Enthusiastic : International Journal of Applied Statistics and Data Science

Modeling the Prevalence of Stunting in Indonesia Using Quantile Regression

2024-09-19T12:54:32+00:00

Stunting is a condition where a child’s height is under the average height of their age. Stunting will have an impact on the quality of human resources. The 2022 Indonesian Nutrition Status Survey reported that the prevalence of stunting in Indonesia reached 21.6%. This number decreased compared to the previous year. However, it remains below the government’s planned target of 14%. Therefore, appropriate methods are needed to model and identify the factors with the most significant impact on the data for each region studied. This research modeled the stunting problem using quantile regression. Quantile regression has several advantages, including the fact that it can be used on data with an inhomogeneous distribution and is not affected by outliers. The results showed that variables that had a significant effect on the prevalence of stunting using 0.95 quantile regression included babies receiving exclusive breast milk, percentage of family planning participants, percentage of households with access to adequate sanitation, low birth weight (LBW) babies, and percentage of toddlers who have Maternal and Child Health (MCH) books. It is hoped that this research can be utilized to carry out appropriate interventions to reduce the prevalence of stunting that occurs in Indonesia.

Temperature Prediction in Norway Using GRUs: A Machine Learning Approach

2025-03-13T01:03:24+00:00

Accurate temperature forecasting in Norway is significant for environmental stewardship and disaster management, in addition to providing essential support for critical sectors, including agriculture, urban development, and energy resource management. This study employed the gated recurrent unit (GRU) to augment the precision of temporal temperature forecasts. After that, it was used to project temperatures for seven days. The dataset, obtained from https://www.yr.no/nb, comprised records of minimum and maximum temperatures spanning from February 1, 2018, to December 31, 2024. The data was partitioned, with 80% allocated for training and 20% designated for testing. Utilizing a training regimen of 20 epochs alongside a three-day lookback interval, the model attained R² scores of 0.82 for minimum temperature predictions and 0.86 for maximum temperature forecasts. These results underscore the GRU model’s capacity to accurately capture daily temperature variations and produce dependable predictions. Given its commendable performance on training and testing datasets, the GRU model is particularly suitable for temperature forecasting.

Claim Reserving Estimation Using the Double Chain Ladder Method with the Bootstrap Approach

2025-01-30T01:08:12+00:00

The claim reserve is the amount of funds the insurance company must set aside to pay claims reported by policyholders. Estimation of claim reserves is carried out as a preventive step for failed payment if the reported claim exceeds the insurance company’s capacity. The estimation of claim reserves in this study was performed using the double chain ladder method with a bootstrap approach. The data used was in the form of a run-off triangle of claim counts and claim amounts presented in incremental and cumulative form. The purpose of this research was to determine the estimated value of reported but not settled (RBNS) and incurred but not reported (IBNR) claim reserves through the bootstrap application on the double chain ladder method. After performing the double chain ladder calculation, the estimated RBNS claim reserves amounted to 6,828,456,000 and the IBNR amounted to 3,714,144,000. Meanwhile, using the bootstrap approach, the RBNS claim reserve estimate was 6,777,539,000 and the IBNR was 3,741,979,000. With the conclusion that the greater the nominal claim reserve allocated, the lower the chance of the company going bankrupt.