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All Journal INTI Nusa Mandiri Jurnal Sistem Komputer dan Informatika (JSON)
Muhammad Fany Nurwibowo
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Computer Science & IT Control & Systems Engineering

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Perbandingan Performa Multi-Algoritma Machine Learning dengan Dua Strategi Validasi pada Klasifikasi Curah Hujan I Dewa Gede Loka Maheswara; Arya Zaki Ramadhan; Rica Azzura Maldina; Muhammad Fany Nurwibowo; Yosik Norman
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9475

Abstract

Prediksi curah hujan yang akurat masih menjadi tantangan karena kompleksitas proses atmosfer serta dampaknya terhadap berbagai sektor. Performa algoritma machine learning dalam klasifikasi curah hujan sangat dipengaruhi oleh karakteristik data dan metode validasi, sehingga diperlukan evaluasi komparatif untuk menentukan model yang paling sesuai pada konteks lokal. Penelitian ini bertujuan membandingkan performa lima model machine learning, yaitu Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbor, dan Decision Tree dalam klasifikasi curah hujan di Kabupaten Tapanuli Tengah menggunakan data observasi harian periode 2015–2024 sebanyak 32.796 data yang diperoleh dari Stasiun Meteorologi FL Tobing. Evaluasi dilakukan melalui skema pembagian data dan 10-cross fold validation dengan metrik precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa Random Forest secara konsisten memberikan performa terbaik pada kedua skema validasi dengan f1-score sebesar 62% dan 63%, lebih stabil dibandingkan model lainnya pada kondisi distribusi kelas yang tidak seimbang. Temuan ini menunjukkan bahwa pendekatan ensemble lebih adaptif dalam menangkap hubungan nonlinier parameter meteorologi serta memberikan dasar metodologis dalam pemilihan model klasifikasi curah hujan untuk mendukung mitigasi bencana hidrometeorologi.
PREDIKSI KATEGORI CURAH HUJAN BERBASIS MACHINE LEARNING UNTUK MENDUKUNG KETAHANAN PANGAN I Dewa Gede Loka Maheswara; Kanaya Kaizzi Larasati; Muhammad Nur Rizqi; Muhammad Fany Nurwibowo; Yosafat Donni Haryanto
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8601

Abstract

Rainfall variability significantly influences food security in Central Tapanuli Regency, North Sumatra, a region where agriculture is strongly reliant on climatic patterns. This research evaluates and compares the classification performance of Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest (RF) for categorizing daily rainfall as supplementary information to strengthen food security. A total of 3,644 daily meteorological records obtained from FL Tobing Meteorological Station spanning 2015 to 2024 were utilized, encompassing seven predictor variables: minimum temperature, maximum temperature, average temperature, mean relative humidity, sunshine duration, peak wind speed, and average wind speed. To mitigate class imbalance, the original six rainfall categories were consolidated into four classes by merging the minority groups. The data were partitioned into training and testing subsets at an 80:20 ratio using stratified sampling, after which the Synthetic Minority Over-sampling Technique (SMOTE) was employed on the training set. Hyperparameter tuning was conducted through Grid Search combined with 5-fold cross-validation, and classification performance was assessed using accuracy, precision, recall, F1-score, and paired t-test analyses. The experimental results indicated that RF delivered superior performance, attaining an accuracy of 51.44% and a weighted F1-score of 0.5036, significantly outperforming both SVM and K-NN (p-value < 0.05). Feature importance analysis revealed that sunshine duration, average temperature, and maximum temperature were the most influential predictors. These outcomes demonstrate that RF holds considerable promise for advancing machine learning-driven rainfall category prediction systems capable of delivering early-stage information for agricultural planting schedules and preparedness against intense rainfall events in Central Tapanuli Regency
Co-Authors Arya Zaki Ramadhan I Dewa Gede Loka Maheswara I Dewa Gede Loka Maheswara Kanaya Kaizzi Larasati Muhammad Nur Rizqi Rica Azzura Maldina Yosafat Donni Haryanto Yosik Norman