Classification of divorce causes during the COVID-19 pandemic using convolutional neural networks

Abstract

The COVID-19 pandemic has affected day-to-day activities. Some families experienced a positive impact, such as an increase of bonding between family members. However, there are families that experienced a negative effect, such as the emergence of various conflicts that lead to a divorce. Based on the literature, it can be stated that the COVID-19 pandemic contributed to the increasing number of divorce rates. This paper proposes a convolutional neural network (CNN) classification algorithm in determining the dominant causes of the increase in divorce rate during the COVID-19 pandemic. CNN is considered suitable for classifying large amounts of data. The data used as research materials are available on the official website of the Indonesian Supreme Court. This research utilizes Supreme Court divorce decisions from March 2020 to July 2021, which constitutes 15,997 datasets. The proposed number of layers implemented during the classification is four. The results indicate that the classification using CNN is able to provide an accuracy value of 96% at the 100th epoch. To provide a baseline comparison, the classical support vector machine (SVM) method was performed. The result confirms that CNN outweighs SVM. It is expected that the results will help any parties to provide a suitable anticipation based on the classified dominant causes of the divorce during the COVID-19 pandemic.