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All Journal Ekonomikalia Journal of Economics Infolitika Journal of Data Science Indatu Journal of Management and Accounting
Afjal, Mohd
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Social Sciences Other

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A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Afjal, Mohd; Ray, Samrat
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.199

Abstract

Customer churn is critical for businesses across various industries, especially in the telecommunications sector, where high churn rates can significantly impact revenue and growth. Understanding the factors leading to customer churn is essential for developing effective retention strategies. Despite the predictive power of machine learning models, there is a growing demand for model interpretability to ensure trust and transparency in decision-making processes. This study addresses this gap by applying advanced machine learning models, specifically Naïve Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, to predict customer churn in a telecommunications dataset. We enhanced model interpretability using SHapley Additive exPlanations (SHAP), which provides insights into feature contributions to predictions. Here, we show that LightGBM achieved the highest performance among the models, with an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis revealed that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These results indicate that combining predictive analytics with interpretability methods can provide telecom companies with actionable insights to tailor retention strategies effectively. The study highlights the importance of understanding customer behavior through transparent and accurate models, paving the way for improved customer satisfaction and loyalty. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to enhance churn prediction models' predictive power and applicability.
The Impact of Credit Access on Economic Growth in SEA Countries Idroes, Ghalieb Mutig; Maulidar, Putri; Marsellindo, Rio; Afjal, Mohd; Hardi, Irsan
Indatu Journal of Management and Accounting Vol. 2 No. 2 (2024): December 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v2i2.256

Abstract

Access to credit serves as a vital catalyst for economic growth, allowing individuals, enterprises, and governments to fund investments, maintain consumption stability, and encourage productive endeavors. Economic growth is fundamental to sustainable development, enhancing living standards, and promoting innovation. This study investigates the impact of credit access on economic growth in Southeast Asia (SEA) countries using monthly data from 2014 to 2020. By applying the Fully Modified Ordinary Least Squares (FMOLS) method, along with robustness checks using the Dynamic Ordinary Least Squares (DOLS) technique, this study includes essential control variables such as capital, labor, and technology. The results reveal that credit access has a positive impact on economic growth, while capital and technology also contribute positively to economic growth. Conversely, labor shows a negative impact on economic growth within the region. These results are consistent across both the FMOLS and DOLS analyses. Based on these findings, Southeast Asian policymakers ought to facilitate credit accessibility by making loan applications more straightforward, minimizing bureaucratic obstacles, and providing lower interest rates, especially for small enterprises and marginalized communities. Moreover, encouraging financial institutions to lend more liberally and utilizing digital platforms can expand access. Additionally, investing in technology, improving capital formation, and tackling labor market challenges will more effectively align with the region's growth path.
General Equilibrium Model Applications in Energy Research: A Bibliometric Analysis Agustina, Maulidar; Thahira, Zia; Zikra, Naswatun; Amalina, Faizah; Afjal, Mohd; Idroes, Ghalieb Mutig
Ekonomikalia Journal of Economics Vol. 3 No. 1 (2025): April 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v3i1.291

Abstract

This study investigates the scholarly landscape of General Equilibrium (GE) model applications within the field of energy research through a bibliometric lens. Utilizing a dataset of 864 journal articles indexed in Scopus from 1974 to 2022, the research maps publication trends, identifies leading contributors, and uncovers prevailing thematic clusters within the field. The analysis employs VOSviewer to visualize co-authorship networks, as well as institutional and country-level productivity, source relevance, and keyword co-occurrence patterns. Results reveal that China, the United States, and Japan are the most prolific countries, while Energy Policy and Energy Economics emerge as the most influential journals. Among the authors, Masui T. stands out as the most productive, while Paganetti registers the highest number of citations, reflecting a significant scholarly impact over recent years. Keyword mapping highlights dominant research themes centered on "computable general equilibrium analysis," "computable general equilibrium model," and "emission control," reflecting the field’s alignment with climate-related energy policy evaluation. This bibliometric overview not only provides a structured understanding of intellectual developments in GE-energy research but also identifies underexplored areas that warrant further investigation—particularly the integration of GE models with renewable energy transitions in developing economies and the incorporation of behavioral and distributional dimensions within energy policy assessments. The study contributes to the advancement of interdisciplinary dialogue by informing future research directions and supporting evidence-based policymaking in the energy-climate nexus.
Co-Authors Agustina, Maulidar Amalina, Faizah Hardi, Irsan Idroes, Ghalieb Mutig Marsellindo, Rio Maulidar, Putri Ray, Samrat Teuku Rizky Noviandy Thahira, Zia Zikra, Naswatun