scholarly journals Pengembangan Aplikasi Perhitungan Prediksi Stock Motor Menggunakan Algoritma C 4.5 Sebagai Bagian dari Sistem Pengambilan Keputusan (Studi Kasus di Saudara Motor)

2018 ◽  
Vol 3 (1) ◽  
pp. 24
Author(s):  
Pandu Pratama Putra ◽  
Andi Supriadi Chan
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Decision Tree ◽  
Decision Process ◽  
Corporate Tax ◽  
Calculation Algorithm ◽  
Processing Data ◽  
Limited Company ◽  
C4.5 Algorithm ◽  
Partition Process

Limited company resources must be managed effectively and efficiently in order to guarantee corporate tax information technology information that will assist management within the company for every necessary process and reliable information. C 4.5 can be used to predict motor stock at Brothers Motor. the learning process that existed in the algorithm C 4.5 is preparing the data traning, calculate the root of the tree, and the partition process of the decision. The decision process will stop when all the records are in the N node that gets the same class, there are no attributes in the record that are partitioned again, there is no record in the empty network. The result of the average calculation algorithm algorithm C 4. Then the results of the calculation can be used for data processing. Data Mining uses the C4.5 algorithm then obtains a decision tree. Produce data obtained 12 rules (rules) in determining the prediction of preparation of motor stocks at Brothers Motor dealers.Key word: dss, algortm c 5.4,motorbike, dealer

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

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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.

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%

scholarly journals Palm oil extraction rate prediction based on the fruit ripeness levels using C4.5 algorithm

2021 ◽  
Vol 13 (2) ◽  
pp. 92-100
Author(s):  
Wahyu Supriyatin
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Palm Oil ◽  
Oil Extraction ◽  
Palm Fruit ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Production Value ◽  
High Production

Oil palm plantations are one of the main keys in supporting Indonesia’s economic growth. The rising consumption needs for palm oil products make it necessary to carry out data mining activities to increase CPO production. The maturity factor of palm fruit dramatically affects the quality of the oil extraction content (CPO yield) produced. This study aims to investigate the effect of fruit ripeness on the yield of CPO by using a data mining classification method with a decision tree. The algorithm used to generate decision tree classification is the C4.5 algorithm. The implementation of the C4.5 algorithm in the study was carried out using the Rapid Miner Studio 5.2 tools. The results shows that the yield of CPO is influenced by the attributes of the condition of the long and ripe fruit, the condition of the long and overripe fruit, the normal condition of the fruit and the age of 3-6 years and the condition of the fruit of normal and age of 7-10 years. Decision tree C4.5 algorithm generates 8 rules with 4 rules showing a high production value, which means that the four rules affect the yield of CPO.

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 ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

2018 ◽  
Vol 2 (2) ◽  
pp. 178
Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Training Data ◽  
Prediction System ◽  
Tree Model ◽  
Average Accuracy ◽  
C4.5 Algorithm ◽  
Academic Grades ◽  
Decision Tree Method ◽  
Tree Method

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

scholarly journals Penerapan Algoritma C4.5 Dalam Memprediksi Ketersediaan Uang Pada Mesin ATM

2021 ◽  
Vol 5 (2) ◽  
pp. 556
Author(s):  
Firman Syahputra ◽  
Hartono Hartono ◽  
Rika Rosnelly
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Travel Time ◽  
Tree Model ◽  
Optimal Service ◽  
C4.5 Algorithm ◽  
Cash Transaction ◽  
Auc Value ◽  
Using Data ◽  
Balance Variable

This study aims to provide an evaluation of the availability of money in ATM machines using data mining. Data mining with the C4.5 algorithm is used to predict cash demand or total cash withdrawals at ATMs. To determine the need for ATM cash based on cash transaction data. It is hoped that this forecasting can help the monitoring department in making decisions about the money requirements that must be allocated to each ATM machine. The results of this study are expected to assist the ATM management unit in optimizing and monitoring the availability of money at an ATM machine for cash needs, so that it can provide optimal service to customers. Algortima C4.5 is an algorithm that is able to form a decision tree, where the decision tree will then generate new knowledge. The results of the test matched the data on the availability of money at the ATM machine. The results of implementing the C4.5 method on the availability of money at the ATM machine are seen from the travel time to the ATM location and also the remaining balance in the machine. The resulting decision tree model is to make the balance variable as the root, then the travel time as a branch at Level 1 with the variables fast, medium, long, and the bank becomes a branch at the last level (Level 2). Then the C4.5 algorithm was tested using the K-Fold Cross validation method with the value of fold = 10, it can be seen that the accuracy rate is 85%, the Precision value is 80% and the Recall value is 66.67%. While the AUC (Area Under Curve) value is 0.833, this shows that if the AUC value approaches the value 1, the accuracy level is getting better

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