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All Journal JIKA: Jurnal Ilmu Keuangan dan Perbankan Management Science Research Journal Eduvest - Journal of Universal Studies International Transactions on Artificial Intelligence (ITALIC)
Aziz, Lukmanul Hakim
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Library & Information Science Neuroscience Social Sciences

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The Measurement of Efficiency of Sharia Banks in Indonesia: Two-Stage Data Envelopment Analysis (DEA) Aziz, Lukmanul Hakim; Ganika, Gerry; Mala, Chajar Matari Fath
Jurnal Ilmu Keuangan dan Perbankan (JIKA) Vol. 13 No. 1: December 2023
Publisher : Program Studi Keuangan & Perbankan, Fakultas Ekonomi dan Bisnis, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jika.v13i1.11397

Abstract

This research was conducted to measure the efficiency of sharia banks in Indonesia. The study utilized two stages, with the first stage employing the Data Envelopment Analysis (DEA) method, and the second stage conducting an analysis using the Tobit Method to determine the influence of market competition, market share, NSFR, CAR, ROA, and BOPO variables on its independent variable, which is efficiency. This research utilized panel data from sharia banks in Indonesia. The findings of this study provide a deeper understanding of the factors influencing the efficiency of sharia banks, and the results can serve as a basis for formulating policies that support the growth of a more efficient sharia banking industry. Several variables that affect sharia banks in Indonesia are market share, NSFR, CAR, ROA, and BOPO. However, variables such as the Lerner Index and ROA do not have a significant impact on the level of efficiency.
The Role of Capital Adequacy Ratio in Enhancing Regional Development Banks' Stability: An Empirical Study from 2012-2022 Aziz, Lukmanul Hakim; Siregar, Hermanto; Achsani, Noer Azam; Irawan, Tony
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.51304

Abstract

This study aims to analyze the role of the Capital Adequacy Ratio (CAR) in moderating factors affecting the stability (Z-score) of Regional Development Banks (BPD) in Indonesia from 2012 to 2022. Using quarterly panel data regression, this research categorizes BPDs into two groups: Category-1 banks that have not met the minimum capital requirements and Category-2 banks that have met these requirements. The findings reveal significant differences in how various factors influence stability across these categories. In Category-1 banks, factors such as market competition (Lerner Index), market share of loans (MSL), and deposits (MSD) have a more pronounced impact on stability, highlighting their reliance on external conditions. Conversely, Category-2 banks exhibit greater resilience, with CAR positively contributing to stability, while factors like efficiency (TEF and SEF) and macroeconomic conditions (regional GDP) play a crucial role in risk management. The study also finds that factors such as Loan to Deposit Ratio (LDR) and Non-Performing Loans (NPL) affect stability differently across categories, emphasizing the need for tailored risk management strategies. These insights provide practical implications for policymakers and banking management in optimizing regulatory frameworks and enhancing the stability of BPDs.
The Measurement of Efficiency and Analysis of Factors Affecting Conventional Commercial Banks in Indonesia Aziz, Lukmanul Hakim; Manurung, Adler Haymans; Sembel, Roy; Imron, Ali
Management Science Research Journal Vol. 2 No. 3 (2023): August 2023
Publisher : PT Larva Wijaya Penerbit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56548/msr.v2i3.63

Abstract

The purpose of this research is to measure the level of efficiency of conventional commercial banks in Indonesia with input variables that are thought to influence output variables using non-parametric methods using the Data Envelopment Analysis (DEA) model and then to analyze the factors that affect the levels of bank efficiency. The object of this study consisted of 12 (twelve) Conventional Commercial Banks in Indonesia which were analyzed from 2012 to 2021. Overall, the results show that the level of efficiency of Conventional Commercial Banks in Indonesia during the period of this study, has not yet reached an optimal level of effectiveness. The factors that significantly affect the level of efficiency of conventional commercial banks are Concentration Ratio 4 (CR4), Market Share (MS), Lerner Index (LI), Loan to Deposit Ratio (LDR) and Capital Adequacy Ratio. (CAR). While the factors that do not affect the level of efficiency of conventional commercial banks are Return on Assets (ROA) and Non-Performing Loans. (NPL).
Life Cycle Assessment of Silicon Photovoltaics and Their Environmental Impacts Aziz, Lukmanul Hakim; Callula, Brigitta; Rozi, Achmad; Madani, Muchlisina
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.941

Abstract

The rapid expansion of silicon based Photovoltaic (PV) technologies continues to drive the global shift toward sustainable energy systems. However, the environmental implications across the full life cycle of PV modules particularly those associated with upstream silicon purification routes remain insufficiently examined. This study provides a comprehensive assessment of the environmental and process level impacts of Metallurgical Grade Silicon (MGS) and Upgraded Metallurgical Grade Silicon (UMGS), covering extraction, manufacturing, operation, and end of life stages. A process oriented Life Cycle Assessment (LCA) is conducted to analyze variations in carbon intensity, hazardous material use, and energy demand, complemented by comparative evaluations of monocrystalline and polycrystalline module production pathways. To enhance analytical precision, this study incorporates an AI-assisted predictive modeling framework using supervised machine learning to estimate Global Warming Potential (GWP) and identify key factors influencing emission variability. The AI-enhanced model reveals that electricity mix and purification route exert the strongest influence on GWP, and scenario simulations demonstrate that UMGS based processes can reduce upstream emissions by up to 89% under favorable energy conditions. Additionally, the study highlights future challenges related to increasing PV waste volumes between 2025 and 2030 and the need for improved recycling infrastructures. Overall, the integration of AI-based prediction with conventional LCA offers a more dynamic and adaptive evaluation of PV sustainability performance. The findings underscore the importance of renewable powered manufacturing, early adoption of low-energy purification technologies, and policy support to achieve long-term environmental and socio-economic benefits.
Co-Authors Adler Haymans Manurung Ali Imron Callula, Brigitta Erliza Noor Ganika, Gerry Hermanto Siregar Madani, Muchlisina Mala, Chajar Matari Fath Roy Sembel Rozi, Achmad Tony Irawan