Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor

Tiska Pattiasina (Politeknik negeri Ambon)
Stenly Ronaldo Titioka (Politeknik Negeri Ambon)
Frangky Jansens Louth (Politeknik Negeri Ambon)
Grace Fredriksz (Politeknik Negeri Ambon)
Join Rachel Luturmas (Politeknik Negeri Ambon)
Andrie CH Salhuteru (Politeknik Negeri Ambon)
Febiola Matuankotta (Politeknik Negeri Ambon)
Laura S Nunumete (Politeknik Negeri Ambon)

Jurnal Teknologi Informasi Mura
Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.