Classification of Tensile Test Behavior of Gnetum gnemon Fiber Structure Using Machine Learning-Based Support Vector Machine
- Hermanus Jemy Suripatty — Satya Wiyata University, Mandala, Nabire, Central Papua, Indonesia, Indonesia
- S Suryadi — Satya Wiyata University, Mandala, Nabire, Central Papua, Indonesia, Indonesia
- Nicodemus Rahanra — Satya Wiyata University, Mandala, Nabire, Central Papua, Indonesia, Indonesia
- Wardhana Wahyu Dharsono — Satya Wiyata University, Mandala, Nabire, Central Papua, Indonesia, Indonesia
- Publication History
- Published online: August 31, 2026
- DOI
- https://doi.org/10.35877/454RI.jinav4954
- Copyright
- Copyright (c) 2026 Hermanus Jemy Suripatty, S Suryadi, Nicodemus Rahanra, Wardhana Wahyu Dharsono
- User License
- https://creativecommons.org/licenses/by-nc-sa/4.0
Abstract
Tensile testing is a widely used method for evaluating the mechanical properties of materials by analyzing the relationship between force and displacement. However, the interpretation of tensile test curves is still largely performed manually, which may introduce subjectivity when distinguishing the mechanical behavior of different specimens. This study aims to classify the tensile behavior of Gnetum gnemon fiber structures using a Support Vector Machine (SVM) algorithm based on machine learning. The dataset consisted of nine specimens categorized into three structural configurations: 1×1, 2×1, and 2×2. The force–displacement curves obtained from the tensile tests were transformed into six mechanical parameters: maximum force, displacement at maximum force, final displacement, area under the curve, initial stiffness, and secant stiffness. All features were standardized using the StandardScaler method prior to classification with a linear-kernel SVM. Model performance was evaluated using the Leave-One-Out Cross Validation (LOOCV) approach. The results demonstrated that the proposed model achieved an accuracy of 88.89%, with a precision of 91.67%, a recall of 88.89%, and an F1-score of 88.57%. The classification report and confusion matrix indicated that most specimens were correctly classified into their respective structural configurations, with only one misclassification occurring in the 2×2 configuration. These findings demonstrate that the integration of mechanical feature extraction with the SVM algorithm effectively distinguishes the tensile behavior of Gnetum gnemon fiber structures and has strong potential as an alternative approach for the analysis and classification of the mechanical characteristics of natural fiber-based materials.
Keywords
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