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All Journal Journal of Computing Theories and Applications Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Indatu Journal of Management and Accounting The International Journal of Financial Systems Grimsa Journal of Business and Economics Studies Journal of Future Artificial Intelligence and Technologies
Idroes, Ghalieb Mutig
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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An Interpretable Machine Learning Strategy for Antimalarial Drug Discovery with LightGBM and SHAP Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-16

Abstract

Malaria continues to pose a significant global health threat, and the emergence of drug-resistant malaria exacerbates the challenge, underscoring the urgent need for new antimalarial drugs. While several machine learning algorithms have been applied to quantitative structure-activity relationship (QSAR) modeling for antimalarial compounds, there remains a need for more interpretable models that can provide insights into the underlying mechanisms of drug action, facilitating the rational design of new compounds. This study develops a QSAR model using Light Gradient Boosting Machine (LightGBM). The model is integrated with SHapley Additive exPlanations (SHAP) to enhance interpretability. The LightGBM model demonstrated superior performance in predicting antimalarial activity, with an ac-curacy of 86%, precision of 85%, sensitivity of 81%, specificity of 89%, and an F1-score of 83%. SHAP analysis identified key molecular descriptors such as maxdO and GATS2m as significant contributors to antimalarial activity. The integration of LightGBM with SHAP not only enhances the predictive ac-curacy of the QSAR model but also provides valuable insights into the importance of features, aiding in the rational design of new antimalarial drugs. This approach bridges the gap between model accuracy and interpretability, offering a robust framework for efficient and effective drug discovery against drug-resistant malaria strains.
Energy Poverty and Environmental Quality Nexus: Empirical Evidence from Selected South Asian Countries Sikdar, Asaduzzaman; Bani, Nor Yasmin binti Mhd; Salimullah, Abul Hasnat Muhammed; Majumder, Shapan Chandra; Idroes, Ghalieb Mutig; Hardi, Irsan
Ekonomikalia Journal of Economics Vol. 2 No. 2 (2024): October 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v2i2.221

Abstract

South Asian countries are included in the economies of developing Asia. The region of South Asia is predominantly affected by energy poverty issues due to a heavy reliance on conventional energy and unpredictable access to energy services. It has about a quarter of the world's population and is home to three of the world's ten most populated countries: India, Pakistan, and Bangladesh. This study investigates environmental sustainability dynamics in South Asian countries from 2000 to 2021, utilizing the Cross-sectional Autoregressive Distributed Lag (CS-ARDL) and Dumitrescu-Hurlin (D-H) causality methods. The research offers insights into the long-term trends and causal relationships that shape environmental outcomes in South Asian nations. Based on empirical findings, in the long-term, it is revealed that increases in energy poverty, economic growth, income inequality, and capital formation raise greenhouse gas (GHG) emissions, while renewable energy and labor reduce GHG emissions. On the other hand, the error correction term shows the speed of adjustment toward equilibrium at 0.75%. Furthermore, the D-H panel causality reveals a directional link between variables. These findings highlight the urgent need for South Asian countries to implement policies to address energy poverty, promote renewable energy adoption, and reduce income inequality to mitigate GHG emissions and achieve long-term environmental sustainability effectively.
Forecasting Bank Stock Trends Using Artificial Intelligence: A Deep Dive into the Neural Prophet Approach Noviandy, Teuku Rizky; Hardi, Irsan; Idroes, Ghalieb Mutig
The International Journal of Financial Systems Vol. 2 No. 1 (2024)
Publisher : Otoritas Jasa Keuangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61459/ijfs.v2i1.41

Abstract

This research aims to use Neural Prophet, a deep learning tool, to predict stock prices in the banking sector with high accuracy and useful insights. The model's capability in managing intricate temporal patterns differentiates it, garnering attention from researchers. The significance of this research lies in its potential to enhance stock price prediction precision, especially in the context of banking stocks, offering stakeholders’ deeper insights. The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance. Accurate predictions benefit risk management, facilitating well-informed investment choices in dynamic markets.
Long-Term Impact of Dirty and Clean Energy on Indonesia’s Economic Growth: Before and During the COVID-19 Pandemic Ringga, Edi Saputra; Hafizah, Iffah; Idroes, Ghifari Maulana; Amalina, Faizah; Kadri, Mirzatul; Idroes, Ghalieb Mutig; Noviandy, Teuku Rizky; Hardi, Irsan
Grimsa Journal of Business and Economics Studies Vol. 2 No. 1 (2025): January 2025
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjbes.v2i1.49

Abstract

Dirty (non-renewable) energy, considered environmentally harmful due to greenhouse gas emissions, is contrasted with clean (renewable) energy, which is believed to have positive ecological impacts that can boost economic growth in the long term. This study analyzes the long-term effects of electricity generation from both dirty and clean energy sources on economic growth in Indonesia, using data from two periods: before the COVID-19 pandemic (2000–2019) and the full period including the COVID-19 pandemic (2000–2022). Empirical findings from Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) methods reveal that dirty energy significantly impacts long-term economic growth in both periods, while clean energy does not have a substantial effect. A robustness check conducted using the Canonical Cointegrating Regression (CCR) method confirms that dirty energy continues to play a crucial role in Indonesia's long-term economic growth. A key finding is that the positive impact of dirty energy generation on economic growth was stronger in the full period including the COVID-19 pandemic compared to before. This suggests that dirty energy contributed more to economic growth during the pandemic. The study recommends a balanced approach to economic growth by prioritizing the transition to clean energy while recognizing the importance of dirty energy in Indonesia's economy. This transition should be gradual, using the current role of dirty energy to support economic development while investing in clean energy alternatives for sustainable growth.
Resilience and Adaptation: Plant Ecology in Indonesia’s Geothermal Environments Idroes, Ghazi Mauer; Khairan, Khairan; Suhartono, Eko; Prasetio, Rasi; Idroes, Ghalieb Mutig; Suhendrayatna, Suhendrayatna
Leuser Journal of Environmental Studies Vol. 3 No. 1 (2025): April 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v3i1.294

Abstract

Geothermal ecosystems are defined by extreme environmental conditions, such as elevated temperatures, high concentrations of toxic chemicals, and fluctuations in abiotic stressors, which shape plant survival and adaptation. These unique ecosystems, found across various geothermal regions globally, support specialized plant communities that have developed distinctive morphological, physiological, and ecological adaptations. Indonesia, located on the Pacific Ring of Fire, is one of the world’s richest geothermal nations, offering an important yet underexplored context for studying vegetation in geothermal zones. This review examines the environmental conditions of geothermal ecosystems, the adaptive strategies of vegetation, and patterns of plant diversity within Indonesian geothermal fields. It also explores ecological succession, community dynamics, and the potential use of geothermal vegetation as environmental indicators for biomonitoring. Despite growing interest, significant research gaps remain, particularly in long-term monitoring and the integration of molecular-level studies. Addressing these gaps is essential for enhancing scientific understanding and informing conservation and sustainable geothermal energy development in tropical regions. This review highlights the ecological significance of geothermal vegetation and underscores the need for interdisciplinary research to support both biodiversity preservation and responsible energy exploitation.
Do Natural Disasters, Fossil Fuels, and Renewable Energy Affect CO2 Emissions and the Ecological Footprint? Idroes, Ghalieb Mutig; Hilal, Iin Shabrina; Hafizah, Iffah; Hamaguchi, Yoshihiro; Bruyn, Chané de; Agustina, Maulidar; Pernici, Andreea; Stancu, Stelian
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.v3i1.285

Abstract

Climate change is a global concern driven by increasing pollution through rising CO2 emissions and growing ecological footprint from human activities. This research investigates how environmental quality (proxied by CO2 emissions and ecological footprint) in Indonesia is affected by multiple factors, including natural disasters, fossil fuels, renewable energy consumption, economic growth, and capital formation from 1965 to 2022. The analysis employs the Autoregressive Distributed Lag (ARDL) model, with robustness ensured using Dynamic Ordinary Least Squares (DOLS), followed by Granger causality tests to examine dynamic relationships between variables. The findings show that natural disasters, fossil fuel consumption, and economic growth contribute to increasing CO2 emissions in the long run, while renewable energy consumption helps reduce them. Natural disasters exhibit a negative but insignificant impact on the ecological footprint. Economic growth increases the ecological footprint, whereas capital formation helps reduce it in the long run. In the short run, fossil fuels are found to increase CO2 emissions, while renewable energy reduces them. Natural disasters are found to increase the ecological footprint. Additionally, the Granger causality test confirms a unidirectional relationship from both natural disasters and economic growth to environmental quality. This study recommends that Indonesia implement integrated strategies focused on accelerating green energy adoption and enhancing disaster resilience to achieve environmental quality.
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.
An Explainable Machine Learning Study of Behavioral and Psychological Determinants of Depression in the Academic Environment Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Ringga, Edi Saputra; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.304

Abstract

Depression is a significant and growing concern within academic environments, affecting both students and staff due to factors such as academic pressure, financial stress, and lifestyle challenges. This study explores the use of machine learning, specifically a Random Forest classifier, to predict depression risk among students using behavioral, psychological, and demographic data. A dataset of 27,788 student records was analyzed after thorough preprocessing and exploratory data analysis. The model achieved strong performance, with an accuracy of 83.52% and an AUC of 0.91, indicating reliable classification of depression status. Local Interpretable Model-agnostic Explanations (LIME) were employed to enhance interpretability, revealing key predictive features such as suicidal ideation, academic pressure, sleep duration, and dietary habits. These interpretable insights align with existing psychological research and provide actionable information for mental health professionals. The findings highlight the value of explainable AI in educational settings, offering a scalable and transparent approach to early depression detection and intervention. Future work should focus on longitudinal data integration, multimodal inputs, and real-world implementation to strengthen the model’s utility and impact.
Credit Card Fraud Detection Through Explainable Artificial Intelligence for Managerial Oversight Muksalmina, Muksalmina; Syahyana, Ahmad; Hidayatullah, Ferdy; Idroes, Ghalieb Mutig; Noviandy, Teuku Rizky
Indatu Journal of Management and Accounting Vol. 3 No. 1 (2025): June 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v3i1.301

Abstract

As digital payment systems grow in volume and complexity, credit card fraud continues to be a significant threat to financial institutions. While machine learning (ML) has emerged as a powerful tool for detecting fraudulent activity, its adoption in managerial settings is hindered by a lack of transparency and interpretability. This study examines how explainable artificial intelligence (XAI) can enhance managerial oversight in the deployment of ML based fraud detection systems. Using a publicly available, simulated dataset of credit card transactions, we developed and evaluated four ML models: Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. Performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score. The Random Forest model demonstrated superior classification performance but also presented significant interpretability challenges due to its complexity. To fill this gap, we applied SHAP (SHapley Additive exPlanations), a leading method for explaining the outputs of the Random Forest model. SHAP analysis revealed that transaction amount and merchant category were the most influential features in determining the risk of fraud. SHAP plots were used to make these insights accessible to non-technical stakeholders. The findings underscore the importance of XAI in promoting transparency, facilitating regulatory compliance, and fostering trust in AI-driven decisions. This study offers practical guidance for managers, auditors, and policymakers seeking to integrate explainable ML tools into financial risk management processes, ensuring that technological advancements are balanced with accountability and informed human oversight.
Do Business Conditions Drive FDI Inflows? A Decomposition Analysis Using B-READY Indicators Hardi, Irsan; Çoban, Mustafa Necati; Maulana, Ar Razy Ridha; Idroes, Ghalieb Mutig; Mardayanti, Ulfa
Indatu Journal of Management and Accounting Vol. 3 No. 1 (2025): June 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v3i1.303

Abstract

Foreign direct investment (FDI) is essential for economic development and business sustainability, and understanding the business conditions that attract it remains a key policy concern. This study adopts a decomposition approach by examining the impact of various B-READY indicators on FDI inflows in separate models, using cross-sectional data from 45 countries. To ensure methodological rigor, it applies three Robust Least Squares (RLS) estimation techniques: M-type, S-type, and MM-type. The findings reveal that six out of ten B-READY indicators exert a positive and statistically significant influence on FDI inflows. The significant B-READY indicators, such as business insolvency, dispute resolution, international trade, labor, market competition, and taxation, highlight critical factors that businesses consider when entering or expanding in foreign markets. These insights offer valuable guidance and practical implications not only for policymakers seeking to strengthen national investment environments, but also for businesses evaluating market readiness and investment risks in foreign economies.
Co-Authors Afjal, Mohd Agustina, Maulidar Ali, Najabat Amalina, Faizah Apriliansyah, Feby Attari, Muhammad Umer Quddoos Ayu Puspitasari, Ayu Bani, Nor Yasmin binti Mhd Bruyn, Chané de Chairunnisa, Rizka Çoban, Mustafa Necati Dahlia, Putri Devi, N. Chitra Duwal, Niroj Eddy Gunawan, Eddy Eko Suhartono Emran, Talha Bin Fadila, Sintia Fajri, Irfan Fazli, Qalbin Salim Fijay, Ade Habya Fikri, Mumtaz Kemal Fitriyani Fitriyani Furqan, Nurul Ghazi Mauer Idroes Hadiyani, Rahmilia Hafizah, Iffah Hamaguchi, Yoshihiro Hapzi Ali Hardia, Natasha Athira Keisha Hidayatullah, Ferdy HUMAM, RAIS AULIA Idroes, Ghifari Maulana Idroes, Rinadi Iin Shabrina Hilal Irsan Hardi Irvanizam, Irvanizam Kadri, Mirzatul Khairan Khairan Khairul, Mhd Khairun Nisa Kusumo, Fitranto Lala, Andi Majid, M. Shabri Abd Majumder, Shapan Chandra Mardayanti, Ulfa Marsellindo, Rio Maulana, Aga Maulana, Ar Razy Ridha Maulidar, Putri Mirza, Muhammad Alfin Falha Muhammad Subianto Mursyida, Waliam Nghiem, Xuan-Hoa Nurleila, Nurleila Pernici, Andreea Phonna, Rahmatil Adha Prasetio, Rasi Ray, Samrat Razief Perucha Fauzie Afidh Rimal Mahdani Rinaldi Idroes Ringga, Edi Saputra RR. Ella Evrita Hestiandari Salimullah, Abul Hasnat Muhammed Saputra, Fachri Eka Saputra, Jumadil Sasmita, Novi Reandy Sikdar, Asaduzzaman Sofyan Syahnur Sofyan, Rahmi Souvia Rahimah Stancu, Stelian sufriani, sufriani Sugara, Dimas Rendy Sugeng Santoso Suhendrayatna Suhendrayatna Suriani Suriani Suwal, Sunil Syahyana, Ahmad T. Zulham Teuku Rizky Noviandy Thahira, Zia Utami, Resty Tamara Wiranatakusuma, Dimas Bagus Zahriah, Zahriah Zhilalmuhana, Teuku Zikra, Naswatun