<![CDATA[The rapid growth of the Artificial Intelligence (AI) industry has driven a high global demand for AI-based workers. However, salary information in some countries is still presented in USD, making it not directly relevant to workers in Indonesia, who have a different purchasing power context. This study aims to classify AI job salary levels within the Indonesian purchasing power context by applying Purchasing Power Parity (PPP) conversion using the Naive Bayes algorithm. The dataset was obtained from Kaggle and is titled Global AI Job Market Salary Trends 2025, containing 15,000 AI job vacancy records from 20 countries. The preprocessing steps included salary conversion based on the 2025 World Bank PPP value (IDR 4,721 per international dollar), encoding, StandardScaler, MultiLabelBinarizer for 24 technical skill features, and SMOTE applied to the training data, resulting in a total of 38 features. The model was evaluated using accuracy, precision, recall, and macro F1-score metrics. The Gaussian Naive Bayes model achieved an accuracy of 82.63% and a macro F1-score of 0.71. Analysis of the dominant factors identified company location, employee location, the PPP value of the country of origin, and years of work experience as the factors that most influenced AI job salary levels. This study demonstrates that PPP-based AI job salary level classification using Naive Bayes can support labor market analysis and career decision-making in Indonesia.Keywords: Salary Classification, Artificial Intelligence Jobs, Naive Bayes, Purchasing Power Parity, SMOTE]]>
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