Publication Search

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.

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 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 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.

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.

Islamic banking financing has become a crucial component of Indonesia's financial sector, providing a Sharia-compliant alternative to conventional financing. Despite its rapid growth, assessing the feasibility of Islamic banking financing remains a major challenge, particularly in terms of risk management, financial sustainability, and regulatory compliance. Previous studies have assessed financing feasibility using various methods, including the 5C approach (Character, Capacity, Capital, Collateral, and Conditions). However, research in this area remains fragmented, with a lack of systematic analysis of key trends, methodologies, and influencing factors. This study uses a Systematic Literature Review (SLR) to synthesize and analyze existing research on the feasibility of Islamic banking financing in Indonesia. The review covers studies published between 2020 and 2022, focusing on research distribution, analytical techniques, and key determinants affecting financing feasibility. The findings reveal that most studies emphasize credit risk assessment, financial literacy, and regulatory frameworks, but lack a unified approach to measuring feasibility. Furthermore, this study highlights gaps in the application of digital technologies, such as big data and machine learning, that can be used to strengthen the financing eligibility assessment system. The application of these technologies not only improves the accuracy of risk predictions but also enables Islamic banking institutions to reach more customers, particularly MSMEs and the informal sector, which have historically been underserved. The results of this study provide valuable insights for Islamic financial institutions, regulators, and researchers, highlighting the need for integrated risk assessment models, a better regulatory framework, and enhanced financial literacy initiatives to strengthen Islamic banking financing in Indonesia. This research contributes to the development of a more structured and comprehensive framework for evaluating financing eligibility, ensuring sustainable growth and financial inclusion in the Islamic banking sector.

Smartfren Telecom Tbk. is one of the telecommunications companies in Indonesia. The company has experienced losses in the last seven periods, from 2017 to 2023. It is feared that this condition will result in a high risk of a company experiencing financial distress or even bankruptcy. This research aims to find out, describe and explain the results of applying the analysis of the financial distress prediction model, namely the Altman Z”-Score model which is used to assess and predict potential bankruptcy with research objects at PT. Smartfren Telecom Tbk for the 2017-2023 period. The method used in this research is a descriptive method using a qualitative approach, and the operational variables used are independent variables, namely a bankruptcy prediction model with the dependent variable being financial ratios. The data used is secondary data in the form of PT's annual financial report. Smartfren Telecom Tbk for the 2017-2023 period. Results of financial distress analysis using the Altman Z”-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period, shows that the company is in a state of distress because the average Z"-Score value is -2.9 or Z < 1.1. This research shows that analysis of bankruptcy or financial distress using the Altman Z"-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period concluded that the company was in a state of distress.

Tropical diseases remain a serious public health challenge in Southeast Asia, particularly malaria, which has high morbidity and mortality rates. The complexity of their spread is influenced by various factors, including climate, environment, and population, requiring a spatially-based analytical approach to understand their distribution patterns. This study aims to develop a regression-based spatial model to predict the spread of tropical diseases and identify hotspots in high-risk areas. The data used include tropical disease case reports from national health agencies, climate data (temperature, rainfall, humidity) from BMKG and WorldClim, and population data (density and mobility) from  BPS and other official sources. The analysis was conducted using a Geographic Information System GIS for spatial mapping, as well as the application of spatial regression models, namely the Spatial Lag Model SLM and Spatial Error Model SEM. The results show that the developed model is able to predict disease distribution with a high level of accuracy, demonstrated by statistical validation through AIC, and Morans I. One of the main findings is the identification of malaria hotspots with a confidence level of 93, as well as the mapping of tropical disease risk predictions covering the Southeast Asian region. These results have significant implications for public health policy, particularly in resource allocation, prevention program planning, and priority area-based interventions. Furthermore, this study recommends the integration of big data and machine learning technologies to enrich predictive models and develop more adaptive early warning systems. Thus, this research contributes to strengthening tropical disease control strategies in Southeast Asia with a comprehensive spatial data-driven approach.