Customer Relationship Management Classification by Hybridizing Genetic Algorithm and Fuzzy K-Nearest Neighbor

2016 ◽  
Vol 147 (13) ◽  
pp. 13-17
Author(s):  
Jashandeep Kaur ◽  
Rekha Bhatia
Keyword(s):  
Genetic Algorithm ◽  
Customer Relationship Management ◽  
Nearest Neighbor ◽  
Relationship Management ◽  
Customer Relationship ◽  
K Nearest Neighbor

scholarly journals KOMPARASI AKURASI METODE K-NEAREST NEIGHBOR DAN C4.5 UNTUK CUSTOMER RELATIONSHIP MANAGEMENT PADA PERUSAHAAN PEMBIAYAAN

2020 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Mohamad Anas Sobarnas ◽  
Iskandar
Keyword(s):  
Customer Relationship Management ◽  
Nearest Neighbor ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Sampling ◽  
K Nearest Neighbor

Faktor utama pembiayaan bisnis adalah Layanan yang Mudah dan Cepat untuk pelanggan, Salah satu masalah perusahaan pembiayaan adalah kalah bersaing karena tidak adanya metode yang sistematis dalam menjaga hubungan yang baik dengan pelanggannya sehingga pelanggan tidak bertambah bahkan pelanggan lama pergi begitu saja. Salah satu metode dalam menjaga hubungan yang baik dengan pelanggan adalah dengan dibuatnya sistem komparasi untuk menguji tingkat keloyalan pelanggan, Sehingga pelanggan tetap loyal kepada Perusahaan. Adapun alat sebagai komparasi dalam aplikasi klasifikasi Pelanggan menggunakan algoritma Klasifikasi yaitu algoritma. K-Nearest Neighbor (K-NN) dan C4.5. Setelah dilakukan pengujian perbandingan kedua algoritma diatas ditemukan akurasi algoritma C4.5 sebesar 93.10% dan nilai akurasi model untuk algoritma KKN sebesar 90.52% dengan selisih akurasi 2,58%. Pengujian aplikasi berbasis web menggunakan bahasa program ASP.Net C # membandingkan hasil akurasi dari perbandingan kedua algoritma diatas maka algoritma (C4.5) adalah menghasilkan nilai yang terbaik, kemudian dilakukan pengujian data sampling keakuratan aplikasi dengan jumlah pengujian data 122 sample didapatkan hasil 109.8 sample akurat atau sebesar 92% dalam menentukan loyalitas klasifikasi pelanggan. Sehingga dengan adanya sistem aplikasi komparasi pelanggan berbasis web ini bisa menjadi alat bantu perusahaan dalam menguji loyalitas pelanggan pada perusahaan.

Combination classification method for customer relationship management

2019 ◽  
Vol 32 (5) ◽  
pp. 1004-1022
Author(s):  
Zhe Zhang ◽  
Yue Dai
Keyword(s):  
Genetic Algorithm ◽  
Decision Trees ◽  
Customer Relationship Management ◽  
Predictive Accuracy ◽  
Behavior Pattern ◽  
Relationship Management ◽  
Customer Relationship ◽  
Combination Method ◽  
Classification Problems ◽  
Content Type

Purpose For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm. Design/methodology/approach In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm. Findings The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results. Originality/value The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

A genetic-based data mining model for customer relationship management in B2B ecommerce

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AS Makinde ◽  
OR Vincent ◽  
ID Acheme ◽  
AT Akinwale
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Calibration Graph ◽  
Customer Relationship ◽  
Business Strategies ◽  
Support Vector ◽  
Proposed Model ◽  
Time Calibration

Customer Relationship Management (CRM) improves the responsiveness and understanding of business strategies among employees and helps to achieve better customer service. However, in Business-to-Business (B2B) eCommerce, CRM data are rarely analyzed across market segments or customer categories, and customer-to-business relationship also poses issues. Thus, making appropriate decisions in the CRM model is difficult. This study presents a modified B2B CRM using the Genetic algorithm and Data Mining Techniques to improve decision making. The model classifies consumers into consumers of Repeat and Shop-and-Go. Modified data mining C5.0 and the Genetic algorithm was employed to optimize rules generated by the decision tree algorithm. The findings showed that the proposed model allocates resources effectively to the most profitable customers’ decisions. The output metrics are machine time, calibration graph, and ROC curve. In comparison with the conventional C5.0, k-NN, and Support Vector Machine, the proposed model has greater accuracy of 89.3 percent. Keywords: Customer Relationship Management, B2B eCommerce, Genetic Algorithm, Data Mining

Research on Hotel Customer Relationship Management System Based on the Classification Algorithm

2019 ◽  
Vol 12 (2) ◽  
pp. 68-75
Author(s):  
Zhao Weili
Keyword(s):  
Support Vector Machine ◽  
Customer Relationship Management ◽  
Management System ◽  
Nearest Neighbor ◽  
Relationship Management ◽  
Classification Algorithm ◽  
Customer Relationship ◽  
Support Vector ◽  
Customer Acquisition ◽  
Fuzzy Support Vector Machine

The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is used as the research object, and a customer acquisition analysis is carried out, and the results show that the new method has quicker training speed and higher classification correctness.

Implementasi Sistem Rekam Medis Pasien Menggunakan Pendekatan Customer Relationship Management (CRM)

2018 ◽  
Vol 7 (2) ◽  
pp. 180
Author(s):  
Wiyanto Wiyanto ◽  
Fajar Butsianto ◽  
Karsito Karsito
Keyword(s):  
Information Technology ◽  
Information System ◽  
Medical Record ◽  
Customer Relationship Management ◽  
Unified Modeling Language ◽  
Relationship Management ◽  
Customer Relationship ◽  
Design System ◽  
System Validation ◽  
Iso 9126

Information technology is rapidly developed in this century that impact to various aspects of the organization really need information technology to support the performance and everyday business processes. In health services, information technology is required to process and storage the patient medical records, so that the patient's medical record is well preserved, and competitive advantage can be obtained between patient and polyclinic. The application of Customer Relationship Management (CRM) approach can be developed by implementing information system of medical record history to get new patient and retain existing patient, improving relationship with patient and maintaining patient loyalty as well as supporting the company/organization to provide excellent service to customers in real time through the advantage of information technology. The aims of this research are to understand patient medical record by CRM approach and Unified Modeling Language (UML) for system design, system validation using Forum Group Discussion (FGD), and using software testing Model ISO 9126. The result of this research are Medical Record History Information System and the result of system validation with FGD is 100% accepted, the result of system test using Model ISO 9126 is good with success rate 82,86%, so it can give contribution to polyclinic.

Customer Relationship Management

2001 ◽  
Vol 30 (8) ◽  
pp. 417-422 ◽  
Author(s):  
Hajo Hippner ◽  
Stephan Martin ◽  
Klaus D. Wilde
Keyword(s):  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship
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