Analysis of the Most Dominant Causing Factors of Divorce in 34 Provinces in Indonesia Using the XGBoost Algorithm

Mon, 30 Jun 2025 00:00:00 +0700

Divorce in Indonesia shows a significant increasing trend and has a broad social impact. This study aims to identify the most dominant causes of divorce in 34 provinces in Indonesia using the Extreme Gradient Boosting (XGBoost) machine learning algorithm. Secondary quantitative data from the Central Bureau of Statistics in 2024 were analyzed by pre-processing, data sharing, model training, and performance evaluation. The results showed that constant disputes and quarrels were the main causes of divorce, followed by substance abuse and forced marriage. The developed XGBoost model achieved 75% accuracy in classifying the level of divorce risk. These findings provide new insights into understanding the social factors that influence divorce and can be the basis for designing more effective prevention strategies.

Analysis of Book Production Quality Using Ewma Control Chart: Comparison of Alpha Parameters, Trend Prediction, and Production Correlation

Amelia Putri, Aulia Hani, Anatasia Faradhilla, Risca Octaviyani Hutapea — Mon, 30 Jun 2025 00:00:00 +0700

In the printing industry, maintaining product quality is a critical factor for ensuring customer satisfaction and competitive advantage. This study applies the Exponentially Weighted Moving Average (EWMA) control chart to monitor and control the quality of book production at CV Renjana Offset. Using secondary data from November 2023 to July 2024, the analysis focuses on the percentage of rejected products as a key indicator of quality performance. The EWMA method, with a smoothing constant of ? = 0.3, effectively highlights small shifts in the production process. Results show that all EWMA values lie within the control limits (UCL = 22.54%, LCL = 0.00%), indicating the process is statistically under control. Furthermore, comparison of different alpha values demonstrates the trade-off between sensitivity and stability. A moderate negative correlation (r = -0.505) between production volume and reject percentage suggests increased efficiency at higher production scales. The predicted reject percentage for August 2024 is 4.74%, indicating process stability. Overall, EWMA proves to be a valuable tool in continuous quality monitoring and data-driven decision-making in book production.

EduMatika: Jurnal MIPA

Analysis of the Most Dominant Causing Factors of Divorce in 34 Provinces in Indonesia Using the XGBoost Algorithm

Analysis of Book Production Quality Using Ewma Control Chart: Comparison of Alpha Parameters, Trend Prediction, and Production Correlation