Separated Couples during the COVID-19 Outbreak: A Survival Support Vector Machine Analysis
DOI:
https://doi.org/10.34123/jurnalasks.v17i1.739Keywords:
feature selection, dissolution, socio-economic determinants, Survival Support Vector MachineAbstract
Introduction/Main Objectives: The separation between spouses has been rising noticeably in recent years in Palangka Raya, particularly during the COVID-19 outbreak. Background Problems: An analysis of time-to-event on those separations will be undertaken quantitatively using survival analysis by comparing the results yielded by Cox proportional hazards (PH) regression and non-parametric Survival Support Vector Machine (SUR-SVM). Novelty: This work suggests a feature selection method that looks for influencing elements related to the c-index by employing backward elimination. Research Methods: This study's data came from Indonesia's Supreme Court webpage, including a database of separation verdicts from the Palangka Raya Religious Court, spanning from April 2020 to March 2021. The response variables were the time-to-separation (marriage length until separation) (t) and the censored state of the occurrence (?). Finding/Results: Based on SUR-SVM, the factors contributing the most to the separation are the absence of children, unsteady employment of appellants, and finance motive as the primary reason. In terms of concordance index and Akaike Information Criterion (AIC), the SUR-SVM outperformed the Cox proportional hazard model. These values of SUR-SVM were 59.24 and 1899.78, respectively. SUR-SVM correctly classified 59.24% of separations based on the chronological order of events.
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