Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Leuser Journal of Environmental Studies
Abd Rahman, Sunarti
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Energy Environmental Science Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Environmental Benefits of Palm Oil Biodiesel Enhancement: Urea Complexation Optimization via RSM Helwani, Zuchra; Amraini, Said Zul; Abd Rahman, Sunarti; Zahrina, Ida; Julhijah, Noni; Ulfaa, Suci Mas’ama
Leuser Journal of Environmental Studies Vol. 2 No. 2 (2024): October 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v2i2.214

Abstract

Indonesian commercial biodiesel products are unstable due to polyunsaturated fatty acids (PUFA). The urea inclusion compound (UIC) method is used during the fractionation process on PUFA to enhance its quality. This study aims to determine the effect of temperature, fatty acid ester metal (FAME)/methanol ratio, and crystallization time on increasing the concentration of PUFA concentrates and to produce high-performance biodiesel products with an iodine indicator <30–40 g I2/100 g. According to the most recent research, the UCF phase product is obtained at a combination of temperature and crystallization time of 20 °C and 4 h, respectively. It has an iodine number of 44.01 and an oxidation stability of 18.61 h, which is close to the criteria for high-performance biodiesel (<30 –40 g I2/100 g). Meanwhile, the results of this study obtained a UCF phase product that has an iodine number of 34.18 and yields 86.57% is obtained at a combination of temperature and crystallization time of 20 °C and 6 hr and FAME-methanol ratio of 6, respectively, which is close to the criteria for high-performance biodiesel (<30 –40 g I2/100 g). The longer complexation time and temperature significantly affected the FAME fractionation of the UCF phase.
Co-Authors Eko Suhartono Ghazi Mauer Idroes Ida Zahrina Julhijah, Noni Khairan Khairan Kusumo, Fitranto Maulana, Aga Suhendrayatna Suhendrayatna Teuku Rizky Noviandy Ulfaa, Suci Mas’ama Zahriah, Zahriah Zuchra Helwani, Zuchra