scholarly journals Penerapan Metode Algoritma C4.5 dalam Menganalisa Pegajuan Kredit pada Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan

2018 ◽  
Vol 7 (1) ◽  
pp. 28-42
Author(s):  
Febri Hadi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Financial Services ◽  
Rapid Development ◽  
Simple Representation ◽  
Classification Technique ◽  
C4.5 Algorithm ◽  
Processing Techniques ◽  
Credit Analysis

The development of data processing techniques at this time has experienced rapid development. The Decision tree is a simple representation of a classification technique that is the process of teaching a function of purpose that maps each set of first attributes of a class defined previously. The decision tree can determine the hidden relationship between a number of potential target variables. In lending to customers, credit analysis is required for lending. The analysis of the kerdit can be done by utilizing data mining in the form of C4.5 algorithm. The C4.5 algorithm is used to provide credit decisions in order for the Sharia financial services cooperative to quickly analyze the credit application by members of a cooperative. The purpose of this research is to apply C4.5 algorithm method in analyzing credit application at Koperasi Jasa Keuangan Syariah Kelurahan Limau Manis Selatan.

scholarly journals Penerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro

2019 ◽  
Vol 5 (1) ◽  
pp. 75-86
Author(s):  
Farid Fadli ◽  
Belsana Butar Butar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Death Rates ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Tree Data ◽  
Classification Category ◽  
Tree Method

Abstract: According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category. Keywords: Algorithm C4.5, Decision Tree, Data Mining

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

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 PENERAPAN ALGORITMA C4.5 UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT (Studi Kasus : Koperia - Koperasi Warga Komplek Gandaria)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Teguh Budi Santoso ◽  
Dela Sekardiana
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Model ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Credit Worthiness ◽  
Loan Amount

<p><em>Current credit giving in KOPERIA (Koperasi Warga Komplek Gandaria) is still based on an objective process. Difficulties in determining the feasibility of giving credit are often experienced by cooperative managers, so that problems arise in the cooperative is a default payment of credit installments of customers in KOPERIA. This study aims to form a decision tree classification model to determine the customer's credit worthiness. In this study the application of C4.5 Algorithm, based on the Sets and Attributes used in this study, namely, the amount of income divided into 2 categories&gt; 5 million and 3-5 million, the amount of balance divided into three, namely&gt; 3 million, 1-3 million and &lt;1 Million, The Loan Amount is divided into three, namely 1-4 Months, 5-8 months, and 9-12 Months and Requirements with attributes of Business Capital, buying goods and others. In this study determine the appropriate root nodes, the classification results using C4.5 Algorithm shows that the accuracy of 97.5% is obtained, based on the results obtained shows that the c4.5 algorithm is suitable to be used to determine the feasibility of lending customers to KOPERIA.</em></p><p><strong><em>Keywords</em></strong><em>: Data Mining, C4.5 Algorithm</em><em>, loan feasibility</em></p>

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

A Hybrid Classification Approach Based on Decision Tree and Naïve Bays Methods

2014 ◽  
Vol 4 (4) ◽  
pp. 61-72
Author(s):  
Saed A. Muqasqas ◽  
Qasem A. Al Radaideh ◽  
Bilal A. Abul-Huda
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Classification Methods ◽  
Hybrid Classifier ◽  
Classification Approach ◽  
Classification Technique ◽  
Average Accuracy ◽  
Proposed Model ◽  
Hybrid Classification

Data classification as one of the main tasks of data mining has an important role in many fields. Classification techniques differ mainly in the accuracy of their models, which depends on the method adopted during the learning phase. Several researchers attempted to enhance the classification accuracy by combining different classification methods in the same learning process; resulting in a hybrid-based classifier. In this paper, the authors propose and build a hybrid classifier technique based on Naïve Bayes and C4.5 classifiers. The main goal of the proposed model is to reduce the complexity of the NBTree technique, which is a well known hybrid classification technique, and to improve the overall classification accuracy. Thirty six samples of UCI datasets were used in evaluation. Results have shown that the proposed technique significantly outperforms the NBTree technique and some other classifiers proposed in the literature in term of classification accuracy. The proposed classification approach yields an overall average accuracy equal to 85.70% over the 36 datasets.

The Analysis and Application of the C4.5 Algorithm in Decision Tree Technology

2012 ◽  
Vol 457-458 ◽  
pp. 754-757
Author(s):  
Hong Yan Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Teaching Quality ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Result Analysis

The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree’s concept, the C4.5 Algorithm and the construction of The Decision Tree, the using of C4.5 Decision Tree Algorithm was applied to result analysis of students’ score for the purpose of improving the teaching quality.

scholarly journals Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

2021 ◽  
Vol 5 (3) ◽  
pp. 1166
Author(s):  
Muchamad Sobri Sungkar ◽  
M Taufik Qurohman
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Extraction Process ◽  
Study Program ◽  
Data Set ◽  
C4.5 Algorithm ◽  
Previous Algorithm ◽  
Process Prediction ◽  
Computer System Architecture

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

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