cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Teuku Rizky Noviandy
Contact Email
trizkynoviandy@gmail.com
Phone
+6282275731976
Journal Mail Official
editorial-office@heca-analitika.com
Editorial Address
Jl. Makam T. Nyak Arief Kompleks BUPERTA Blok L7B, Lamgapang, Aceh Besar, Provinsi Aceh
Location
Kab. aceh besar,
Aceh
INDONESIA
Ekonomikalia Journal of Economics
Published by PT. Heca Sentra Analitika
ISSN : -     EISSN : 29885787     DOI : https://doi.org/10.60084/eje
Core Subject : Economy, Science, Social,
Economics, Econometrics & Finance Energy Environmental Science
Ekonomikalia Journal of Economics (EJE) stands as a distinguished global scholarly publication. It is dedicated to releasing original research articles and review papers of exceptional quality within the realm of economics. The primary aim of the journal is to foster cross-disciplinary research, facilitate the exchange of knowledge, and propel the advancement and implementation of pioneering approaches. EJE remains steadfast in its pursuit of excellence, significance, and influence, serving as an invaluable asset for researchers, professionals, and educators across the globe. Topics of this journal includes, but not limited to: microeconomics and macroeconomics, international economics, development economics, public economics, behavioral economics, econometrics, regional economics, monetary economics, islamic economics, energy economics, environmental economics, political economy
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekomomi dan Ekonometrika
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2023): July 2023" : 5 Documents clear
Decrypting the Relationship Between Corruption and Human Development: Evidence from Indonesia Hardi, Irsan; Saputra, Jumadil; Hadiyani, Rahmilia; Maulana, Ar Razy Ridha; Idroes, Ghalieb Mutig
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.22

Abstract

Corruption is considered endemic in a large part of the world's population and is believed to be a factor that disrupts market behavior and distorts competition, thereby hindering economic growth and human development. This study aims to unveil the impact of corruption on Indonesia's human development through various approaches, utilizing Fully-Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Moderated Regression Analysis (MRA), Path Analysis, and Vector Error Correction Model (VECM) methods, with data covering the period from 1995 to 2022. The results of the estimation are discussed in three parts: 1) Dynamic Impact, by analyzing the long-term direct effect of corruption on human development; 2) Indirect Impact, by examining the role of government expenditure, tax revenue, and public debt in mediating the effect of corruption on human development; and 3) Causal Impact, by determining the unidirectional and bidirectional relationships between all variables studied. The findings indicate that corruption does not have a lasting direct impact on human development. Moreover, government expenditure and public debt play a role in moderating the impact of corruption on human development. Additionally, there is no causal link between corruption and human development, whereas there are causal connections between human development, government expenditure, tax revenue, and public debt. The results of this study will be valuable in assessing the extent of corruption's impact on human development, particularly in Indonesia, and aim to raise awareness of policymakers, hence encouraging individuals to participate in combating corruption.
Unveiling the Carbon Footprint: Biomass vs. Geothermal Energy in Indonesia Idroes, Ghalieb Mutig; Syahnur, Sofyan; Majid, M. Shabri Abd; Idroes, Rinadi; Kusumo, Fitranto; Hardi, Irsan
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.47

Abstract

Global climate change, caused by greenhouse gases (GHGs) emissions, particularly carbon dioxide (CO2), has an enormous and unprecedented impact on our planet's ecosystem, development, and long-term sustainability. This study investigates the dynamic impact of biomass and geothermal energy on CO2 emissions in Indonesia from 2000 to 2020. Employing the Green Solow model with the approach of Fully-Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Autoregressive Distributed Lag (ARDL) and Pairwise Granger causality test. The cointegration tests suggest the existence of a long-term equilibrium relationship between CO2 emissions, biomass, and geothermal energy. Empirical evidence reveals that although biomass and geothermal energy positively influence CO2 emissions, their overall impact is relatively low. This highlights the potential for these renewable energy sources to contribute to CO2 reduction and promote environmental sustainability. The Granger causality test confirms a causal relationship between CO2 emissions, biomass, and geothermal energy. Important policy recommendations for promoting sustainable energy practices in Indonesia involve investing in high-quality biomass and geothermal facilities to reduce emissions, implementing energy efficiency programs and fossil fuel conservation measures, and encouraging the use of electricity-based biomass and geothermal energy sources to reduce dependence on non-renewable fuels. These recommendations play a crucial role in achieving environmental and economic sustainability.
Understanding Short-Term and Long-Term Price Fluctuations of Main Staple Food Commodities in Aceh Province, Indonesia: An ARDL Investigation Putra, Hadi Arisyah; Fijay, Ade Habya; Suriani, Suriani; Seftarita, Chenny; Ringga, Edi Saputra; Wintara, Heri; Fadliansah, Oka
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.50

Abstract

Aceh Province still relies on external sources from other regions for its main staple food commodities, resulting in unpredictable price fluctuations. To address this issue, it is essential to identify the key determinants responsible for these fluctuations and implement suitable preventive measures and policies. Utilizing monthly time-series data from January 2016 to December 2020 and employing the Autoregressive Distributed Lag (ARDL) approach, we investigate the short-term and long-term impact of variables like raw material prices, rainfall, and price index received by farmers on the price fluctuations. The results of the ARDL estimation reveal that all selected independent variables play a crucial role and significant in influencing the price fluctuations of main staple food commodities. Armed with these findings, we suggest that policymakers can provide necessary resources to farmers, strengthen weather monitoring systems, and enhance market transparency, thus better controlling future price fluctuations of regional staple food commodities.
Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Adam, Muhammad; Rusyana, Asep; Sofyan, Hizir
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.51

Abstract

This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
Natural Disasters and Economic Growth in Indonesia Idroes, Ghalieb Mutig; Hardi, Irsan; Nasir, Muhammad; Gunawan, Eddy; Maulidar, Putri; Maulana, Ar Razy Ridha
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.55

Abstract

Natural disasters can have a profound impact on a country's economic growth, making it crucial for policymakers to understand the relationship between natural disasters and economic growth in order to develop effective strategies that mitigate adverse effects and promote sustainable development. The study utilizes secondary data spanning from 1990 to 2021 and employs the Fully-Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Co-Integrating Regression (CCR), and Vector Error Correction Model (VECM) methods. The study's findings provide valuable insights into the substantial effects of natural disasters on economic growth, indicating a positive long-term impact. Furthermore, the analysis highlights a unidirectional causality, illustrating the notable influence of natural disasters on the country's economic performance. Policymakers should prioritize investments in upgrading and retrofitting infrastructure, focusing on key sectors like transportation, energy, water, and telecommunications, to mitigate the adverse effects of natural disasters and promote sustainable economic growth.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2026): April 2026 (In Press) Vol. 3 No. 2 (2025): October 2025 Vol. 3 No. 1 (2025): April 2025 Vol. 2 No. 2 (2024): October 2024 Vol. 2 No. 1 (2024): April 2024 Vol. 1 No. 2 (2023): November 2023 Vol. 1 No. 1 (2023): July 2023