Pengembangan Back-end pada Aplikasi Smart Nutrition Berbasis Node.js dan Hapi dengan Integrasi Google Cloud Platform - Lalu Kurnia Muhammad Ridho; Jarir Jarir; Akbar Juliansyah (Jurnal Teknologi Informasi dan Multimedia, 2025)
Advances in digital technology drive the need for smart and integrated nutrition monitoring systems, but developers often focus only on features without considering architectural design. This research aims to develop and implement a RESTful API on the Google Cloud Platform (GCP) backend for the Smart Nutrition App, which has the ability to support daily fruit consumption tracking powered by machine learning. The methodology used is based on the Software Development Life Cycle (SDLC) model, including requirements analysis, cloud-native system design, modular API development using Node.js and Hapi.js, functional testing in Postman, and stress testing in K6 to 4000 virtual users. The results show that the RESTful API can sustain a load of up to 1000 virtual users with 0% error rate, but performance degrades very sharply above this level, to the point where the error rate is 100% at 4000 users. These findings indicate the need for infrastructure optimization to support the demands of real applications. The result of this research is that the system meets the functional requirements and performs well at small scale but requires infrastructure improvements such as load balancing and auto-scaling for scaled environments. The main contribution of this research is to present a scalable and modular backend framework for Smart Nutrition App as a future reference when developing similar systems.