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Nababan, Dinda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Klasifikasi Multikelas Varietas Kacang Kering Menggunakan Metode Hybrid SVM Berbasis DAG Nababan, Dinda; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14094

Abstract

This study analyzes the performance of three conventional SVM strategies, namely One-vs-One (OvO), One-vs-Rest (OvR), and Directed Acyclic Graph OvO (DAG-OvO), compared with the hybrid approach Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) in the context of multiclass classification using the Dry Bean Dataset. All models are evaluated based on accuracy and macro metrics to measure the consistency of predictions between classes. The results show that both conventional and hybrid methods achieve the same high level of accuracy, namely 0.92, with Precision, Recall, and F1-score Macro values ​​that were also identical between approaches. The main difference between the approaches lies in computational efficiency. OvO and DAG-OvO show the fastest training time, while DAG-RvR is the most efficient method in the inference stage. These findings confirm that the hybrid DAG-RvR structure can accelerate the prediction process without compromising accuracy, making it worthy of consideration for applications that require fast inference.
Co-Authors Christyan Tamaro Nadeak Farid, Fajri Rassiyanti, Linda