Automatic classification of insulator by combining k-nearest neighbor algorithm with multi-type feature for the Internet of Things

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

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Classification of Lower Back Pain Using K-Nearest Neighbor Algorithm

2018 6th International Conference on Cyber and IT Service Management (CITSM) ◽

10.1109/citsm.2018.8674361 ◽

2018 ◽

Cited By ~ 1

Author(s):

Green Arther Sandag ◽

Natalia Elisabet Tedry ◽

Steven Lolong

Keyword(s):

Back Pain ◽

Lower Back Pain ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Lower Back ◽

K Nearest Neighbor Algorithm

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Identification and Classification of Technical Lignins by means of Principle Component Analysis and k‐Nearest Neighbor Algorithm

Chemistry–Methods ◽

10.1002/cmtd.202100028 ◽

2021 ◽

Author(s):

Friedrich Fink ◽

Franziska Emmerling ◽

Jana Falkenhagen

Keyword(s):

Principle Component Analysis ◽

Nearest Neighbor ◽

Component Analysis ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Principle Component ◽

K Nearest Neighbor Algorithm ◽

Technical Lignins

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Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double)

Journal Of Computer Networks, Architecture and High Performance Computing ◽

10.47709/cnapc.v2i2.401 ◽

2020 ◽

Vol 2 (2) ◽

pp. 204-210

Author(s):

Jeprianto Sinaga ◽

Bosker Sinaga

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Savings And Loans ◽

Long Time ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

The Many

Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.

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