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All Journal Jurnal Meteorologi Klimatologi dan Geofisika Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Pratiwi, Anggitya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Mathematics Physics

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IDENTIFIKASI PERUBAHAN CURAH HUJAN DAN SUHU UDARA MENGGUNAKAN RCLIMDEX DI WILAYAH SERANG Hidayat, Nizar Manarul; Pandiangan, Alexander Eggy; Pratiwi, Anggitya
Jurnal Meteorologi Klimatologi dan Geofisika Vol 5 No 2 (2018): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.557 KB) | DOI: 10.36754/jmkg.v5i2.57

Abstract

Data iklim dianalisis untuk mengidentifikasi perubahan besaran parameter iklim maupun peristiwa iklim ekstrem. Data yang menjadi kajian berasal dari Stasiun Meteorologi Serang, Banten. Tujuan penelitian ini adalah menganalisis karakterisktik data cuaca stasiun pengamatan cuaca Serang terutama pada suhu udara dan curah hujan serta mengidentifikasi perubahan pada pola dan nilai kedua parameter tersebut. Pengolahan data dilakukan dengan menggunakan RClimdex untuk mengetahui adanya perubahan jangka panjang suhu dan curah hujan. Secara keseluruhan slope indeks suhu udara pada stasiun pengamatan Serang mengalami penurunan diantaranya indeks TX10p dan TN10p, yang mengindikasikan jumlah hari dengan suhu minimum pada siang hari dan malam hari mengalami penurunan. Pada stasiun Serang slope indeks suhu udara (TN90p) cenderung meningkat yang mengindikasikan suhu minimum lebih hangat. Indeks curah hujan hampir seluruhnya mengalami penurunan kecuali CDD yang menunjukkan peningkatan jumlah hari tanpa hujan berturut-turut di wilayah Serang.
Air Temperature-based Spatial Modeling of Remote Sensing Data Using Machine Learning Approaches: a Systematic Literature Review Sampelan, David; Pratiwi, Anggitya; Baihaqi, Anas; Agustiarini, Suci
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i2.8450

Abstract

This study presents a systematic review of spatial air temperature modeling based on remote sensing data using machine learning approaches during the period 2016–2025. Using the PRISMA framework, we conducted literature searches in Google Scholar (998 articles) and Scopus (489 articles).. After merging the datasets, removing duplicates, and applying inclusion–exclusion criteria, 12 articles were retained for in-depth analysis. The findings indicate a marked increase in publications since 2021, reflecting growing global interest in integrating remote sensing and machine learning for air temperature estimation. Ensemble algorithms such as Random Forest and XGBoost dominate due to their balance of accuracy and computational efficiency, while temporal deep learning approaches such as LSTM and TCN are emerging as powerful tools for capturing complex atmospheric dynamics. Among remote sensing predictors, Land Surface Temperature (LST) is the most frequently used, often complemented by NDVI, albedo, and elevation to improve spatial accuracy. Geographical context strongly influences methodological performance. XGBoost proves effective in heterogeneous urban areas, Random Forest performs well in mountainous regions, and artificial neural networks demonstrate higher adaptability in extreme environments such as the Greenland ice sheet. Nonetheless, limited ground-based observations and sparse station networks remain key challenges, particularly across tropical and archipelagic regions. This review identifies three major directions for future research: (1) expanding studies to underrepresented tropical regions, (2) leveraging temporal deep learning methods for detecting extreme events, and (3) integrating multisensor data with innovative validation strategies to enhance the robustness and reliability of air temperature modeling.
Co-Authors Agustiarini, Suci Baihaqi, Anas Hidayat, Nizar Manarul Pandiangan, Alexander Eggy Sampelan, David