Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest

Muhammad Taqiyuddin, Theopilus Bayu Sasongko

Abstract


Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.

Keywords


Weather Prediction; Random Forest; Exploratory Data Analysis; BMKG; Data Preprocessing; Model Evaluation; Weather Uncertainty

Full Text:

PDF

References


L. Malihah, “Tantangan Dalam Upaya Mengatasi Dampak Perubahan Iklim Dan Mendukung Pembangunan Ekonomi Berkelanjutan: Sebuah Tinjauan,†Jurnal Kebijakan Pembangunan, vol. 17, no. 2, pp. 219–232, Dec. 2022, doi: 10.47441/jkp.v17i2.272.

A. M. Siregar, “Klasifikasi Untuk Prediksi Cuaca Menggunakan Esemble Learning,†PETIR, vol. 13, no. 2, pp. 138–147, Sep. 2020, doi: 10.33322/petir.v13i2.998.

G. H. Sandi and Y. Fatma, “PEMANFAATAN TEKNOLOGI INTERNET OF THINGS (IOT) PADA BIDANG PERTANIAN,†Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 1, 2023.

L. Gardashova and S. Jabrayilova, “Weather forecasting using big data,†InterConf, no. 32(151), pp. 629–634, Apr. 2023, doi: 10.51582/interconf.19-20.04.2023.067.

S. Huang, E. Ailer, N. Kilbertus, and N. Pfister, “Supervised learning and model analysis with compositional data,†PLoS Comput Biol, vol. 19, no. 6 June, Jun. 2023, doi: 10.1371/journal.pcbi.1011240.

P. Santoso, H. Abijono, and N. L. Anggreini, “ALGORITMA SUPERVISED LEARNING DAN UNSUPERVISED LEARNING DALAM PENGOLAHAN DATA,†Unira Malang |, vol. 4, no. 2, 2021.

R. Supriyadi, W. Gata, N. Maulidah, A. Fauzi, I. Komputer, and S. Nusa Mandiri Jalan Margonda Raya No, “Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah,†vol. 13, no. 2, pp. 67–75, 2020.

T. Agustina, M. Masrizal, and I. Irmayanti, “Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection,†sinkron, vol. 8, no. 2, Apr. 2024, doi: 10.33395/sinkron.v8i2.13625.

H. Wu, “Comparison of Random Forest and LSTM in Stock Prediction,†Advances in Economics, Management and Political Sciences, vol. 86, no. 1, pp. 28–34, May 2024, doi: 10.54254/2754-1169/86/20240936.

R. Mardianto, Stefanie Quinevera, and S. Rochimah, “Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga,†Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 63–71, May 2024, doi: 10.52158/jacost.v5i1.742.

I. Irawan, R. Qisthiano, M. Syahril, and P. M. Jakak, “Optimasi Prediksi Kelulusan Tepat Waktu: Studi Perbandingan Algoritma Random Forest dan Algoritma K-NN Berbasis PSO,†Jurnal Pengembangan Sistem Informasi dan Informatika, vol. 4, no. 4, 2023.

R. Meenal, P. A. Michael, D. Pamela, and E. Rajasekaran, “Weather prediction using random forest machine learning model,†Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, pp. 1208–1215, May 2021, doi: 10.11591/ijeecs.v22.i2.pp1208-1215.

Z. A. Dwiyanti and C. Prianto, “Prediksi Cuaca Kota Jakarta Menggunakan Metode Random Forest,†Jurnal Tekno Insentif, vol. 17, no. 2, pp. 127–137, Oct. 2023, doi: 10.36787/jti.v17i2.1136.

M. I. Bendi, “Informasi Peringatan Dini Potensi Kekeringan Meteorologis Provinsi Nusa Tenggara Timur,†Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI), vol. 7, no. 1, pp. 46–91, 2024.

Farhanuddin, Sarah Ennola Karina Sihombing, and Yahfizham, “Komparasi Multiple Linear Regression dan Random Forest Regression Dalam Memprediksi Anggaran Biaya Manajemen Proyek Sistem Informasi,†Journal of Computers and Digital Business, vol. 3, no. 2, pp. 86–97, May 2024, doi: 10.56427/jcbd.v3i2.408.

Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,†Jurnal KomtekInfo, pp. 1–7, Jan. 2023, doi: 10.35134/komtekinfo.v10i1.330.

A. Putri, C. Syaficha Hardiana, E. Novfuja, F. Try Puspa Siregar, Y. Fatma, and R. Wahyuni, “Comparison of K-NN, Naive Bayes and SVM Algorithms for Final-Year Student Graduation Prediction Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,†Institut Riset dan Publikasi Indonesia (IRPI) MALCOM: Indonesian Journal of Machine Learning and Computer Science Journal Homepage, vol. 3, no. 1, pp. 20–26, 2023.

M. Iqbal Baihaqi, A. Syaripudin, and F. Agung Nugroho, “Implementation Of The Random Forest Algorithm In Stock Price Predictions Based On Historical Data Implementasi Algoritma Random Forest Pada Prediksi Harga Saham Berdasarkan Data Historis,†Jubitek: JURNAL BIG DATA DAN TEKNOLOGI INFORMASI, vol. 1, pp. 42–51, 2023.

A. Prandika Siregar, D. Priyadi Purba, J. Putri Pasaribu, K. Reza Bakara, and J. V Willem Iskandar Pasar Medan Estate, “Implementasi Algoritma Random Forest Dalam Klasifikasi Diagnosis Penyakit Stroke,†Jurnal Penelitian Rumpun Ilmu Teknik (JUPRIT), vol. 2, no. 4, pp. 155–164, 2023, doi: 10.55606/juprit.v2i4.3039.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,†PeerJ Comput Sci, vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.

H. Sinaga, A. Army, M. Duha, J. Banjarnahor, and S. H. Sinaga, “ANALISIS PREDIKSI DETEKSI STROKE DENGAN PENDEKATAN EDA DAN PERBANDINGAN ALGORITMA MACHINE LEARNING,†Jurnal Ilmiah Betrik, vol. 14, no. 2, 2023.




DOI: https://doi.org/10.30865/mib.v8i3.7897

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.