Implementation of Machine Learning to Predict the Weather Using a Support Vector Machine
Implementasi Machine Learning Untuk Memprediksi Cuaca Menggunakan Support Vector Machine
DOI:
https://doi.org/10.21070/ups.2889Keywords:
Machine Learning;, Classification, Weather Prediction, SVMAbstract
Weather is the totality of events that take place in the Earth's atmosphere over several days. Weather that occurs over a longer period of time is known as climate. Things that can affect the state of a weather include: temperature, air pressure, wind speed, air and rainfall. Now the climate in Indonesia itself is sometimes erratic. In fact, when the weather in an area is sunny, in no time it can turn into rain or even a storm. Uncertain changes in this climate can lead to difficulties in predicting a weather. One of the technological advances that can be used today is machine learning. Machine learning is one application that is part of artificial intelligence. Machine learning in Indonesian is also called machine learning.
Downloads
References
B. Suma, Implementasi Machine Learning Di Dalam Prediksi Cuaca. 2020. doi: 10.13140/RG.2.2.16086.47680.
F. R. Lumbanraja, R. S. Sani, D. Kurniawan, and A. R. Irawati, “Implementasi Metode Support Vector Machine Dalam Prediksi Persebaran Demam Berdarah Di Kota Bandar Lampung,” J. Komputasi, vol. 7, no. 2, 2019, doi: 10.23960/komputasi.v7i2.2426.
A. Sulthoni Akbar, C. Dewi, and R. C. Wihandika, “Prediksi Cuaca Kota Denpasar menggunakan Algoritma ELM dengan Optimasi Quantum Delta Particle Swarm Optimization,” vol. 5, no. 3, pp. 1126–1135, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
NUR ROCHMAN DARMAWAN, Prediksi Kondisi Cuaca Kota Surabaya Menggunakan Metode Artificial Neural Network Prediction of Surabaya City Weather Conditions Using Artificial Neural Network Method. 2019.
M. Y. R. Rangkuti, M. V. Alfansyuri, and W. Gunawan, “Penerapan Algoritma K-Nearest Neighbor (Knn) Dalam Memprediksi Dan Menghitung Tingkat Akurasi Data Cuaca Di Indonesia,” Hexag. J. Tek. dan Sains, vol. 2, no. 2, pp. 11–16, 2021, doi: 10.36761/hexagon.v2i2.1082.
Downloads
Additional Files
Posted
License
Copyright (c) 2023 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
