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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Tunas Geografi Jurnal Reka Lingkungan Majalah Geografi Indonesia Jurnal Transportasi JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Ilmiah Dinamika Sosial JURNAL GEOGRAFI Geografi dan Pengajarannya Jurnal Masyarakat Maritim Jurnal Nuansa Akademik: Jurnal Pembangunan Masyarakat Agri Wiralodra Indonesian Journal of Earth Sciences Jurnal Penelitian Geografi (JPG) Innovative: Journal Of Social Science Research Flight Attendant Kedirgantaraan: Jurnal Public Relation, Pelayanan, Pariwisata Jurnal Inovasi Pendidikan GeoEco
Pratama, Alvin
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Public Health Social Sciences Transportation Other

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Analysis distribution sulfur dioxide and nitrogen dioxide concentration from PLTU Pangkalan susu with callpuff method Zulkarnain, Randy; Suryati, Isra’; Pratama, Alvin
Jurnal Pendidikan Teknologi Kejuruan Vol 3 No 4 (2020): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v3i4.15923

Abstract

Coal-fired power plants will emit several types of pollutants into the ambient air such as particulates and gases. One way to estimate the extent of the impact distribution of these pollutants is by using air quality modeling. The model used in this study is Calpuff, where this model is a non-steady state model and is influenced by variations in meteorological factors. The research location is PLTU Pangkalan Susu (2x200 MW) with SO2 and NO2 parameters. The purpose of this study was to calculate the concentrations of SO2 and NO2 with Calpuff, to validate modeling with field observations and to simulate the distribution of impacts. The results showed that the concentration of SO2 model obtained was 0.32 - 3.57 µg/m3 and NO2 was 0.51 - 5.15 µg/m3. Meanwhile, the observation results showed that the SO2 concentration was 27 - 39.88 µg/m3 and NO2 was 19.77 - 29.73 µg/m3. The simulation results of the distribution of SO2 and NO2 concentrations with the Calpuff model show that the impact distribution area is in the direction of the wind in the windrose and the affected area is in the southwest of the PLTU Pangkalan Susu. The results of model validation for the values ​​of d = 0.97, r = 0.616 - 0.665 and FB = -1.719 - -1.849, which means that the Calpuff model is quite valid and can be applied to predict the impact distribution area at PLTU Pangkalan Susu.
META ANALYSIS: THE EFFECT OF AUGMENTED REALITY ON STUDENTS' GEOGRAPHY LEARNING OUTCOMES IN SCHOOLS Lubis, Darwin Parlaungan; Anja Kusumawati, Eka Suci; Rahmadi, M Taufik; Sugiharto; Permana, Sendi; Pratama, Alvin; Suciani, Ayu; Siahaan, Nurhalimah
JURNAL GEOGRAFI Geografi dan Pengajarannya Vol 23 No 2 (2025): JURNAL GEOGRAFI Geografi dan Pengajarannya
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jggp.v23n2.p373-384

Abstract

Geography learning outcomes reflect the success of the learning process, particularly in the cognitive domain. However, many students struggle to understand abstract geographical concepts that are ideally taught in natural settings. This study aims to analyze the impact of augmented reality (AR) as a learning medium on geography learning outcomes. Using a quantitative meta-analysis method, 14 ISSN-certified national journal articles were reviewed. The results show that AR significantly enhances geography learning outcomes, with an average effect size of 0.34, indicating a strong positive impact. he results show that AR significantly enhances geography learning outcomes, with an average effect size of 0.34. According to Cohen's criteria, this value is categorized as a large effect, signifying that AR has a substantial and meaningful impact on students’ understanding of abstract geographical concepts. This effect size also surpasses that typically reported for conventional learning media, highlighting the innovative advantage of AR in geography education. Keywords: Augmented Reality, Learning Outcomes, Meta-analysis
TRACING THE SOURCES AND TRANSPORT PATHWAYS OF PARTICULATE IN JAKARTA USING THE WRF-HYSPLIT MODEL DURING THE JULY 2023 POLLUTION EPISODE Batubara, Ririn Anggina; Pratama, Alvin; Nurlatifah, Amalia
Jurnal Reka Lingkungan Vol 14, No 1 (2026)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekalingkungan.v14i1.13-26

Abstract

PM2.5 pollution remains a critical environmental and public health issue in Jakarta, particularly during the dry season when unfavorable meteorological conditions enhance pollutant accumulation. This study examines the sources and transport pathways of PM2.5 during a severe pollution episode in July 2023, utilizing a combined Weather Research and Forecasting (WRF) and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) modeling approach. Meteorological simulations were evaluated against surface observations, while backward trajectory analyses were conducted using multiple meteorological datasets to assess the consistency of transport. The results indicate that PM2.5 transport into Jakarta was predominantly influenced by air masses originating from the east and southeast, associated with industrial activities, power plants, and local fire events in surrounding regions. The presence of the southeast monsoon contributed to reduced atmospheric dispersion, resulting in prolonged pollutant residence times over the urban area. Despite some limitations in wind speed simulation, the WRF model adequately represented key meteorological parameters relevant to trajectory analysis. These findings highlight that Jakarta’s air pollution is driven by the combined effects of local emissions and regional transport processes, emphasizing the need for integrated air quality management strategies that extend beyond administrative boundaries.
IDENTIFYING CHANGES IN MANGROVE FOREST DISTRIBUTION IN LANGKAT REGENCY USING GOOGLE EARTH ENGINE Batubara, Rina Mawardah; Rahmadi, M Taufik; Harefa, Meilinda Suriani; Lubis, Darwin Parlaungan; Permana, Sendi; Nikmah, Rifqi Ulfatun; Pratama, Alvin
GeoEco Vol 11, No 2 (2025): GeoEco July 2025
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v11i2.99525

Abstract

Mapping the distribution of mangrove forests is very important to determine the ecological changes, because mangroves protect biodiversity. Google Earth Engine (GEE) is a cloud-based platform that can analyse the distribution of mangrove forests in a complex and efficient manner. This study aims to analyse the use of Google Earth Engine in monitoring changes in the distribution of mangrove forests in Langkat Regency from 2018 to 2024. This study uses Sentinel-2A image data by applying NDVI computed on Google Earth Engine (GEE). The results of the study show that there has been a change in the distribution of mangrove forests from 27,095.4 Ha (2018) to 26,700.56 Ha (2021) and to 24,685.74 Ha (2024), which shows a reduction in distribution by 394.84 Ha (2018-2021) and 2,014.82 Ha (2021-2024). The largest change in distribution occurred in Pangkalan Susu District with a reduction in area of 307.99 Ha (2021) and 984.64 Ha (2024). Changes in the distribution of mangrove forests are caused by human activities in land clearing for ponds and oil palm plantations. The accuracy test of the Sentinel-2A image uses a Confusion Matrix with an Overall Accuracy of 85%. This study shows the potential that Google Earth Engine-based Citra Sentinel-2A data can be used to monitor the distribution of mangrove forests.
Co-Authors Afrita, Indra Angraini, Tia Anja Kusumawati, Eka Suci Arbiansyah Arbiansyah, Arbiansyah Asyifa Putri, Nindy Baehaqi Batubara, Rina Mawardah Batubara, Ririn Anggina Damanik, M Ridha Syafii Darwin P. Lubis, Darwin Duha, Asnimar Elfayetti Elfayetti Eni Yuniastuti Fadilah, Rayhan Fazera, Della Fitra Delita, Fitra Fitri Ramadani, Yunda Fitri, Maria Fortunata, Lisna Anggia Harahap, Amru Akbar Harefa, Meilinda Suriani Ilvaldo, Iqbal Kembaren, Rezia Gloria Manurung, Naomi Nainggolan, Edoy Nikmah, Rifqi Ulfatun Novira, Nina Nurlatifah, Amalia Nurmala Berutu Pratiwi Sembiring, Yolanda Pulungan, Syaputra Hidayat Putri, Nindy Asyifa Rahmadi, M Taufik Ramadani, Yunda Fitri Ryabtsev, Vladimir S. Sugiharto Sembiring, Lin Arsenna Br Sembiring, Yolanda Pratiwi Sendi Permana Siahaan, Nurhalimah Sianturi , Stevan Federico Sianturi, Stevan Federico Sihombing, Erlina Teresia Sinabutar, Anggi Violin Sinaga, Monica Amelia Sintong, Mahara Sipayung, Calvin Situmorang, Amrita Margaretha Sriyanti , Nora Suciani, Ayu Sugiharto Suriani Harefa, Meilinda Suryati, Isra’ Tanjung, Putra Laksamana Teresia Sihombing, Erlina Triana, Yeni Tuhono, Eling Tumiar Sidauruk Utami, Widya Astri Zulkarnain, Randy