Comparative Analysis of SVM Accuracy and Effectiveness on Weka and Google Colab for Job Recommendations - Rafika Sani; Nopa Wilyanita; Suparmi Suparmi; Tasya Maulidia (Universitas Malikussaleh, 2026)
Data Mining is a crucial technique for processing large and complex datasets to uncover hidden patterns that support strategic decision-making. This study evaluates the effectiveness of the Support Vector Machine (SVM) method implemented using Weka and Google Colab to classify employees into two categories: those recommended for promotion (positive class) and those not recommended (negative class). The research aims to compare the performance of both implementations in providing accurate and objective job recommendations. The dataset used in this study consists of employee performance evaluation records. The SVM model was trained and tested using different implementation platforms: Weka and Google Colab. In Weka, the model was configured with a polynomial kernel and achieved an accuracy of 99.62%, with precision, recall, and F-measure values of 1.000, 0.952, and 0.975 for the