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All Journal Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics
Kusumo, Fitranto
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Medicine & Pharmacology Other

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Journal : Leuser Journal of Environmental Studies

 1 
TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection Idroes, Ghazi Mauer; Maulana, Aga; Suhendra , Rivansyah; Lala, Andi; Karma, Taufiq; Kusumo, Fitranto; Hewindati, Yuni Tri; Noviandy, Teuku Rizky
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 Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Zahriah, Zahriah; Suhendrayatna, Suhendrayatna; Suhartono, Eko; Khairan, Khairan; Kusumo, Fitranto; Helwani, Zuchra; Abd Rahman, Sunarti
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.
Co-Authors Abd Rahman, Sunarti Azhar, Fauzul Eko Suhartono Ghazi Mauer Idroes Hewindati, Yuni Tri Idroes, Ghalieb Mutig Idroes, Rinadi Iin Shabrina Hilal Irsan Hardi Irvanizam, Irvanizam Khairan Khairan Lala, Andi Majid, M. Shabri Abd Maulana, Aga Misbullah, Alim Rinaldi Idroes Sasmita, Novi Reandy Sofyan Syahnur Suhendra , Rivansyah Suhendrayatna Suhendrayatna Taufiq Karma Teuku Rizky Noviandy Zahriah, Zahriah Zuchra Helwani, Zuchra