Comparative Analysis of SVM Accuracy and Effectiveness on Weka and Google Colab for Job Recommendations - Rafika Sani; Nopa Wilyanita; Suparmi Suparmi; Tasya Maulidia (Sisfo: Jurnal Ilmiah Sistem Informasi, 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 "recommended" class and 0.996, 1.000, and 0.998 for the "not recommended" class. Meanwhile, the implementation in Google Colab, using the LIBSVM library with a polynomial kernel, produced an accuracy of 97.85%, with precision, recall, and F-measure values of 0.984, 0.932, and 0.957 for the "recommended" class and 0.978, 0.986, and 0.982 for the "not recommended" class. The comparison results indicate that the Weka implementation provides slightly higher accuracy and better classification performance. However, Google Colab offers more flexibility and scalability, making it suitable for handling larger datasets. The findings of this study highlight the potential of SVM as a reliable tool for employee performance evaluation and job promotion recommendations. The use of machine learning in human resource management can enhance decision-making processes, ensuring fairness and efficiency in personnel assessments.