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All Journal Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics
Fitranto Kusumo
Centre for Technology in Water and Wastewater, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007 NSW Australia
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Environmental Science Other

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TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection Ghazi Mauer Idroes; Aga Maulana; Rivansyah Suhendra; Andi Lala; Taufiq Karma; Fitranto Kusumo; Yuni Tri Hewindati; Teuku Rizky Noviandy
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.42

Abstract

Forest fires have emerged as a significant threat to the environment, wildlife, and human lives, necessitating the development of effective early detection systems for firefighting and mitigation efforts. In this study, we introduce TeutongNet, a modified ResNet50V2 model designed to detect forest fires accurately. The model is trained on a curated dataset and evaluated using various metrics. Results show that TeutongNet achieves high accuracy (98.68%) with low false positive and false negative rates. The model's performance is further supported by the ROC curve analysis, which indicates a high degree of accuracy in classifying fire and non-fire images. TeutongNet demonstrates its effectiveness in reliable forest fire detection, providing valuable insights for improved fire management strategies.
Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring Ghazi Mauer Idroes; Teuku Rizky Noviandy; Aga Maulana; Zahriah Zahriah; Suhendrayatna Suhendrayatna; Eko Suhartono; Khairan Khairan; Fitranto Kusumo; Zuchra Helwani; Sunarti Abd Rahman
Leuser Journal of Environmental Studies Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i2.99

Abstract

Urban areas worldwide grapple with environmental challenges, notably air pollution. DKI Jakarta, Indonesia's capital city, is emblematic of this struggle, where rapid urbanization contributes to increased pollutants. This study employed the CatBoost machine learning algorithm, known for its resistance to overfitting and capability to handle missing data, to predict urban air quality based on pollutant levels from 2010 to 2021. The dataset, sourced from Jakarta's air quality monitoring stations, includes pollutants such as PM10, SO2, CO, O3, and NO2. After preprocessing, we used 80% of the data for training and 20% for testing. The model displayed high accuracy (0.9781), precision (0.9722), and recall (0.9728). The feature importance chart revealed O3 (Ozone) as the top influencer of air quality predictions, followed by PM10. Our findings highlight the dominant pollutants affecting urban air quality in Jakarta, Indonesia and emphasizing the need for targeted strategies to reduce their concentrations and ensure a cleaner and healthier urban environment.
Unveiling the Carbon Footprint: Biomass vs. Geothermal Energy in Indonesia Ghalieb Mutig Idroes; Sofyan Syahnur; M. Shabri Abd Majid; Rinadi Idroes; Fitranto Kusumo; Irsan Hardi
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.
A Deep Dive into Indonesia's CO2 Emissions: The Role of Energy Consumption, Economic Growth and Natural Disasters Ghalieb Mutig Idroes; Irsan Hardi; Teuku Rizky Noviandy; Novi Reandy Sasmita; Iin Shabrina Hilal; Fitranto Kusumo; Rinaldi Idroes
Ekonomikalia Journal of Economics Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i2.115

Abstract

This study examines the influence of non-renewable energy consumption, renewable energy consumption, economic growth, and natural disasters on Indonesia's carbon dioxide (CO2) emissions spanning from 1980 to 2021. The Autoregressive Distributed Lag (ARDL) model is employed, with supplementary robustness checks utilizing Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR). The findings reveal that economic growth, along with non-renewable and renewable energy consumption, significantly affects CO2 emissions in both the short and long term. Robustness checks confirm the positive impact of non-renewable energy consumption and economic growth, while renewable energy consumption has a negative effect on CO2 emissions. Moreover, natural disasters exhibit a positive short-term impact on CO2 emissions. Pairwise Granger causality results further underscore the intricate relationships between the variables. To mitigate climate change and curb CO2 emissions in Indonesia, the study recommends implementing policies that foster sustainable economic development, encourage the adoption of renewable energy, and enhance disaster resilience.
Co-Authors Aga Maulana Andi Lala Eko Suhartono Ghalieb Mutig Idroes Ghalieb Mutig Idroes Ghazi Mauer Idroes Iin Shabrina Hilal Irsan Hardi Khairan Khairan M. Shabri Abd Majid Novi Reandy Sasmita Rinadi Idroes Rinaldi Idroes Rivansyah Suhendra Sofyan Syahnur Suhendrayatna Suhendrayatna Sunarti Abd Rahman Taufiq Karma Teuku Rizky Noviandy Yuni Tri Hewindati Zahriah Zahriah Zuchra Helwani