Trend Analysis and Prediction of Violence Against Women and Children Cases in Jakarta Based on the Victim’s Education Level Using ARIMA and SARIMA Method

Zaqi Kurniawan; Rizka Tiaharyadini; Arief Wibowo; Rusdah

doi:10.32736/sisfokom.v14i2.2349

Authors

Zaqi Kurniawan Department of Technology and Information, Budi Luhur University, Jakarta
Rizka Tiaharyadini Department of Technology and Information, Budi Luhur University, Jakarta
Arief Wibowo Department of Technology and Information, Budi Luhur University, Jakarta
Rusdah Department of Technology and Information, Budi Luhur University, Jakarta

DOI:

https://doi.org/10.32736/sisfokom.v14i2.2349

Keywords:

Arima, Child Violence, Education, Sarima, Gender-Based and Child-Directed Violence

Abstract

Violence against women and children remains a critical social issue in Jakarta, Indonesia, where densely populated urban areas often correlate with increased risks of domestic abuse. The urgency of addressing this problem lies in its direct impact on public health, education, and community well-being. This study uses time series prediction models to examine and anticipate trends in the number of reported incidents of violence against women and children in Jakarta. Using publicly accessible data from Jakarta Open Data and the National Commission for the Protection of Women and Children, we applied the ARIMA and SARIMA Models. Key variables included in the dataset are the data period, education level, and total number of victims Using three performance indicators—MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error)—to assess model accuracy the ARIMA model performed better than the SARIMA model. SARIMA recorded an RMSE of 80.26, an MAE of 66.21, and an undefined MAPE because of zero values in the real data, while ARIMA specifically obtained an RMSE of 32.22, an MAE of 32.09, and a MAPE of 5.19%. These results suggest that the non-seasonal ARIMA model is more suitable for this dataset. The study contributes to policy planning and early intervention strategies by offering a data-driven approach to predicting trends in violence within urban contexts.

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