scholarly journals Data Mining Power Determination Nurses Sultan Sulaiman Hospital With C4.5 Algorithm

2020 ◽  
Vol 2 (1) ◽  
pp. 77-82
Author(s):  
Wenika Hidayati ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Health Services ◽  
Decision Tree ◽  
Regional Hospital ◽  
Data Mining Algorithm ◽  
Hospital Data ◽  
Mining Algorithm ◽  
C4.5 Algorithm ◽  
Existing Data

The hospital is an agency engaged in health services in the which there are a number of special professions that can provide health services to the community items, namely doctors, Midwives and nurses and other professes. In this discussion, Arise and problems that can be raised into case studies to find out the results and information of each process in data mining Carried out with the C4.5 algorithm items, namely nurses. However, there are Several obstacles to Determine the nurses who will be declared passed or failed and accepted to work and can provide health services to the community, especially Patients who come for treatment. Therefore we need a method to identify nurses in a hospital. Data Mining with c4. 5 Algorithm can be used to the make predictions or classifications of nurses who are eligible to perform health services in hospitals by making decision trees based on existing data. This study aims to apply the data mining algorithm C4.5 in Determining nurses based on four attributes of used items, namely Accreditation, GPA, Age, and the value of each criterion has been determined in advance. The results of the study in the form of a decision tree Obtained from the data mining process with the C4.5 algorithm will provide information on the determination of nurses in the Sultan Sulaiman Regional Hospital.

scholarly journals Penerapan Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C4.5

2018 ◽  
Author(s):  
Juna Eska
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Added Value ◽  
Data Mining Algorithm ◽  
Material Quality ◽  
Mining Algorithm ◽  
C4.5 Algorithm ◽  
Using Data ◽  
Wall Decoration ◽  
Price Algorithm

Wallpaper wallpaper or wallpaper wall is a wall decoration with a variety of motifs and colors. Wallpaper isused to change the appearance of a space to be more beautiful and has added value. Plain house walls tend tomake the occupants of the house feel bored because of the monotonous wall appearance. For that, having theinitiative to design the wall of the house with wallpaper into a bright idea that should be tried. Coloring thewalls of the house with wallpaper can add a beautiful impression on a room, so the room looks more expressive.Various motifs, colors, and wallpaper styles can be selected. Therefore, the seller must be more careful toprovide wallpaper which will be a lot of devotees, so it is necessary to recommend the type of wallpaper typeusing Classification method is done using data mining algorithm C4.5. data required is the best wallpaperbrand data, color, motif, material quality, size, and price. Algorithm C4.5 is a data classification algorithm oftype of decision tree. The decision tree The C4.5 algorithm is constructed with several stages including theselection of attributes as roots, creating branches for each value and dividing instances in branches. Thesestages will be repeated for each branch until all the cases on the branch have the same class. From thecompletion of the decision tree there will be some rules.

scholarly journals Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

2021 ◽  
Vol 22 (1) ◽  
pp. 476
Author(s):  
Rini Sovia ◽  
Abulwafa Muhammad ◽  
Syafri Arlis ◽  
Guslendra Guslendra ◽  
Sarjon Defit
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Medicinal Product ◽  
Pharmaceutical Products ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
Great Demand ◽  
Mining Algorithm ◽  
Using Data

<p>This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.</p>

scholarly journals Decision Tree: A Predictive Modeling Tool Used in Cloud Trust Prediction

2019 ◽  
Vol 8 (6) ◽  
pp. 4779-4783
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Modeling ◽  
Cloud Provider ◽  
Data Mining Algorithm ◽  
Trust Value ◽  
The Public ◽  
Mining Algorithm ◽  
Current Scenario ◽  
Reliable Basis

Trust is one of the important challenges faced by the cloud industry. Ever increasing data theft cases are contributing in worsening this issue. Regarding trust, author has a perception that this challenge can be handled to some extend if consumer can evaluate “Trust Value “ of the provider or can predict the same on some reliable basis. Current research is using predictive modeling for predicting trustworthiness of cloud provider. This paper is an attempt to utilize the data mining algorithm for predictive modeling. Decision Tree, a supervised data mining algorithm has been used in the current work for making predictions. Certification attainment criteria as prime basis for trust evaluation. In current scenario, data mining algorithm will classify providers in category of low, medium and high category of trust on the basis of information displayed on the public domain

scholarly journals Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks

2021 ◽  
Vol 25 (9) ◽  
pp. 1613-1616
Author(s):  
O.B. Alaba ◽  
E.O. Taiwo ◽  
O.A. Abass
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Model ◽  
Risk Model ◽  
Data Mining Algorithm ◽  
Tree Model ◽  
Mining Algorithm ◽  
Loan Risk ◽  
Structured Questionnaire ◽  
J48 Decision Tree

The focus of this paper is on the development of data mining algorithm for developing of predictive loan risk model for Nigerian banks. The model classifies and predicts the risk involved in granting loans to customers as either good or bad loan by collecting data based on J48 decision tree, BayesNet and Naïve Bayes algorithms for a period of ten (10) years (2010 2019) from using structured questionnaire. The formulation and simulation of the predictive model were carried out using Waikato Environment for Knowledge Analysis (WEKA) software. The performance of the three algorithms for predicting loan risk was done based on accuracy and error rate metrics. The study revealed that J48 decision tree model is the most efficient of all the three models.

Sign in / Sign up

Export Citation Format

Share Document