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All Journal Megasains Jurnal Pijar MIPA Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Agustiarini, Suci
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Mathematics Physics

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Potensi Hujan Ekstrem Nusa Tenggara Barat Kaitannya dengan Tropical Cyclone di Samudera Hindia Agustiarini, Suci; Putra, Made Dwi Jendra
Megasains Vol 10 No 2 (2019): Megasains Vol. 10 No. 2 Tahun 2019
Publisher : Stasiun Pemantau Atmosfer Global Bukit Kototabang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46824/megasains.v10i2.162

Abstract

Siklon tropis merupakan suatu sistem tekanan rendah kuat yang terbentuk di atas perairan laut yang hangat di wilayah lintang rendah dan biasanya ditandai hujan lebat di sekitar wilayah yang dilaluinya. Besarnya potensi hujan lebat bahkan ekstrem tentunya berbeda-beda disetiap wilayah. Penelitian ini dilakukan untuk mengetahui wilayah mana saja di sekitar NTB yang berpotensi terjadi hujan lebat hingga ekstrem saat terjadi siklon tropis di sekitar perairan Samudera Hindia bagian barat hingga barat laut, sehingga dapat memberikan peringatan dini sebelum munculnya bencana hidrometeorologi seperti banjir dan longsor sebagai dampak dari siklon tropis tersebut. Data yang digunakan dalam penelitian ini yaitu data curah hujan harian dan data kejadian siklon tropis dalam 10 tahun (2008-2017). Besarnya potensi hujan lebat hingga ekstrem dihitung menggunakan metode komposit seluruh kejadian siklon pada setiap tahapannya yang telah dikategorikan oleh BOM terhadap nilai curah hujan harian. Hasil dari penelitian ini menunjukkan wilayah yang berpotensi cukup besar terjadi hujan ekstrem saat siklon tropis yaitu sebagian kota Mataram dengan peluang mencapai >15%. Saat dalam tahapan depresi tropis dan badai tropis sebagian Lombok barat bagian selatan, Lombok Utara, Lombok Timur bagian utara, Sumbawa bagian tengah, dan Kota Bima juga berpotensi terjadi hujan ekstrem namun dengan peluang yang sangat kecil yaitu <10%.Siklon tropis yang dapat memicu terjadinya hujan dengan kategori lebat sangat berpeluang ketika masih dalam tahapan depresi tropis dengan wilayah terdampak yang hampir merata di seluruh NTB, namun ketika terus berkembang menjadi siklon tropis maka potensialnya akan semakin meningkat mencapai >30% dan diiringi dengan wilayah terdampak yang semakin kecil.
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.
Study of Developing Models of Crop Failure Risk Information Agustiarini, Suci; Sampelan, David; Maurits, Yuhanna; Baihaqi, Anas; Patria Megantara, Restu; Ulfah, Afriyas; Permana, Angga; Kirana, Nindya; Sulistio Adi Wibowo, Dewo; Purwaningsih, Ni Made Adi; Pamungkas, Cakra Mahasurya Atmojo; Putrantijo, Nuga; Fajariana, Yuaning
Jurnal Pijar Mipa Vol. 19 No. 1 (2024): January 2024
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v19i1.5981

Abstract

Climate is one factor that can influence plant growth. The risk of crop failure due to climate variability can be in the form of reduced water sources, which impact water needs in the land and the emergence of pests and diseases in plants. The risk of planting failure can impact product quality, which has the potential to decrease, higher plant handling costs, and various things that cause losses to farming businesses. The availability of climate forecast information, such as rainfall and other parameters, encourages writers to apply it to information that is easier for users to understand. One of the machine learning algorithms, Decision Tree, is used as a model in determining the risk of planting failure based on each attribute/parameter, including monthly rain, ENSO and IOD phenomena, drought, groundwater availability, and Oldeman climate type. This study aims to make a model prediction of crop failure risk potential, and the calculation is based on climate prediction data. The results of this study show differences in climatic conditions for each commodity when there is an increased potential risk of planting failure. Monthly rainfall is the most dominant factor influencing rice, maize, and soybean planting failure. Validation of the decision tree model shows that this model is quite good in determining the potential risk of crop failure in all commodities studied, with the proportion of correct proportion of more than 65%. However, the Heidke Skill Score (HSS) shows that this model is good for Paddy and Soybean; Maize shows an HSS of less than zero.
Co-Authors Angga Permana Baihaqi, Anas Fajariana, Yuaning Kirana, Nindya Maurits, Yuhanna Pamungkas, Cakra Mahasurya Atmojo Patria Megantara, Restu Pratiwi, Anggitya Purwaningsih, Ni Made Adi Putra, Made Dwi Jendra Putrantijo, Nuga Sampelan, David Sulistio Adi Wibowo, Dewo Ulfah, Afriyas