BibTex Citation Data :
@article{Medstat20591, author = {Tatik Widiharih and Moch Mukid}, title = {CREDIT SCORING MENGGUNAKAN METODE LOCAL MEANS BASED K HARMONIC NEAREST NEIGHBOR (MLMKHNN)}, journal = {MEDIA STATISTIKA}, volume = {11}, number = {2}, year = {2018}, keywords = {}, abstract = { Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method. Keywords : sampling design, all possible samples, statistical efficiency , cost efficienc y }, issn = {2477-0647}, pages = {107--117} doi = {10.14710/medstat.11.2.107-117}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20591} }
Refworks Citation Data :
Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method.
Keywords: sampling design, all possible samples, statistical efficiency, cost efficiency
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