Application of the DAG-SVM for multi-class mobile phone price classification - Sihombing, Natanael Oktavianus Partahan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid (Desimal: Jurnal Matematika, 2025)
This study investigated the application of multiclass Support Vector Machine (SVM) strategies for smartphone price range classification using the Mobile Price Classification dataset (N = 2,000). The aim was to assess whether the Directed Acyclic Graph SVM (DAG-SVM) could provide improvements in predictive performance or computational efficiency compared with the conventional One-vs-One (OvO) and One-vs-Rest (OvR) approaches. The dataset’s twenty features were standardized using Z-score normalization and split into training and testing sets with an 80:20 ratio. All models were implemented using a linear kernel and evaluated based on accuracy, macro-precision, macro-recall, macro-F1, and execution time. The results showed that both OvO and DAG-SVM achieved the highest performance, with an accuracy and macro-F1 score of 96.25%, while OvR performed substantially lower. Despite the theoretical efficiency of DAG-SVM, its Python-based sequential elimination process led to slower prediction time than OvO. This study contributed empirical evidence that execution time can diverge from theoretical expectations in practical implementations and demonstrated the importance of computational efficiency analysis when comparing multiclass SVM architectures for mobile price classification.