Deep Learning-Based Artificial Neural Network For Predicting HIV/AIDS Risk Using Demographic Data in Medan City - Windania Purba; Risda Putri Greccella; Dendy Prasetyo; M Arief; Nimrot Saritua Marbun; Mutiara Adelia Afrita Purba (Jurnal Sisfokom (Sistem Informasi dan Komputer), 2026)
Medan was one of the largest metropolitan areas in Indonesia, characterized by high population density and mobility, which increased the risk of the spread of infectious diseases, including HIV/AIDS. This disease remained a serious public health problem because it attacked the human immune system and increased vulnerability to opportunistic infections. The objective of this research was to develop a predictive model for HIV/AIDS risk using demographic data from Medan City. This study included the analysis of age, gender, year of onset, and transmission factors. The high number of HIV/AIDS cases and the need for a data-driven approach to support more effective prevention measures were the main challenges. Evaluating potential risks played a significant role in generating useful information for guiding decisions related to public health policies. This study used deep learning with an Artificial Neural Network (ANN) algorithm. The process included data preprocessing, min-max scaling for normalization, encoding, data splitting with a ratio of 80:20, and class weighting. The findings indicated that the model obtained an accuracy rate of 57% and an AUC score of 0.632. The results indicated that the majority of cases were found in men, with same-sex transmission playing a role, and individuals aged 25-49 faced the greatest risk. In conclusion, the ANN model showed potential for predicting HIV/AIDS risk, but its performance still needed improvement. Further development was required to achieve better results.