Empirical Analysis of Influential Factors Affecting Domestic Workers’ Turnover Intention : Emphasis on Public Database and Decision Tree Method

2020 ◽  
Vol 22 (4) ◽  
pp. 41-58
Author(s):  
Geo Nu Ko ◽  
◽  
Hyun Jin Jo ◽  
Kun Chang Lee
Keyword(s):  
Decision Tree ◽  
Empirical Analysis ◽  
Turnover Intention ◽  
Domestic Workers ◽  
Influential Factors ◽  
Public Database ◽  
Factors Affecting ◽  
Decision Tree Method ◽  
Tree Method

scholarly journals Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office

2021 ◽  
Vol 2 (3) ◽  
pp. 64-68
Author(s):  
Ridlo Muttaqien ◽  
Musthofa Galih Pradana ◽  
Andri Pramuntadi
Keyword(s):  
Decision Tree ◽  
Customer Loyalty ◽  
Influential Factors ◽  
Marketing Strategies ◽  
Management Information ◽  
Experimental Application ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

PT Pegadaian (Persero) is engaged in the business of providing credit services with pawn, non-pawning and gold investment products. One of the right marketing strategies to survive today's high competition is to maintain customer loyalty, researchers use several data variables available in the MIS (Management Information System) in the form of customer transaction frequency, how many products are taken by customers, customer satisfaction and direct interviews. to predict customer loyalty of PT Pegadaian (Persero) by implementing the c4.5 algorithm. The c4.5 algorithm is the algorithm used to create a decision tree. Decision trees are a very powerful and well-known method of classification and prediction. The decision tree method converts very large facts into a decision tree that represents the rule. Rules can be easily understood in natural language. This study aims to determine the accuracy of the C4.5 algorithm to predict customer loyalty of PT Pegadaian (Persero) and the most influential factors in loyalty. The results of the experimental application of the c4.5 algorithm show that the level of accuracy generated in predicting customer loyalty is quite high, namely 89.94% in data testing 1 and 94% in data testing 2. The application of the c4.5 algorithm in predicting customer loyalty of PT Pegadaian (Persero) can be well applied.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

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