Identifikasi Keterampilan Digital dalam Iklan Lowongan Kerja Menggunakan Klasifikasi Teks dan Named Entity Recognition - Geraldy, Handy; Farentina, Rizka Amalia; Dirk, Fransisca Angelina (Politeknik Statistika STIS, 2025)
Technological advancements have significantly reshaped the nature of work. A survey conducted by APINDO indicates that technology adoption contributed to elevated layoff rates during January-March 2025. Meanwhile, McKinsey & Company states that Indonesia will need 9 million digital talents (2014-2030). This study maps digital talent demand by classifying job vacancies data from Jobstreet based on digital skill levels (digital, semi-digital, and non-digital) and identifying the most frequently mentioned digital skills. The XGBoost achieves the best performance with an F1-score of 94.33%, outperforming SVM, logistic regression, and random forest. The study has provided an overview of job vacancy classifications based on the level of digital skills required. The XGBoost results indicate that 53,1% of job vacancies are classified as non-digital jobs. Furthermore, the NER model successfully identified skill entities in digital job vacancies, revealed that “communication”, “problem solving”, “software”, “design”, “SQL”, and “programming” were the most frequently mentioned skills.