Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Software Engineering and Information System (SEIS)
Adityo, Tri Novian
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
MACHINE LEARNING UNTUK PREDIKSI SUHU: SEBUAH TINJAUAN Adityo, Tri Novian; Mukhtar, Harun; Firdaus, Rahmad; Taufiq, Reny Medikawati; Gunawan, Rahmad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.12177

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.
Co-Authors Gunawan, Rahmad Harun Mukhtar Rahmad Firdaus Taufiq, Reny Medikawati