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
Article Metrics:
Last update:
Prediction of Loan Pricing on the basis of Area Location using K-Nearest Neighbour and Support Vector Machine Learning Algorithms
Last update: 2024-04-23 15:07:20
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Media Statistika journal and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Media Statistika journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Media Statistika]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Di Asih I Maruddani (Editor-in-Chief) Editorial Office of Media StatistikaDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: maruddani@live.undip.ac.id
Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Gedung F Lantai 3, Jalan Prof Jacub Rais, Kampus Tembalang
Semarang 50275
Visitor Number:
Indexing:
