prior to delisting, while the Altman Z-Score model exhibited lower accuracy (68.75% and 62.50%). This key finding emphasizes the superiority of the Springate Model as a crucial diagnostic tool for investors and regulatory bodies in assessing corporate bankruptcy risk.

This study aims to analyze the prediction of financial distress using the Altman Z-Score, Springate, and Zmijewski methods at PT Sri Rejeki Isman Tbk in 2019-2023. This type of research is descriptive research with a quantitative approach. Using secondary data with documentation techniques and literature studies in the form of related company financial reports, books, articles, journals and other publications related to the research topic. The sampling technique was carried out using a purposive sampling method. The sample in this study was obtained using a purposive sampling technique and obtained as many as 5 financial reports from the company PT Sri Rejeki Isman Tbk for the period 2019-2023. The results of the study show that the results of calculations using the Altman Z-Score method indicate that in 2019-2023 PT Sri Rejeki Isman Tbk experienced fluctuations in the company consistently still in the category of bankruptcy, the Springate method shows that the company experienced a decline in its financial performance, and the Zmijewski method shows that companies that experience fluctuations in financial performance conditions, Although there are fluctuations in the X-Score value and improvements in certain years.

Indonesia is the world's leading producer of spices, but it still faces challenges in manual visual quality assessment, which is inconsistent. This study aims to develop a spice quality classification system using a Deep Learning approach based on Convolutional Neural Networks (CNN). Data was collected through digital images of five types of spices (cloves, cardamom, cinnamon, pepper, and nutmeg) classified into two categories: good and bad. The dataset was then processed and used to Train the CNN model using Tensorflow. The model architecture consists of several convolution, pooling, and dense layers, and is integrated into a web-based prototype application using Streamlit. Evaluation results show that the model achieves high Accuracy of 98.86% (Training), 98.45% (Validation), and 98.45% (Testing). The prototype application can provide automatic Predictions of spice quality through a simple and responsive interface. The results of this study indicate that CNN is effective in identifying the visual quality of spices and can serve as an objective, efficient technological solution that supports the enhancement of Indonesia's spice export competitiveness.

This research is motivated by significant fluctuations and a decline in profits for companies in the clothing and luxury goods subsector listed on the IDX for the period 2021-2023, indicating potential financial difficulties. This research aims to evaluate the company's condition using four prediction models, namely Altman-ZScore, Springate, Fulmer, and Taffler, and to determine the most suitable model for the sector. The method applied is quantitative comparative with a purposive sample of 11 companies, using financial statement data from 2021-2023 and analyzed using the formulas for each predictive model. The research findings indicate that the four models produce different predictions regarding the company's financial condition. Additionally, there are differences in the accuracy levels of the four models. The Springate and Taffler models achieved the highest accuracy rate of 85%, followed by Altman at 67% and Fulmer at 64%. The findings of this study confirm that the Springate model is the most reliable tool for early warning for companies and stakeholders, enabling faster preventive measures to prevent bankruptcy.

The rapid development of the smart city concept encourages the need for energy management that is more efficient, sustainable and adaptive to the needs of modern urban communities. In this context, renewable energy is the main solution to reduce dependence on fossil energy sources that are limited and pollute the environment. This research aims to optimize the utilization of renewable energy in smart cities by integrating Big Data technology and Decision Support Systems (DSS). The approach used in this research is a case study and system modeling method, which involves collecting energy data from various sources such as IoT sensors, weather stations, and energy distribution systems in real-time. The data is then analyzed using Big Data Analytics techniques to identify energy consumption patterns, potential renewable energy production, and peak load predictions. Furthermore, a decision support system was designed to assist policy makers and city managers in determining optimal energy distribution and usage strategies based on the available data and simulations. The results show that the integration of Big Data and DSS is able to increase the efficiency of renewable energy utilization up to 25% compared to conventional systems. In addition, the system is also able to dynamically respond to changing conditions and provide more accurate and adaptive decision recommendations. These findings indicate that the synergy between data technology and decision support systems plays a strategic role in creating sustainable and environmentally sound smart cities.

Bankruptcy is a condition where a business cannot operate effectively due to severe financial difficulties it is currently experiencing.  This research purposes to analyze the ratio of the Altman, Springate, and Grover models in analyzing bankruptcy in food and beverage corporations listed on the IDX with reference to signaling theory. The data analysis approach used are One Way Anova test and accuracy level test. Using 20 company samples with purposive sampling method. The final proceeds of the research explain that there are significant differences between the Altman and Springate models, significant differences between the Altman and Grover models, and no significant differences between the Springate and Grover models in predicting bankruptcy in food and beverage companies for the period 2019-2023. The very accurate prediction model was achieved by the Grover model.

Financial distress refers to a company’s persistent inability to meet financial obligations, signaling severe monetary strain that precedes formal bankruptcy or liquidation proceedings. This study investigates the impact of intellectual capital (VAICTM), operational capacity (TATO), capital structure (DER), and operating cash flow (OCF) on financial distress (Altman Z-Score), with profitability (ROA) serving as a mediating variable. The theoretical framework of this research is grounded in signaling theory, agency theory, and resource-based view theory. The study focuses on basic materials companies listed on the Indonesia Stock Exchange (IDX) between 2019 and 2023. The study utilized criterion-based sampling to select qualified respondents. Secondary datasets were analyzed through panel regression and path analysis, with Eviews 12 as the computational tool. Key findings include: (1) intellectual capital and operating capacity demonstrate a statistically significant positive influence on profitability; (2) capital structure exerts a significant adverse impact on profitability; (3) operating cash flow exhibits no statistically discernible impact on profitability; (4) both operating cash flow and profitability are positively and significantly associated with increased financial distress; (5) capital structure displays a significant inverse relationship with financial distress severity; (6) intellectual capital and operating capacity show no statistically significant associations with direct financial distress prediction; (7) profitability partially mediates the influence of intellectual capital, operating capacity, and capital structure on financial distress; and (8) profitability does not serve as a mediating variable between operating cash flow and financial distress.

This study examines the factors that influence financial distress in companies affiliated with Israel, focusing on the roles of profitability, liquidity, leverage, sales growth, and firm size. The research is driven by the phenomenon of boycotts caused by geopolitical conflicts involving Israel, which have impacted the financial performance of several companies, particularly in Indonesia. The study uses a quantitative approach, analyzing a sample of companies listed on the Indonesia Stock Exchange (IDX) that are affiliated with Israel during the 2023-2024 period. The data consists of quarterly financial statements, which are analyzed using the Altman Z-Score bankruptcy prediction model. The findings show that profitability and liquidity have a significant effect on financial distress, while leverage and sales growth have a smaller impact. Firm size is also found to reduce the risk of financial distress. These results suggest that companies linked to Israel are more vulnerable to financial risks due to boycotts triggered by international political tensions.

Econometrics is a discipline in economics that combines economic theory, mathematics, and statistics to quantitatively assess economic phenomena. This paper aims to introduce econometrics as an important tool in economic analysis and explain its applications in various sectors such as macroeconomics, microeconomics, development, and finance. Using a descriptive qualitative research method based on literature review, this paper explains how econometrics is used to test economic conjectures, make predictions, and support data-driven decisions. In addition, this article also discusses the advantages of econometrics, the challenges that arise in its application, and the software used in econometric analysis, including Excel, SPSS, and EViews. The findings show that econometrics is useful not only for academics but also for decision makers and business actors in designing more efficient economic strategies and policies. However, several problems such as data limitations, model assumptions, and specification errors are still challenges that need to be overcome by increasing capabilities and utilizing technology. It is hoped that this paper can broaden understanding and encourage more effective and appropriate use of econometrics in Indonesia.

This qualitative literature review explores the transformative role of generative artificial intelligence (GenAI) in reshaping organizational problem-solving. Moving beyond prediction, GenAI supports ideation, design, and decision-making by enhancing exploration, reducing cognitive constraints, and enabling hybrid human-machine intelligence. Drawing on recent studies in strategic management, organizational learning, and AI innovation, this review synthesizes evidence of GenAI’s capacity to augment creativity, frame redefinition, and solution diversity. The findings highlight both opportunities—such as improved search efficiency and strategic adaptability—and challenges, including algorithmic opacity, trust issues, and socio-technical complexity. Ultimately, GenAI represents a generative shift in how organizations define problems and pursue innovation, requiring thoughtful integration to maximize its cognitive and strategic value

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.

This qualitative literature review explores the advances and challenges in predicting SME failures, focusing on methodological trends, data imbalance solutions, and model validation practices. Over recent years, machine learning techniques have gained prominence, replacing traditional statistical models and improving predictive accuracy. Key strategies for overcoming data imbalance, such as Synthetic Minority Over-sampling Technique (SMOTE) and cost-sensitive learning, have also been highlighted. However, challenges persist, particularly in model interpretability, generalization, and overfitting. The review emphasizes the need for continuous refinement of predictive models and validation practices to ensure real-world applicability. The findings suggest that while considerable progress has been made, future research should aim to enhance model transparency and address limitations in data representation to improve SME failure prediction across diverse contexts.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

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.