Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal
NURISSAIDAH ULINNUHA
Department of Mathematics, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediksi Curah Hujan di Kabupaten Sumenep Menggunakan Metode Extreme Gradient Boosting (XGBoost) dan Algoritma Grid Search M THUFAIL ALWANNABIL SAMAS; NURISSAIDAH ULINNUHA; MOH HAFIYUSHOLEH
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 11, No 1 (2026): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v11i1.30-43

Abstract

AbstrakCurah hujan di Kabupaten Sumenep merupakan variabel meteorologis penting karena memengaruhi kegiatan pertanian dan produksi garam. Penelitian ini bertujuan memprediksi curah hujan harian menggunakan metode Extreme Gradient Boosting (XGBoost) yang dioptimalkan dengan Grid Search. Variabel yang digunakan meliputi suhu udara, durasi sinar matahari, tekanan udara, kelembapan udara, kecepatan angin, dan penguapan. Data yang digunakan berupa data cuaca harian dari BMKG periode 1 Juli 2020 hingga 30 Juni 2024. Proses pemodelan meliputi preprocessing data, pembentukan fitur lag, pembagian data menggunakan Time Series Cross Validation dengan pendekatan expanding window, serta optimasi hyperparameter menggunakan Grid Search. Model dengan kombinasi hyperparameter terbaik menghasilkan MAAPE sebesar 0.9152 dan RMSE sebesar 11.9566. Kata kunci: Curah Hujan, Grid Search, Kabupaten Sumenep, Prediksi, XGBoostAbstractRainfall in Sumenep Regency is an important meteorological variable because it affects agricultural activities and salt production. This study aims to predict daily rainfall using the Extreme Gradient Boosting (XGBoost) method optimized with Grid Search. The variables used include air temperature, sunshine duration, air pressure, air humidity, wind speed, and evaporation. The data used is daily weather data from BMKG for the period July 1, 2020, to June 30, 2024. The modeling process includes data preprocessing, lag feature formation, data division using Time Series Cross Validation with an expanding window approach, and hyperparameter optimization using Grid Search. The model with the best hyperparameter combination produced an MAAPE of 0.9152 and an RMSE of 11.9566.Keywords: Rainfall, Grid Search, Sumenep Regency, Prediction, XGBoost
Co-Authors M THUFAIL ALWANNABIL SAMAS MOH HAFIYUSHOLEH