AI Implementation in Transaction Data Entry for Cash Book Applications
This study focuses on creating and implementing a personal finance management information system that incorporates voice recognition technology integrated with Google Gemini AI 2.5 to improve the efficiency and precision of recording financial transactions. Traditional systems encounter challenges such as labor-intensive manual inputs, elevated error rates in records, and inconsistent user engagement. The Waterfall model was utilized, covering requirements analysis, system design, implementation, testing, and ongoing maintenance. Data gathering involved conducting interviews with 10 participants, observing recording methods, and reviewing literature related to voice recognition and AI technologies. The application was created as a hybrid mobile app utilizing Apache Cordova and the Web Speech API for converting speech to text, alongside Google Gemini AI 2.5 for the automatic extraction of transaction details (type, amount, category). Data is stored locally using SQLite to enable offline access. Blackbox testing, accuracy assessments, and user acceptance evaluations resulted in 87% accuracy in voice recognition, 92% accuracy in transaction categorization, an 83% reduction in input time, and a System Usability Scale (SUS) score of 78, reflecting strong usability. The SRIPSI system allows for transaction recording through everyday Indonesian/English language, a real-time balance dashboard, transaction history, and straightforward reporting, effectively addressing the shortcomings of previous manual applications like Money Lover and Wallet.