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All Journal Journal of Information System Jurnal Informatika dan Multimedia JTIM : Jurnal Teknologi Informasi dan Multimedia G-Tech : Jurnal Teknologi Terapan JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Sewagati
Ashafidz Fauzan Dianta, Ashafidz Fauzan
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : G-Tech : Jurnal Teknologi Terapan

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Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Darmawan, Zakha Maisat Eka; Dianta, Ashafidz Fauzan; Fathoni, Kholid; Rachmawati, Oktavia Citra Resmi; Apriandy, Kevin Ilham
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6649

Abstract

Weather classification is crucial in various sectors, including agriculture, transportation, and disaster management. Accurate weather prediction can help mitigate risks and improve decision-making in these fields. However, classifying weather conditions remains challenging due to the complex and dynamic nature of meteorological data. This study aims to compare different machine learning classification methods to determine the most effective model for weather classification. The research employs a structured methodology consisting of seven key steps: literature study, data understanding, exploratory data analysis, data preparation, modeling, evaluation, and hyperparameter tuning. The study used Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, AdaBoost, and Extra Trees to identify the best-performing classifier. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Gradient Boosting achieved the highest performance, surpassing other models with an accuracy of 90.15%. To optimize the model further, hyperparameter tuning was conducted using GridSearchCV, and feature selection was done using SelectKBest. This process resulted in an improved accuracy of 90.22%, demonstrating the effectiveness of model optimization.
Co-Authors Aji, Rendra Suprobo Akbar, Zulhaydar Fairozal Anggraeni, Martianda Erste APRIANDY, KEVIN ILHAM Damastuti, Fardani Annisa Darmawan, Zakha Maisat Eka Dien, Habibie Ed Dwi Susanto Fadelis Sukya, Fadelis Faradisa, Rosiyah Fathoni, Kholid Fery Sofian Efendi Ikawati, Deasy Sandhya Elya Kusuma, Selvia Ferdiana Nurindiyani, Artiarini Kusuma Rachmawati, Oktavia Citra Resmi Rante, Hestiasari Ratna Widyastuti, Ratna Revindasari, Fony Rifangi, Moh Rinanza Zulmy Alhamri, Rinanza Zulmy Rizky Yuniar Hakkun Safrodin, Mohamad Sa’dyah, Halimatus Septian Enggar Sukmana Sudaryanto, Aris Toga Aldila Cinderatama Tri Budi Santoso Veronica Lita Hapsari Widi Sarinastiti Zainuddin, Muhammad Agus Zikky, Moh Zikky, Mohamad