PEMANFAATAN NATURAL LANGUAGE PROCESSING (NLP) UNTUK ANLISIS SENTIMEN DALAM EVALUASI RISIKO KREDIT UTILIZATION OF NATURAL LANGUAGE PROCESSING (NLP) FOR SENTIMENT ANALYSIS IN CREDIT RISK EVALUATION
Abstract
Penelitian ini mengkaji pemanfaatan Natural Language Processing (NLP) dalam analisis sentimen untuk meningkatkan akurasi evaluasi risiko kredit di Indonesia, khususnya pada sektor UMKM dan informal yang minim data struktural. Studi dilakukan melalui systematic literature review (SLR) terhadap 40 jurnal terpilih periode 2008–2025 dari Google Scholar, IEEE Xplore, ScienceDirect, Scopus, dan ACM Digital Library. Hasil kajian menunjukkan bahwa model berbasis transformer seperti BERT dan LSTM meningkatkan akurasi prediksi risiko gagal bayar hingga 89% dengan menggabungkan data tidak terstruktur (media sosial, transkrip wawancara) dan data kuantitatif. Studi kasus di Bank BCA dan fintech Validus membuktikan penurunan false positive sebesar 30% dan peningkatan presisi skoring kredit sebesar 25%. Namun, implementasi NLP menghadapi tantangan seperti variasi dialek, slang bahasa Indonesia, bias linguistik (misal overestimasi risiko usaha perempuan), serta isu etika data pribadi. Biaya implementasi tinggi (rata-rata USD 150.000 per institusi) dan rendahnya literasi digital di pedesaan juga menjadi hambatan. Solusi yang diusulkan meliputi pengembangan model NLP khusus sektor keuangan, pelatihan data representatif, kolaborasi multidisiplin, serta adopsi federated learning. Implikasi penelitian ini menekankan perlunya penguatan regulasi etika, peningkatan literasi digital UMKM, dan pengembangan model hybrid yang hemat biaya.
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