<![CDATA[The development of the digital industry in Indonesia has driven an increasing demand for professional workers in the information technology (IT) sector. Along with this, the need arises to understand and map salary levels based on job profiles to create transparency and efficiency in the recruitment process. This study aims to predict the salary categories of IT professionals using the Support Vector Machine (SVM) method in well-known marketplace companies such as Gojek, Shopee, Tokopedia, Traveloka, Tiket.Com and Bukalapak. The dataset used contains 611 data entry records with attributes of company, work location, experience and skills as well as salary. The preprocessing process consists of label encoding, numeric normalization, and multi-hot encoding for skill features. The salary categories are divided into three: low, medium, and high. The SVM model is trained with the Radial Basis Function (RBF) kernel and evaluated with accuracy, precision, recall, and f1-score metrics. The evaluation results show that the SVM model is able to classify salary categories with an accuracy of 82%. This model shows the best performance in the Medium salary category with an f1-score of 0.93. This study proves that SVM can be used as an alternative to build an effective IT Salary Category prediction system.]]>
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