Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm - Ihsan AlFauzi; Budiman Budiman; Nur Alamsyah (Universitas Bina Sarana Informatika, 2025)
Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotional attributes on TV, radio, and social media. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is used. Algorithms tested include Naive Bayes, K-Nearest Neighbor, Support Vector Machine (SVM), Random Forest, and XGBoost. The results showed that SVM had the best performance with 80% accuracy, followed by KNN (79%), Naive Bayes (77%), XGBoost (77%), and Random Forest (76%). SVM provided the most accurate and consistent predictions in marketing promotion classification. This research concludes that the optimal utilisation of ICT and the application of appropriate classification algorithms can increase the effectiveness of marketing promotions in the digital era.