Model Analisis Machine Learning dengan Pendekatan Deep Learning dalam Penentuan Kolektabilitas
DOI:
https://doi.org/10.23887/jstundiksha.v12i2.54035Keywords:
K-Mean Cluster, K- Nearest Neighbor (KNN), Decision Tree (DT)Abstract
Penelitian ini bertujuan untuk melakukan proses prediksi dan klasifikasi dalam pemberian status pinjaman dengan mengembangkan sebuah model analisis menggunakan Machine Learning (ML). Pembelajaran ML menggunakan pendekatan Deep Learning (DL) dengan beberapa metode diantaranya K-Means Cluster, K-Nearest Neighbor (K-NN) dan Decision Tree (DT). Metode K-Means mampu melakukan clusterisasi terhadap pengelompokan data nasabah. Kinerja KNN juga dapat berkontribusi untuk memberikan hasil prediksi yang tepat dan akurat. Demikian juga dengan performa DT yang mampu melakukan klasifikasi dengan menghasilkan knowladge based dalam penentuan status pinjaman. Adapun variabel analisis yakni usia, status perkawinan, jumlah tanggungan, status tempat tinggal, pendapatan. Hasil penelitian ini menyajikan bahwa keluaran prediksi K-NN memberikan ketepatan dengan akurasi sebesar 90%. Hasil DT juga telah mampu menyajikan knowladge based dalam bentuk pohon keputusan status pinjaman. Dengan hasil tersebut maka penelitian ini secara keseluruhan dapat memberikan kontribusi bagi pihak UPK dalam proses manajemen terkhusus dalam penentuan status peminjaman. Penelitian ini perlu dilakukan untuk memberikan kemudahan dalam memberikan kredit yang tepat sesuai dengan ketentuan yang telah ditetapkan.
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