Analisa Performa Metode LightGBM untuk Prediksi Kecanduan Media Sosial 380 347
DOI:
https://doi.org/10.26623/transformatika.v23i2.13165Keywords:
Lightgbm, Kecanduan Media Sosial, Pembelajaran Mesin, Prediksi, Pemilihan FiturAbstract
Social media has now become an integral part of daily activities, driven by the increasingly rapid development of digital technology. Excessive social media use can trigger negative impacts such as psychological disorders, sleep deprivation, and social conflict. This study assesses the effectiveness of the Light Gradient Boosting Machine (LightGBM) in predicting social media addiction using data from 705 respondents from Kaggle. The analysis stages included data cleaning, categorical variable transformation, and feature selection based on Pearson correlation. The model was trained with a 70:30 ratio and evaluated using accuracy, precision, recall, and f1-score. The results showed 98% accuracy, thus LightGBM is considered highly effective as a social media addiction prediction model.
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