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p-Index From 2021 - 2026
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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik Dan Multi-disiplin Ilmu
Ramlah binti Mailok
Universiti Pendidikan Sultan Idris
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Spatial Data Analysis for Environmental and Climate Monitoring Using Google Earth Engine and Random Forest Classification Supiyandi Supiyandi; Ramlah binti Mailok
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu Vol. 2 (2026)
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/ikosstemi.v2.152

Abstract

Land-cover change is a primary driver of environmental degradation and local climate alteration, yet up-to-date, spatially explicit information remains scarce in rapidly developing regions such as North Sumatra, Indonesia. This study aims to develop an accurate, reproducible workflow for environmental and climate monitoring by integrating cloud-based spatial data processing with machine-learning classification. Multitemporal Sentinel-2 surface-reflectance imagery (2019 and 2024) and Landsat-derived land-surface temperature were processed on the Google Earth Engine (GEE) platform. Spectral bands were combined with vegetation, water, and built-up indices (NDVI, NDWI, NDBI, EVI, SAVI, BSI) and topographic data, after which a Random Forest (RF) classifier distinguished six land-cover classes from stratified training samples. The RF model achieved an overall accuracy of 92.4% and a kappa coefficient of 0.906, outperforming Classification and Regression Tree (86.1%) and Support Vector Machine (88.7%) baselines. NDVI, the near-infrared band, and NDWI were the most influential predictors. Between 2019 and 2024 the built-up area expanded by 18.7% while forest and cropland contracted, and these changes coincided with a measurable rise in land-surface temperature, confirming a strong inverse relationship between vegetation cover and surface heating. The results demonstrate that the GEE–RF approach provides a low-cost, scalable, and replicable basis for routine environmental and climate monitoring to support evidence-based regional planning.
Unveiling the Knowledge Structure and Emerging Research Trends of Google Earth Engine Applications in Remote Sensing: A Bibliometric Analysis of Scopus-Indexed Publications (2021–2026) Supiyandi Supiyandi; Ramlah binti Mailok
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1511

Abstract

The convergence of the Random Forest (RF) ensemble classifier with the Google Earth Engine (GEE) planetary-scale computing platform has become one of the dominant operational paradigms in contemporary Earth observation. Despite the rapid accumulation of literature, no systematic bibliometric account of this specific intersection has been published. This study analyses 107 Scopus-indexed journal articles published between 2021 and 2026 that jointly apply RF and GEE to remote sensing problems. Using the bibliometrix R package and VOSviewer, the corpus was examined through descriptive indicators, performance analysis, and five science-mapping techniques: co-authorship, keyword co-occurrence, co-citation, bibliographic coupling, and thematic mapping. Results show a compound annual growth rate of 51.97% between 2021 and 2025, 1,685 total citations, an h-index of 24, and a highly concentrated publication landscape in which three journals Sustainability, International Journal of Digital Earth, and Ecological Informatics account for 59.8% of output. China dominates production with 48 affiliated documents, followed by the United States (20). Co-citation analysis identifies a compact intellectual core anchored by Gorelick et al. (2017), Breiman (2001), and Belgiu and Drăguţ (2016). Thematic analysis reveals a field that is methodologically mature but conceptually narrow: RF is applied across eight application domains, yet only 10.3% of documents address model transferability, 4.7% address uncertainty quantification, and 4.7% address reproducibility. Emerging fronts include explainable AI (SHAP), SAR–optical fusion, and deep-learning hybridisation. The study concludes by articulating seven research gaps and a corresponding agenda for the next phase of cloud-based Earth observation research.
Co-Authors Supiyandi Supiyandi