Tinjauan Pustaka: Pemodelan Spasial Suhu Udara Berbasis Machine Learning dengan Integrasi LST, Albedo, dan Data Observasi Stasiun Cuaca
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Abstract
Suhu udara (Ta) merupakan indikator iklim penting yang berperan dalam kajian lingkungan, kesehatan, dan perencanaan wilayah. Keterbatasan stasiun cuaca menjadikan variasi spasial Ta sulit dipetakan secara detail, terutama di wilayah dengan topografi kompleks. Kajian ini menyajikan systematic literature review mengenai pemodelan spasial Ta berbasis machine learning (ML) dengan integrasi data suhu permukaan darat (Land Surface Temperature/LST), Albedo, dan observasi stasiun cuaca. Pencarian pustaka dilakukan menggunakan perangkat Publish or Perish yang terhubung ke Google Scholar untuk periode 2016–2025. Dari 1.000 publikasi yang diperoleh, hanya 8 artikel yang memenuhi kriteria inklusi dan relevan dianalisis lebih lanjut. Hasil telaah menunjukkan bahwa LST merupakan variabel utama yang digunakan dalam seluruh studi, dengan dukungan variabel tambahan seperti Albedo, NDVI, DEM, radiasi matahari, dan faktor meteorologi. Metode ML yang paling dominan adalah Random Forest (RF), disusul Gradient Boosting, Support Vector Regression (SVR), Cubist Regression, serta Multiple Linear Regression (MLR) sebagai pembanding. RF dan Gradient Boosting banyak diterapkan karena akurasinya tinggi dalam menangani data heterogen, sementara SVR efektif untuk regresi nonlinier, dan Cubist Regression terbukti stabil di wilayah bertopografi ekstrem. Secara umum, integrasi data satelit dengan pengamatan darat menghasilkan model dengan performa tinggi (R2 > 0,9 dan RMSE sekitar 1–2 °C). Kajian ini menegaskan bahwa pendekatan ML berbasis penginderaan jauh memiliki potensi besar untuk meningkatkan pemahaman dan pemodelan Ta, sekaligus membuka peluang penelitian lanjutan di wilayah dengan dinamika lingkungan yang beragam.
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