Cooperatives are a forum that can help people, especially small and medium-sized communities. Cooperatives play an important role in the economic growth of the community such as the price of basic commodities which are relatively cheap and there are also cooperatives that offer borrowing and storing money for the community. Constraints that have been felt by this cooperative are that borrowers find it difficult to repay loan installments, causing bad credit. Because the cooperative in conducting credit analysis is carried out in a personal manner, namely by filling out the loan application form along with the requirements and conducting a field survey. Therefore there is a need for an evaluation to be carried out in lending to borrowers. To minimize these problems, it is necessary to detect customer criteria that are used to predict bad loans and to determine whether or not the elites are eligible to take credit using data mining. The data mining technique used is classification with the Naive Bayes method. Based on testing the accuracy of the resulting model obtained accuracy level of 59%, sensitivity (True Positive Rate (TP Rate) or Recall) of 46.80%, specificity (False Negative Rate (FN Rate or Precision) of 69.81%, Positive Predictive Value (PPV) of 57.89%, and Negative Predictive Value (NPV) of 59.67%.Abstrak-Koperasi merupakan suatu wadah yang dapat membantu masyarakat terutama masyarakat kecil dan menengah. Koperasi memegang peranan penting dalam pertumbuhan ekonomi masyarakat seperti harga bahan pokok yang tergolong murah dan juga ada koperasi yang menawarkan peminjaman dan penyimpanan uang untuk masyarakat. Kendala yang pernah di rasakan oleh koperasi ini adalah peminjam susah untuk membayar angsuran pinjaman sehingga menyebabkan terjadinya kredit macet. Karena pada koperasi dalam melakukan analisa pemberian kredit dilakukan secara personal, yaitu dengan cara mengisi lembar formulir permohonan peminjaman kredit disertai dengan persyaratan dan melakukan survey lapangan. Oleh karena itu perlu adanya evaluasi yang dilakukan dalam pemberian kredit kepada para peminjam. Untuk meminimalisir permasalahan tersebut perlu dilakukan pendeteksian kriteria-kriteria nasabah yang digunakan untuk memprediksi kredit macet serta untuk menentukan layak atau tidaknya peminja m dalam pengambilan kredit dengan menggunakan data mining. Teknik data mining yang digunakan adalah klasifikasi dengan metode naive bayes. Berdasarkan pengujian akurasi dari model yang dihasilkan diperoleh tingkat accuracy sebesar 59%, sensitivity (True Positive Rate (TP Rate) or Recall) sebesar 46,80%, specificity (False Negative Rate (FN Rate or Precision) sebesar 69,81%, Positive Predictive Value (PPV) sebesar 57,89%, dan Negative Predictive Value (NPV) sebesar 59,67%. Kata Kunci-Data Mining, Kredit Macet, Naive Bayes, Prediksi. I. PENDAHULUANKoperasi merupakan suatu wadah yang dapat membantu masyarakat terutama masyarakat kecil dan menengah. Koperasi memegang peranan penting dalam pertumbuhan ekonomi masyarakat seperti harga bahan pokok yang tergolo...
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