C5.0 Algorithm and Synthetic Minority Over-sampling Technique (SMOTE) for Rainfall Forecasting in Bandung Regency

ERWIN KURNIAWAN

Informasi Dasar

19.04.3346
006.312
Karya Ilmiah - Skripsi (S1) - Reference

Weather is an essential aspect of life because it can affect human activities. Therefore, it is important for weather prediction to have high accuracy. One of the methods used to predict rainfall is data mining. In this study, a classification model was developed using the C5.0 algorithm to forecast rainfall in Bandung Regency. Then, the SMOTE algorithm was used to overcome imbalanced datasets. Weather data for the model development were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG) of Bandung for the years 2005 until 2017. Subsequently, the model was validated using a k-fold cross-validation. The results of the C5.0 test produced the highest accuracy of 92% for the imbalance dataset, while the accuracy of the addition of data using the SMOTE technique was 99%.

Subjek

DATA MINING
 

Katalog

C5.0 Algorithm and Synthetic Minority Over-sampling Technique (SMOTE) for Rainfall Forecasting in Bandung Regency
 
 
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

ERWIN KURNIAWAN
Perorangan
Fhira Nhita, Annisa Aditsania
 

Penerbit

Universitas Telkom
 
2019

Koleksi

Kompetensi

  • BUG1D2 - BAHASA INGGRIS I
  • BUG1E2 - BAHASA INGGRIS II
  • CDG4K3 - DATA MINING
  • CIG4A3 - PEMBELAJARAN MESIN
  • CCH4A3 - PENULISAN PROPOSAL
  • CCH4D4 - TUGAS AKHIR
  • CII4A2 - PENULISAN PROPOSAL
  • CII4E4 - TUGAS AKHIR
  • CPI4A2 - PENULISAN PROPOSAL
  • III4A4 - TUGAS AKHIR

Download / Flippingbook

 

Ulasan

Belum ada ulasan yang diberikan
anda harus sign-in untuk memberikan ulasan ke katalog ini