MACHINE LEARNING UNTUK PREDIKSI SUHU: SEBUAH TINJAUAN

Tri Novian Adityo; Harun Mukhtar; Rahmad Firdaus; Reny Medikawati Taufiq; Rahmad Gunawan

doi:10.37859/seis.v6i2.12177

Authors

Tri Novian Adityo Teknik Informatika, Universitas Muhammadiyah Riau
Harun Mukhtar Teknik Informatika, Universitas Muhammadiyah Riau
Rahmad Firdaus Teknik Informatika, Universitas Muhammadiyah Riau
Reny Medikawati Taufiq Teknik Informatika, Universitas Muhammadiyah Riau
Rahmad Gunawan Teknik Informatika, Universitas Muhammadiyah Riau

DOI:

https://doi.org/10.37859/seis.v6i2.12177

Keywords: Machine Learning, Temperature Prediction, SLR, Deep Learning, Meteorology

Abstract

Global climate change has increased the demand for accurate temperature prediction to support decision-making in sectors such as agriculture, disaster mitigation, and energy management. Machine Learning (ML) and Deep Learning (DL) approaches have been widely applied to model the non-linear and dynamic characteristics of temperature data. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol. From 125 identified articles, 45 studies published between 2021 and 2025 were selected for detailed analysis. The results indicate that Long Short-Term Memory (LSTM) is the most frequently used algorithm, both as a standalone model and within hybrid architectures. Most studies employ multivariate datasets sourced from BMKG, ERA5 Reanalysis, satellite imagery, and the Internet of Things (IoT). Data preprocessing techniques, particularly norssmalization and time-series construction, play a crucial role in improving model stability. However, challenges remain, including hyperparameter sensitivity, complex weather data characteristics, and geographical variability. Future research opportunities include adaptive model development, multi-source data integration, and comprehensive comparative studies among algorithms.

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