This study aims to predict the relevance of international certification programs in
the field of Information Technology (IT) over the next five years using a machine learning
approach. Data were collected through questionnaires distributed to students, alumni,
and lecturers within the ITCC ITPLN environment, then processed through several
stages, including preprocessing, model training, evaluation, and prediction visualization.
The Decision Tree (C4.5) algorithm was employed due to its ability to generate
interpretable classification models through decision trees. The average respondent score
was used as the classification basis, with a threshold of ≥ 3.5 categorized as “Relevant”
and < 3.5 as “Not Relevant.” The results indicate that the majority of respondents
perceive certification programs as still relevant to current and future industry needs, with
the model achieving an accuracy level above 90%. These findings are further supported
by external data such as government reports and scientific publications projecting a
demand for more than 9 million digital talents in Indonesia by 2030. This study
emphasizes that international certification remains an essential instrument to enhance
human resource competencies and competitiveness in the digital era. The research is
expected to provide practical contributions for ITCC ITPLN in developing adaptive
certification strategies aligned with industry dynamics, while also serving as an academic
reference in applying the Decision Tree algorithm for data-driven predictive analysis.