Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal INTI Nusa Mandiri Jurnal Sistem Komputer dan Informatika (JSON)
I Dewa Gede Loka Maheswara
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERBANDINGAN MODEL MACHINE LEARNING PADA KLASIFIKASI CURAH HUJAN DI BOGOR I Dewa Gede Loka Maheswara; Ahmad Hanif Al’aziz
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6296

Abstract

Accurate rainfall prediction remains a significant challenge due to the involvement of complex physical processes and its substantial impact on various sectors of society. Rainfall prediction can be performed using classification techniques in Data Mining. Each algorithm employed for rainfall prediction may yield different performance outcomes, depending on factors such as the size of the dataset, the number of missing values, and the meteorological parameters utilized in the study. Selecting the appropriate algorithm for rainfall prediction continues to pose a challenge. This study aims to compare the performance of Naïve Bayes, Decision Tree, and Random Forest in order to identify the best model for classifying rainfall in Bogor Regency. The data utilized in this study includes maximum temperature, minimum temperature, average temperature, average humidity, duration of sunlight exposure, maximum wind speed, average wind speed, maximum wind direction, and rainfall. The dataset spans five years comprising a total 1.825 of data obtained from the Class III Citeko Meteorological Station. The results indicate that Random Forest, when trained with a smaller proportion of data compared to the proportion of test data to be predicted, achieves the best performance, with a precision of 59.1%, recall of 64.3%, and f1-score of 65.5%. This performance is attributed to the ensemble principle employed by Random Forest, which combines multiple weak learner trees to produce a robust learner tree.
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.
Co-Authors Ahmad Hanif Al’aziz Arya Zaki Ramadhan Muhammad Fany Nurwibowo Rica Azzura Maldina Yosik Norman