Data-Driven Recommendation System Using Google Trends and Marketplace for MSMEs - Susana Dwi Yulianti; Asep Kurniawan; Shierra Intan Anggari; Okta Gabriel Sinsaku (bit-Tech, 2025)
Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia’s economic growth but often struggle to remain competitive in the rapidly evolving digital marketplace. A key challenge lies in promoting products that align with dynamic consumer preferences and online search trends. This study aims to design and develop a web-based recommendation system intended for MSME business owners and marketers, integrating product data collected through marketplace web scraping with search trend data from Google Trends. Using a Research and Development (R&D) approach with a prototyping model, the research involves stages of data collection, preprocessing, system modeling, implementation, and evaluation. The system utilizes a dataset of 1,028 marketplace products and applies a Hybrid Filtering approach that combines content-based filtering using TF-IDF and collaborative filtering, enhanced by Google Trends as an external weighting factor to improve contextual relevance. Developed using FastAPI and MySQL, the system achieved strong performance with a precision of 0.87, recall of 0.84, and an F1-score of 0.85. In practice, the system assists MSMEs such as local snack producers in identifying and promoting products aligned with trending consumer interests, thereby enhancing visibility and market competitiveness. This research contributes to advancing data-driven decision-making for MSMEs by offering a practical, adaptive, and trend-aware recommendation framework that supports more effective digital marketing strategies in real time.