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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing Yoga Religia; Agung Nugroho; Wahyu Hadikristanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.874 KB) | DOI: 10.29207/resti.v5i1.2813

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.
Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging Agung Nugroho; Yoga Religia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.536 KB) | DOI: 10.29207/resti.v5i3.3067

Abstract

The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimization techniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%.