Publication Search

The Indonesian government has implemented various programs to improve public welfare; however, social assistance often misses its target, primarily due to a lack of accurate data. Sirapit Subdistrict, as a government institution, has access to important population data for policy development, particularly in the distribution of aid based on community welfare levels. Factors such as education, age, number of dependents, and income play a significant role in determining an individual's welfare. To address this issue, this study proposes the use of the Apriori method to analyze the factors affecting population welfare. The Apriori method is a data mining algorithm useful for discovering association patterns within a dataset. The study results show that with a support value of 3% and a confidence level of 100%, a pattern was found where residents with a high school education, 1-2 dependents, aged 35-45 years, earning Rp 500,000 - Rp 999,999, and with a low welfare level tend to work as laborers. These findings are expected to serve as a foundation for formulating more targeted policies to improve community welfare in Sirapit Subdistrict.

The decline in water quality is a serious environmental issue, particularly in urban areas. This research aims to develop an Internet of Things (IoT)-based water quality monitoring system using ESP32, pH, TDS, and turbidity sensors. The system is designed to monitor water quality parameters in real-time and transmit data to a cloud platform for further analysis. The system prototype was tested with water samples from various sources, and the results demonstrated high accuracy, with a maximum deviation of ±0.5% compared to laboratory results. Thus, this system offers an efficient and easy-to-implement solution for continuous water quality monitoring, which can aid in water resource management in urban environments.

Bullying is a verbal or non-verbal bullying activity through the media (cyber bullying) or directly, carried out by a child or group of children against other children. Aggressive behavior such as bullying among teenagers results in problems such as anxiety. The problem at SMKN 2 Binjai in 2024 is the difficulty of identifying children's anxiety about bullying disorders because there is no strong reference as evidence for cases of bullying carried out by perpetrators against victims. So research is needed to cluster gender, type of bullying and children's anxiety levels, with many parties still not monitoring children's activities enough to see how big the impact is on children who experience bullying. The aim of this research is to determine gender, type of bullying and different levels of anxiety among children who experience bullying. Based on the results of grouping bullying cases using the K-Means algorithm, 3 clusters and 3 iterations were obtained, where cluster 1 contained 9 data. , cluster 2 has 4 data and cluster 3 has 7 data, so it can be concluded that bullying cases tend to occur in women who experience types of bullying in the form of cyber and psychological with a mild level of anxiety.