<![CDATA[Digital transformation has reshaped the labor market, with online platforms such as Glints serving as large-scale data repositories that connect job seekers with employers. In the Greater Jakarta (Jabodetabek) region, salary information is a critical factor in career decision-making; however, salary-related information asymmetry remains a major challenge. This study begins with a descriptive analysis of 1,497 job vacancies collected through web scraping techniques to examine salary distributions across locations and employment statuses. The salaries were classified into three categories: low salary (≤ IDR 5 million), medium salary (IDR 5–10 million), and high salary (≥ IDR 10 million). The results indicate that the majority of job vacancies fall into the low-salary category (77.09%), followed by the medium-salary category (21.37%), while high-salary positions constitute only 1.54% of the total dataset. Subsequently, this study aims to develop salary category classification models by comparing two machine learning methods: Logistic Regression and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score under multiple training–testing split scenarios. The experimental results demonstrate that Random Forest consistently outperforms Logistic Regression, achieving a highest accuracy of 98.00%, compared to approximately 79% for Logistic Regression. These findings suggest that the relationship between job characteristics and salary categories is complex and non-linear, making it more effectively captured by ensemble-based, non-linear algorithms such as Random Forest. This study contributes to improving salary transparency and supports the development of more accurate and data-driven salary prediction systems.]]>
