Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir

  • Msy Aulia Hasanah Politeknik Negeri Sriwijaya
  • Sopian Soim Politeknik Negeri Sriwijaya
  • Ade Silvia Handayani Politeknik Negeri Sriwijaya
Keywords: Confusion Matrix, CRISP-DM, Data Mining, Decision Tree, Clasification Algorithms

Abstract

Indonesia is part of a tropical climate with high rainfall intensity. High rainfall intensity can potentially cause flooding. To minimize this, accurate weather predictions are needed to be able to anticipate beforehand. This research was conducted with the aim of classifying based on the rain category with the dichotomy of heavy rain and very heavy rain using data mining techniques with the CRISP-DM methodology. The algorithm used in the classification technique is CART (Classification And Regression Tree) with Confusion Matrix test parameters. Based on the results of the model evaluation, it shows that the CART algorithm has a fairly good performance in classifying with an accuracy value of 89.4%.

Downloads

Download data is not yet available.

References

A. K. Neno, H. Harijanto, and A. Wahid., “Hubungan Debit Air dan Tinggi Muka Air di Sungai Lambagu Kecamatan Tawaeli Kota Palu,” War. Rimba, vol. 4, no. 2, pp. 1–8, 2016.

S. P. Nugroho, “Evaluasi dan analisis curah hujan sebagai faktor penyebab bencana banjir jakarta (in Bahasa),” J. Sains Teknol. Modif. Cuaca, vol. 3, no. 2, pp. 91–97, 2002.

B. K. Tjasyono, I. Juaeni, and W. B. Harijono, “Proses Meteorologis Bencana Banjir,” J. Mkg, vol. 8, no. 2, pp. 64–78, 2007.

А. Вульфин and А. Фрид, “Нейросетевая модель анализа технологических временных рядов в рамках методологии Data Mining,” Информационно-Управляющие Системы, no. 5, 2011.

B. P. T.P and R. D. Indah Sari, “Penerapan Data Mining Untuk Prakiraan Cuaca Di Kota Malang Menggunakan Algoritma Iterative Dichotomiser Tree (Id3),” Jouticla, vol. 2, no. 2, pp. 101–108, 2017, doi: 10.30736/jti.v2i2.68.

P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, “Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5,” Prism. Pros. Semin. Nas. Mat., vol. 3, pp. 64–71, 2020.

S. Huber, H. Wiemer, D. Schneider, and S. Ihlenfeldt, “DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model,” Procedia CIRP, vol. 79, pp. 403–408, 2019, doi: 10.1016/j.procir.2019.02.106.

D. S. Informasi, Artificial Neural Malang Ann Method Implementation To Predict Rainfall in Case of Dengue Fever Anticipation in Malang District. 2018.

R. Prasetya, “Penerapan Teknik Data Mining Dengan Algoritma,” vol. 2, no. 2, 2020.

Sugiyono, “Dokumen Karya Ilmiah | Skripsi | Prodi Teknik Informatika - S1 | FIK | UDINUS | 2016,” Fik, vol. 1, no. 1, pp. 1–2, 2016.

J. Coding and S. K. Untan, “Kata Kunci: Kebakaran Hutan, Data Mining, K-Nearest Neighbor (KNN), Fire Weather Index(FWI). 1.,” vol. 06, no. 2, 2018.

J. Wijaya, “Implementasi algoritma pohon keputusan cart untuk menentukan klasifikasi data evaluasi mobil skripsi,” 2019.

Published
2021-10-07
How to Cite
[1]
M. Hasanah, S. Soim, and A. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir”, JAIC, vol. 5, no. 2, pp. 103-108, Oct. 2021.