A Sustainable Perspective: Using Machine Learning Approach to Predict the Donation Behavior of Used Smartphones in Indonesia to Extend Smartphones Usage Life

Abstract

The growing issue of electronic waste (e-waste) in Indonesia, driven by the short lifecycle of smartphones and limited interest in refurbished devices, highlights the need for sustainable disposal alternatives. This study investigates the factors influencing Indonesians willingness to donate used smartphones, promoting e-waste reduction and digital access for underprivileged students in rural areas. Analyzing data from 416 respondents, we found that 57% expressed willingness to donate, with key factors including device obsolescence, age, and involvement in social activities. Machine learning models applied to predict donation behavior accurately predicting outcomes 91.67% of the time. The findings reveal the potential of functional but obsolete smartphones to address educational needs, offering a sustainable solution that bridges the digital divide and supports e-waste reduction. These insights guide strategies for social organizations to enhance donation programs, tackling both behavioral and logistical barriers for greater environmental and social impact