Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang)

Abstrak S alah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya tim dalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif. Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining. Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 ( 56,35% dan 55,03%). Kata kunci —classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit Abstract One of the causes of non-performing loans come from the internal , that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation( CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques . Preprocessed initial- credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm( 57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software( 56,35% and55,03 %, respectively). Keywords — classification based on association, CBA, data mining, classification, credit risk

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