Claim Missing Document
Check
Articles

Found 32 Documents
Search

Analysis of the Impact of the Peatland Moratorium on Poverty: Evidence from a Difference-in-Differences Analysis Zamaya, Yelly; Pratiwi, Sulistya Rini; Purnamasari, Vidya; Susilo, Ignatia Bintang Filia Dei; Abdurakhman, Abdurakhman
Journal of Economic Education and Entrepreneurship Studies Vol. 7 No. 1 (2026)
Publisher : Department of Economics Education, Faculty of Economics, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62794/je3s.v7i1.189

Abstract

This study examines whether Indonesia's peatland moratorium policy influenced poverty outcomes in Riau Province. Enacted through Presidential Instruction No. 10/2011, the moratorium suspended the issuance of new land-use permits in primary forests and peatland areas as part of the government's commitment to environmental conservation. Using a quasi-experimental Difference-in-Differences (DiD) framework, the study designates districts with peatland coverage exceeding 100,000 hectares as the treatment group, while districts below this threshold serve as the control group. The analysis spans both the pre-moratorium period (2009–2011) and the post-moratorium period (2012–2024). The DiD coefficient of −2.407 (p = 0.228) reveals no statistically significant divergence in poverty trajectories between treated and control areas, indicating that the moratorium lacked direct, measurable effects on household welfare. This outcome underscores the inherent limitations of single-sector environmental governance in resolving the multidimensional character of poverty. Among all covariates examined, educational attainment measured as average years of schooling emerges as the most powerful determinant of poverty reduction (β = −2.468, p < 0.001). Unemployment exhibits a positive association with poverty approaching conventional significance thresholds (β = 0.444, p = 0.053), while GDP per capita shows a statistically significant negative effect. Economic growth, though directionally consistent with poverty reduction theory, does not reach significance, suggesting structural impediments to inclusive growth in the province. These findings call for complementary socioeconomic policies that address human capital deficits and labor market constraints alongside environmental conservation measures.
Gold Price Forecasting with Long Short Term Memory (LSTM) and ARIMAX Method Adila, Raisa Naura; Abdurakhman, Abdurakhman
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.97739

Abstract

Gold is very popular investment instrument due to its annual prices increases. In the long term, gold prices follow a nonlinear pattern, but in the short term, there are fluctuations influenced by various factors, including global market dynamics, monetary policy, and overall economic conditions. Therefore, predicting gold prices is an important step in minimizing risk and maximizing profits for investors. In this study, we analyze the performance of two methods for forecasting global gold prices, namely long short term memory (LSTM) and autoregressive integrated moving average with exogenous variables (ARIMAX). Data used is weekly global gold price data from August 1, 2000, to June 1, 2024. The variables used are Close as the dependent variable and Open as the exogenous variable. The data used is stationary data through the differencing process and algorithmic transformation to overcome non-stationarity issues. The best LSTM model uses the Tanh activation function with 30 LSTM units, 10 timesteps, and a dropout of 0.01, resulting in a MAPE value of 5.323%. The best ARIMAX model obtained was the ARIMAX (0,1,1) model, with a MAPE value of 0.55% for the test data and 0.61% for the training data. The research results, indicate that the higher accuracy of ARIMAX reflects its suitability for linear data such as gold prices, but the accuracy of LSTM which is below 10% still performs well for more complex data patterns.Keywords: gold price; forecasting; LSTM; arimax.