AI-Based Testing Using NLP Algorithm On Eggsperts Website Functionality Using Boundary Value Analysis Technique - Harahap, Zolla Perdana Putra; Alfarizi, Akhfa Bagas; Ferddinansya, Rio; Andi, Adrian Fardan; Nasir, Muhammad; Indriasari, Sofiyanti (Teknik Informatika Universitas Suryakancana Cianjur, 2025)
The poultry farming subsector plays a crucial role in national food security, yet remains constrained by manual recording and efficiency constraints. Digital transformation offers solutions such as the Eggspert website, designed to assist farmers in managing production and sales data quickly and in an integrated manner. This study aims to test the reliability and functionality of the Eggspert system and assess the effectiveness of integrating Artificial Intelligence (AI) into the software testing process. Unlike previous AI-assisted testing studies that primarily focus on generic software applications, this research emphasizes the application of NLP-based AI Testing within a domain-specific digital livestock management system, addressing the lack of empirical testing frameworks tailored to the poultry farming sector. This research is an experimental quantitative approach using Black Box Testing and Boundary Value Analysis (BVA) techniques, combined with Natural Language Processing (NLP)-based AI Testing. The process follows the Software Testing Life Cycle (STLC) stages to ensure systematic and measurable testing. Of the 42 test cases executed, 33 passed and 9 failed, resulting in a success rate of 78.57%. Each test case was executed repeatedly under consistent test conditions to ensure functional stability, with failures indicating specific validation weaknesses rather than random system behavior. Most system functions met specifications, although minor deficiencies remained in text validation and zero pricing. The integration of AI Testing has been shown to improve error detection efficiency. The combination of BVA and AI Testing effectively verified the functionality of the Eggspert system, increased the reliability and efficiency of the testing process, and has the potential to serve as a basis for developing an AI-based testing system in the digital livestock sector.