Anik Vega Vitianingsih, Zahriah Othman, Safiza Suhana Kamal Baharin, Aji Suraji, Anastasia Lidya Maukar
The difficulty of acquiring data from numerous intergovernmental agencies/institutions for prone road traffic accidents (PRTA) spatial datasets produces a small-scale dataset that causes dataset imbalance. Class imbalance in small-scale datasets causes uncertainty in the results of the modeling PRTA classification. The proposed research is a scenario-based case representation model on the pre-processing data stage to increase the sensitivity of algorithm classification in a small-scale dataset that causes dataset imbalance using machine learning (ML), the synthetic oversampling method. The retrieval of attributes from the spatial dataset is transformed into the raw dataset, the normalized dataset, the synthetic minority over-sampling technique (SMOTE) raw dataset, and SMOTE normalized dataset scenarios. Balancing datasets using four variants of SMOTE, namely ADASYN, Borderline-SMOTE, K-Means SMOTE, and SVM-SMOTE resampled. To evaluate how well the PRTA classification model performed, we utilized the hyper-parameters optimization technique and the genetic algorithm (GA) search cross-validation. The experiment was run with the ML classifier method, including the k-nearest neighbor (KNN), support vector machines (SVM), multilayer perceptron (MLP), naive bayes (NB), logistic regression (LR), and random forest (RF). The Area Under Curve (AUC) was used to evaluate the results of the experiments. The results of the dataset test in a predetermined scenario conclude that a single algorithm that is computationally light to produce an optimal classifier tends to use a raw dataset that is balanced using SMOTE. The KNN method as a single algorithm for classification based on the distance between samples is superior, with an AUC value of 0.89, which is included in the good classification category of all ML classifiers proposed to handle small data sets imbalanced classes using SMOTE raw datasets for K-Means variants SMOTE. © 2022,International Journal of Intelligent Engineering and Systems.All Rights Reserved
Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Department of Civil Engineering, University of Widyagama Malang, Malang, Indonesia; Industrial Engineering Department, President University, Bekasi, Indonesia
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