Optimizing to Predict Purchase Intention in Fashion Thrifting Using Artificial Neural Networks Approach - Fadil Abdullah; Manase Sahat H Simarangkir; Adie Kusna Wibowo; Abdullah Rizky Alfatih; Nadiya Maharani (Politeknik META Industri Cikarang, 2026)
Background Thrifting has emerged as a prominent trend within the fashion industry, driven by increasing consumer awareness of sustainability and the demand for affordable fashion alternatives Purpose This study develops an Artificial Neural Network (ANN) model to optimize purchase intention for thrifting fashion products based on trends, online promotions, and brand image Methodology The model uses three node variations (10, 20, 30), two hidden layers, a sigmoid activation function, 10,000 iterations, and a feed-forward propagation algorithm. The 30-node configuration performed best, achieving a determination coefficient of 0.97 during training and 0.98 during testing, indicating high predictive accuracy. Findings The findings confirm that trends, online promotions, and brand image significantly influence purchase intention, demonstrating the model’s potential to optimize marketing strategies. By leveraging ANN, businesses can enhance marketing efficiency, adapt to market dynamics, and improve decision-making Implications This research highlights the effectiveness of AI-driven methodologies in analyzing consumer behavior and supporting targeted marketing efforts. The model’s success also suggests broader AI integration possibilities in strategic planning for the fashion industry Originality this study contributes to the literature by providing deeper insights into purchase intention formation and offers practical implications for improving marketing efficiency and strategic decision-making in sustainable fashion businesses