Adaptive Learning Berbasis Artificial Intelligence dalam Pendidikan Sains: Personalisasi Pembelajaran dan Literasi Sains
DOI:
https://doi.org/10.66031/reset.v2i1.683Keywords:
Machine Learning, Financial Risk, Predictive Analytics, Model Interpretability, AI GovernanceAbstract
Transformasi digital telah meningkatkan kompleksitas risiko keuangan sekaligus menghasilkan volume dan keragaman data yang semakin besar, sehingga pendekatan prediksi berbasis metode konvensional menghadapi keterbatasan dalam menangkap pola risiko yang dinamis dan nonlinier. Machine learning (ML) menawarkan kapasitas analitik untuk mengidentifikasi pola, mengklasifikasikan risiko, dan menghasilkan prediksi secara lebih adaptif. Artikel ini bertujuan melakukan tinjauan kritis terhadap penerapan ML sebagai teknologi cerdas dalam prediksi risiko keuangan dengan menganalisis domain penerapan, pendekatan algoritmik, manfaat prediktif, serta keterbatasan terkait kualitas data, bias, generalisasi, interpretabilitas, keamanan, dan tata kelola. Kajian dilakukan melalui literature review terhadap publikasi tahun 2020–2026 yang diperoleh dari beberapa basis data akademik dan dianalisis secara tematik berdasarkan fokus, pendekatan, serta kontribusi penelitian. Hasil sintesis menunjukkan bahwa ML memiliki keunggulan dalam menangani data kompleks dan mendukung prediksi risiko kredit, financial distress, kebangkrutan, serta risiko pada ekosistem keuangan digital. Namun, keunggulan prediktif tidak secara otomatis menjamin reliabilitas model karena dipengaruhi kualitas data, data drift, bias, keterbatasan interpretabilitas, keamanan informasi, dan tata kelola. Kajian ini menegaskan perlunya evaluasi ML yang mengintegrasikan performa prediktif, robustness, interpretabilitas, keamanan, dan akuntabilitas sebagai dasar penerapan yang bertanggung jawab.
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All data generated or analyzed during this study are included in this published article. Additional datasets are available from the corresponding author upon reasonable request.
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