Performa Algoritma Data Mining Untuk Klasifikasi Data Perceraian

Elvin Leander Hadisaputro, Joy Nashar Utamajaya

Abstract

Data mining is the process of analyzing data to find patterns from existing data to produce useful information. One type of data mining is classification, which distinguishes a data object from another. There are several algorithms for classification. This study looks for the best performance of data mining algorithms for decision tree classification, nave Bayes, k-nearest neighbors, neural networks and Support Vector Machines. Performance is seen without using feature selection and using feature selection found in the RapidMiner application, namely Backward Elimination and Forward Selection. The data used is data on divorce cases at the Penajam Religious Court from 2018-2020. 2018-2019 data is used as training data and 2020 data is used as test data. The best performance found from the use of test data is the nave Bayes algorithm that uses Backward Elimination with an accuracy value of 81.72% and AUC 0.691.

Keywords

Performa algoritma; Feature selection; Klasifikasi; Data Perceraian; Decision tree; Naive Bayes; k-nearest neighbour; neural network; support vector machine

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