DAMPAK GENERATIF AI PADA PASAR KERJA IT DENGAN DEEP LEARNING: ANALISIS KOMPARATIF BERT DAN BILSTM
Keywords:
Deep Learning, Analisis Pasar Kerja, BERT, LSTM, Skor Disrufsi AIAbstract
Difusi kecerdasan buatan (AI) secara fundamental merestrukturisasi pasar kerja teknologi informasi (TI) global. Makalah ini menyajikan kerangka deep learning adaptif untuk klasifikasi pekerjaan TI menggunakan dataset multi-sumber selama lima tahun (April 2021–April 2026). Proses reduksi data secara sistematis menyaring 780.000 data mentah menjadi 20.000 catatan representatif dan selanjutnya menjadi 4.879 sampel terdeduplikasi dengan validitas temporal bulanan yang terverifikasi. Kami membandingkan performa jaringan Bidirectional Long Short-Term Memory (BiLSTM) dengan Deep Neural Network (DNN) berbasis Sentence-BERT. Hasil eksperimen menunjukkan DNN+BERT mencapai akurasi 87,8±0,9%, mengungguli BiLSTM (80,3±1,2%) dengan signifikansi statistik (McNemar χ²=41,3, p<0,001). Penelitian ini juga mengusulkan metrik AI Disruption Score (ADS) untuk mengkuantifikasi keterpaparan AI di tingkat pekerjaan, yang berhasil memetakan enam klaster pekerjaan melalui K-Means, dengan nilai disrupsi antara 0,02 (DevOps) hingga 0,58 (Riset AI/ML). Meskipun metrik ADS ini masih memerlukan validasi eksternal lebih lanjut terhadap data pasar industri yang lebih luas, secara praktis, temuan ini memberikan panduan strategis bagi praktisi HR dalam perencanaan tenaga kerja dan bagi institusi pendidikan dalam reorientasi kurikulum TI yang adaptif.
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