Predictive Modeling of Wildlife Trade Using the k-Nearest Neighbor Algorithm

Abstract

Wildlife trade conducted under suboptimal supervision can pose a significant threat to the long-term sustainability of biodiversity. Several animal species, including mammals and reptiles, are now facing extinction due to the lack of proper monitoring of wildlife trade. Wildlife trade practices are frequently found across various media platforms, including websites, due to the ease of access they offer to the public. However, if the data is processed manually, it requires considerable time, effort, and resources. To address this issue, an artificial intelligence-based approach is needed to help process wildlife trade data so that areas vulnerable to wildlife trafficking can be predicted quickly and accurately. Therefore, an artificial intelligence-based approach is needed to identify regions with high wildlife trade activity that are at risk of exceeding the permitted trade limits. This study aims to develop a model capable of predicting regions at risk of wildlife trade. The algorithms used in this study are the K-Means clustering algorithm to classify regions based on the risk level of wildlife trade areas, and the k-Nearest Neighbor algorithm to predict the risk level of wildlife trade areas. In addition, the Market Basket Analysis method is used to identify association patterns in wildlife trade between countries. The data used consists of wildlife trade data from various countries from 2018 to 2020. Using the clustering approach, this study classifies three levels of risk for wildlife trafficking: low, medium, and high. The results of the study show that the predictive model developed is capable of identifying areas vulnerable to wildlife trade, achieving a training accuracy of 99% with import data and 100% with export data. After being evaluated using 3-fold cross-validation, the model achieved an accuracy of 97% for import data and 98% for export data. The testing accuracy obtained in this study was 100% for both import and export data. Through the market basket analysis approach, this study concludes that, based on the data considered, no strong association patterns have been found in wildlife trade activities between specific countries.