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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Sulistyowati, Indah Dwi
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Computer Science & IT Control & Systems Engineering Education Library & Information Science Mathematics

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PENERAPAN MACHINE LEARNING DENGAN ALGORITMA SUPPORT VECTOR MACHINE UNTUK PREDIKSI KELEMBAPAN UDARA RATA-RATA Sulistyowati, Indah Dwi; Sunarno, Sunarno; Djuniadi, Djuniadi
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 15 No 1, September Tahun 2024
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.15.1.284-290

Abstract

Machine learning dapat digunakan untuk memprediksi suatu data. Support Vector Machine merupakan bagian dari teknik data mining yang dipergunakan untuk mengidentifikasi dan memprediksi hubungan antara variabel pada suatu dataset. Metode ini efektif untuk melakukan prediksi baik itu untuk klasifikasi ataupun analisis regresi. Perangkat lunak Orange Data Mining 3.3.12 digunakan untuk melakukan proses prediksi. Selanjutnya algoritma Support Vector Machine digunakan untuk memprediksi kelembaban udara. Data masukan adalah suhu, kecepatan angin, penyinaran matahari, dan juga kelembaban udara harian maksimum dan minimum.  Data diambil dari Stasiun Meteorologi Jawa Timur di wilayah Malang pada tahun 2015-2023 sebanyak 2922 dataset. Tujuan penelitian ini adalah untuk mendapatkan nilai RMSE, MAE, dan R-Squared (R2). Rasio perbandingan data pelatihan dan data pengujian ditetapkan pada 70:30. Hasil penelitian menunjukkan bahwa hasil akurasi Root Mean Squared Error (RMSE) dengan nilai 3,378, Mean Absolute Error (MAE) dengan nilai 2,738, dan R-squared (R2) dengan nilai 0,723. Berdasarkan hasil korelasi tersebut menunjukkan bahwa algoritma Support Vector Machine ini termasuk memiliki pengaruh kuat terhadap hasil prediksi kelembaban udara rata-rata harian
Application of Feature Selection and Comparative Analysis of Machine Learning Models for Rainfall Prediction in Jakarta Sulistyowati, Indah Dwi; Sunarno, Sunarno; Iqbal, Iqbal; Syamsuri, KGS M Nurs
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11000

Abstract

Accurate rainfall prediction plays a vital role in reducing disaster risks and supporting public preparedness, particularly in Jakarta where dense population and frequent floods cause serious economic and social impacts. In this study, weather data from the Kemayoran Meteorological Station covering 2004–2023 were analyzed to build rainfall prediction models using machine learning. Three classification algorithms were compared: Logistic Regression, Decision Tree, and Random Forest, selected to represent linear, non-linear, and ensemble approaches. Feature selection was applied using Recursive Feature Elimination (RFE) to identify the most relevant predictors. The models were evaluated using 5-fold cross-validation with metrics including Accuracy, Precision, Recall, F1 Score, ROC AUC, and Cohen’s Kappa. The results indicate that Random Forest achieved the best overall performance with Accuracy of 0.7622, Precision around 0.70, Recall up to 0.63, F1 Score about 0.65, ROC AUC ranging from 0.8044 to 0.8171, and Cohen’s Kappa near 0.48. Logistic Regression also performed competitively with Accuracy of 0.7648, ROC AUC of 0.829, and Kappa of 0.49, while Decision Tree showed lower results with Accuracy of 0.6890 and ROC AUC of 0.6636. The RFE process successfully reduced 18 meteorological attributes to 5 influential features, mainly temperature and relative humidity, which were dominant in distinguishing rainfall events. These findings demonstrate that both Random Forest and Logistic Regression outperform Decision Tree, and Random Forest with RFE can be recommended as the most robust model for rainfall prediction in Jakarta.
Co-Authors Djuniadi Djuniadi Iqbal Iqbal Sunarno Sunarno Syamsuri, KGS M Nurs