Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing

Comparative Analysis of Optimization Algorithms in Random Forest for Classification of Bank Marketing Data

  • Yoga Religia Universitas Pelita Bangsa
  • Agung Nugroho Universitas Pelita Bangsa
  • Wahyu Hadikristanto Universitas Pelita Bangsa
Keywords: Data Mining, Bank Marketing, Random Forest, Bagging, Genetic Algorithm

Abstract

The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.

 

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Published
2021-02-28
How to Cite
Yoga Religia, Agung Nugroho, & Wahyu Hadikristanto. (2021). Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 187 - 192. https://doi.org/10.29207/resti.v5i1.2813
Section
Artikel Teknologi Informasi