Analisis Dampak ChatGPT Sebagai Code Assistant Terhadap Kualitas Kode Mahasiswa Informatika UTS - Siska Atmawan Oktavia Siska; Siska Atmawan Oktavia; Muhammad Alfan Habib (Management of Information System Journal, 2026)
The rapid advancement of Artificial Intelligence (AI) has introduced various tools that support software development, one of which is ChatGPT. This study aims to analyze the impact of using ChatGPT as a code assistant on the quality of program code produced by Informatics students at Universitas Teknologi Sumbawa. A quantitative experimental method was employed by comparing programming tasks completed manually and with the assistance of ChatGPT. Code quality was evaluated using SonarQube based on three metrics: Maintainability Index, Cyclomatic Complexity, and Reliability (Bug Count), followed by statistical analysis to examine differences between the two conditions. The results indicate that there were no significant differences across all evaluated metrics between manually written code and code generated with ChatGPT assistance (p > 0.05). These findings suggest that the use of ChatGPT did not affect code quality in this study; however, it still has the potential to improve the efficiency of software development. Furthermore, this study provides empirical evidence regarding the impact of using ChatGPT on code quality, based on SonarQube static analysis metrics. The findings are expected to serve as a reference for educators, students, and researchers in evaluating the use of ChatGPT as a code assistant in both learning and software development contexts.