Development of A Secure Freelance Web-Based and Sentiment-Oriented Digital Platform for Local Artisans: A Case Study of Akungba Akoko - Olatunde David Akinrolabu; Zikirullah Amuda; Samuel Abe; Titilayomi Oluwatosin; Ayomiposi Goodluck Adetula; Akinwale Moses Akinpetide; Akinjogunla Toluwalase Daniel (AI Society & STMIK Indonesia, 2026)
The rapid growth of digital technologies has transformed service delivery across many sectors; however, local artisans in semi-urban communities like Akungba Akoko continue to face limited market visibility, trust deficits, and a lack of structured digital platforms to connect with clients. This study addresses these challenges by developing a secure freelance web-based platform integrated with an AI-driven sentiment analysis engine designed to enhance transparency and improve artisan–client interactions. The main objectives of the research were to provide artisans with a centralised digital marketplace, ensure secure and reliable transactions, and enable intelligent review interpretation that supports trust-building and informed decision-making. The study identified that existing freelancing systems are not adequately tailored to service-based artisans and often lack security features, real-time feedback analysis, and localised accessibility. A mixed-methods approach was employed, beginning with a community survey involving over 2,000 respondents to assess platform needs and user expectations. The system was implemented using the MERN stack, MongoDB, Express.js, React.js, and Node.js combined with a Python-based sentiment analysis module utilising VADER and TextBlob for annotation. Security mechanisms such as JWT authentication, bcrypt password hashing, Paystack payment integration, and multilayer input validation were incorporated to safeguard user data and prevent common cyberattacks. Results showed that 60% of residents experience difficulty locating reliable artisans, while 85% of artisans expressed strong interest in a digital marketplace. System evaluation produced a 99% sentiment-classification accuracy, a 94% usability score from user testing, and a 99% success rate in security audits. These findings demonstrate the platform’s effectiveness in fostering trust, improving accessibility, and strengthening digital inclusion. Overall, the study demonstrates that the developed platform offers a scalable and impactful solution for enhancing artisan visibility and secure client engagement. It is recommended that future enhancements integrate advanced machine-learning models and mobile-application support to broaden user adoption and long-term sustainability.