Evaluating next-generation sequencing (NGS) data requires an extensive knowledge of bioinformatics and programming commands, which could limit the studies in this area. We propose a user-friendly system to analyse raw NGS data from HIV-1 patient samples to identify amino acid variants and the virus susceptibility to antiretrovirals. SIRA-HIV was developed as an R Shiny web application. The software Segminator II was applied to analyse viral data. Four genotypic interpretation systems were implemented in R language to classify the HIV susceptibility - the French National Agency for AIDS Research (ANRS), the Stanford HIV Drug Resistance Database (HIVdb), the Rega Institute (Rega) and the Brazilian Network for HIV-1 Genotyping (Brazilian Algorithm). SIRA-HIV was structured in two analysis components. The Drug Resistance Positions module shows the resistance positions, their frequencies, and the coverage. In the Genotypic Resistance Interpretation Algorithms module, the rule-based systems are available to interpret HIV-1 drug resistance genotyping results. SIRA-HIV exhibited comparable results to Deep Gen HIV, HyDRA, and PASeq. As advantage, the proposed application shows susceptibility levels from the most widely used rule-based systems and works locally, allowing analysis not to rely on the internet. SIRA-HIV could be a promising system to aid in HIV-1 patient data analysis.