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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Gagah Gumelar
Universitas AMIKOM Yogyakarta
Computer Science & IT

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Klasifikasi Metode Naïve Bayes dan K-Nearest Neighbor untuk Menentukan Keluarga Tidak Mampu Riza Marsuciati; Gagah Gumelar; Rudy Prietno
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.111 KB)

Abstract

The problem of poverty has a critical role in social life, especially for the government associated with all forms of programs to eradicate poverty. The classification of low-income families also serves as a point to prioritize all forms of assistance in government programs. In these problems, it is quite apparent that the distribution of aid is not well-targeted. In this study, we are looking for for the classification method with the best performance in classifying low-income families. This study limits the classification method to the Naïve Bayes classification method and the k-Nearest Neighbor classification method. The dataset used is more than 800 families spread overtwo labels with 12 parameters, where 30 percent of family data is used as training data, and 70 percent of family data becomes as test data. The test results show that the average accuracy of the Naïve Bayes calcification method is 82.68%, while the K- Nearest Neighbor classification method is 85.57%. This study concludes that the best method for classifying low- income families is the Naïve Bayes method
Kombinasi Algoritma Sampling dengan Algoritma Klasifikasi untuk Meningkatkan Performa Klasifikasi Dataset Imbalance Gagah Gumelar; Norlaila2; Quratul Ain; Riza Marsuciati; Silvi Agustanti Bambang; Andi Sunyoto; M. Syukri Mustafa
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.284 KB)

Abstract

A class to be imbalanced when there is a class that has more data than other classes. A comparison between minority classes and the majority class is called Imbalance Ratio (IR). The greater the difference between the minority class and the majority class the value of the Imbalance Ratio (IR) is getting larger. Dataset imbalance in data mining is a serious problem. The application of the classification algorithm regardless of class balance resulted in a good prediction for the majority class and a neglected minority class. Therefore, in this research, the SMOTE algorithm was applied to balance the dataset. The study used 4 datasets with different Imbalance Ratio and used classification algorithms, C45, Naïve Bayes, K-NN, and SVM. Then compared before and after using SMOTE. The research results that have been done accuracy value and value G-mean Naïve Bayes algorithm is consistent with its performance at each level of imbalance ratio, before the implementation has no good performance, whereas after the implemented SMOTE algorithm Naïve Bayes has a consistent increase in accuracy. So it can be concluded that the combination SMOTE + Naïve Bayes most effectively used in the imbalance dataset with different levels in the scheme of 10 fold cross validation and 80% data testing tested as much as 50 times.
Co-Authors Andi Sunyoto M. Syukri Mustafa Norlaila2 Quratul Ain Riza Marsuciati Rudy Prietno Silvi Agustanti Bambang