Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa

2014 ◽  
Vol 6 (1) ◽  
pp. 15-20 ◽  
Author(s):  
David Hartanto Kamagi ◽  
Seng Hansun
Keyword(s):  
Data Mining ◽  
Drop Out ◽  
Time Pass ◽  
Academic Fields ◽  
C4.5 Algorithm ◽  
Index Terms ◽  
Using Data ◽  
Academic Year ◽  
Source Of Information ◽  
Test Result

Graduation Information is important for Universitas Multimedia Nusantara  which engaged in education. The data of graduated students from each academic year is an important part as a source of information to make a decision for BAAK (Bureau of Academic and Student Administration). With this information, a prediction can be made for students who are still active whether they can graduate on time, fast, late or drop out with the implementation of data mining. The purpose of this study is to make a prediction of students’ graduation with C4.5 algorithm as a reference for making policies and actions of academic fields (BAAK) in reducing students who graduated late and did not pass. From the research, the category of IPS semester one to semester six, gender, origin of high school, and number of credits, can predict the graduation of students with conditions quickly pass, pass on time, pass late and drop out, using data mining with C4.5 algorithm. Category of semester six is the highly influential on the predicted outcome of graduation. With the application test result, accuracy of the graduation prediction acquired is 87.5%. Index Terms-Data mining, C4.5 algorithm, Universitas Multimedia Nusantara, prediction.

scholarly journals Predicting Student Drop-Out in Higher Institution Using Data Mining Techniques

2020 ◽  
Vol 1496 ◽  
pp. 012005 ◽  
Author(s):  
W F Wan Yaacob ◽  
N Mohd Sobri ◽  
S A Md Nasir ◽  
W F Wan Yaacob ◽  
N D Norshahidi ◽  
...  
Keyword(s):  
Data Mining ◽  
Drop Out ◽  
Data Mining Techniques ◽  
Higher Institution ◽  
Using Data

Application of Data Mining in e-Commerce

2014 ◽  
Vol 7 (4) ◽  
pp. 79-91 ◽  
Author(s):  
Mohamed Chajri ◽  
Mohamed Fakir
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
Market Share ◽  
Data Mining Techniques ◽  
Commercial Area ◽  
Using Data ◽  
Definition Of ◽  
Source Of Information ◽  
The Web

The web in recent years has been a big trend, which helped make it a source of information and essential in the various fields of research, in particular, the commercial area that represents the e-commerce (electronic commerce). However, the competition in the e-commerce sites is very tight. This has pushed companies to conserve and retain customers rather than seeking to expand its market share by conquering politically. These requirements have introduced the extraction of knowledge from data in e-commerce sites, using data mining techniques. This article will be an introduction to the concept of data mining, a definition of economic concepts related to e-commerce, and the authors' approach to the application of data mining techniques in e-commerce.

scholarly journals Using Data Mining with C4.5 Algorithm for Student Department Selection at MTs N Kaliangkrik

2021 ◽  
Vol 1 (1) ◽  
pp. 22-36
Author(s):  
Ardhin Primadewi
Keyword(s):  
Data Mining ◽  
Test Score ◽  
General Knowledge ◽  
General Intelligence ◽  
Test Results ◽  
Language Knowledge ◽  
C4.5 Algorithm ◽  
System Input ◽  
Using Data ◽  
Selection Of

Psychological tests can determine the characteristics of behavior, personality, attitudes, interests, motivation, attention, perceptions, thinking power, intelligence, fantasies of students. MTs N Kaliangkrik routinely conducts tests for the selection of majors on its students assisted by Pelita Harapan Bangsa Magelang. In the implementation of the test for students at MTs N Kaliangkrik, processing and calculating the score still used Ms. Excel which requires extra time to recap and know the test results and the school needs to recap the existing results. The system developed applies data mining using the C4.5 Algorithm to predict the selection of majors. The test that is used as system input is the grade IX test score of MTs N Kaliangkrik which includes verbal, non-verbal, general intelligence, language knowledge, definite knowledge, general knowledge, and qualitative power tests. The accuracy of the similarity in the system reaches 80% (good) so that the system is suitable for use as a prediction tool for selecting majors in other schools.

scholarly journals Potable Water Order Forecasting System Using Data Mining Technique

2021 ◽  
Vol 11 (6) ◽  
pp. 1-8
Author(s):  
Phatarapon Vorapracha ◽  
Keyword(s):  
Data Mining ◽  
Drinking Water ◽  
Potable Water ◽  
Bayesian Classification ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
C4.5 Algorithm ◽  
Forecasting System ◽  
Using Data

Potable water order forecasting system using data mining technique. It aims to analyze, design and develop potable water order forecasting system using data mining technique. There is a comparison data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. This web application system allows the system to predict each customer's drinking water orders. Subscription support ordering, drinking water and bank payment. In terms of user interaction and use the MySQL database program to organize the system database. The result of development potable water order forecasting system using data mining technique. Have tested data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. From data research results using data in 9 months of training and 2 months of testing, it was found that the accuracy was 85.59%. C4.5 algorithm and test the system from the evaluation of 2 administrators, 3 employees and 5 customers, total 10 people with average mean of 4.20 .

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

Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 94-110
Author(s):  
Deepti Aggarwal ◽  
Sonu Mittal ◽  
Vikram Bali
Keyword(s):  
Data Mining ◽  
Uttar Pradesh ◽  
Drop Out ◽  
Primary Data ◽  
Educational Institutions ◽  
Technical Institute ◽  
Data Set ◽  
Data Mining Techniques ◽  
Preventive Actions ◽  
Using Data

The educational institutes are focusing on improving the performance of students by using several data mining techniques. Since there is an increase in the number of drop out students every year, if we are able to predict whether a student will complete the course or not, it is possible to take some preventive actions beforehand. The primary data set used for modelling has been taken from a reputed technical institute of Uttar Pradesh which consists of data of 6,807 students containing 20 academic and non-academic attributes. The most relevant attributes are extracted using CorrelationAttributeEval (in WEKA) technique using Ranker search method which ranks the attributes as per their evaluation. Synthetic minority oversampling technique (SMOTE) filter is applied to deal with the skewed data set. The models are built from eight classifiers that are analysed for predicting the most appropriate model to classify whether a student will complete the course or withdraw his/her admission.

Application of Data Mining in e-Commerce

2016 ◽  
pp. 302-314
Author(s):  
Mohamed Chajri ◽  
Mohamed Fakir
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
Market Share ◽  
Data Mining Techniques ◽  
Commercial Area ◽  
Using Data ◽  
Definition Of ◽  
Source Of Information ◽  
The Web

The web in recent years has been a big trend, which helped make it a source of information and essential in the various fields of research, in particular, the commercial area that represents the e-commerce (electronic commerce). However, the competition in the e-commerce sites is very tight. This has pushed companies to conserve and retain customers rather than seeking to expand its market share by conquering politically. These requirements have introduced the extraction of knowledge from data in e-commerce sites, using data mining techniques. This article will be an introduction to the concept of data mining, a definition of economic concepts related to e-commerce, and the authors' approach to the application of data mining techniques in e-commerce.

scholarly journals Implementation of C4.5 Algorithm and Forward Chaining Method for Higher Education Performance Analysis

2019 ◽  
Vol 125 ◽  
pp. 21002
Author(s):  
Mochamad Idris ◽  
Mustafid ◽  
Jatmiko Endro Suseno
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Individual Factors ◽  
Economic Growth And Development ◽  
Data Mining Techniques ◽  
Forward Chaining ◽  
C4.5 Algorithm ◽  
Using Data ◽  
Education Performance ◽  
Analyze Data

Higher education has an important role to develop human resources in the economic growth and development of the country. One of specific way of evaluating and analyze data in education is to use data mining techniques. C4.5 algorithm as one of the data mining techniques that have good performance is very relevant used for data analysis tools. In this research using data on the performance of lecturers in college, there are 100 records with a 6 variable that affects individual factors in the productivity of lecturers including age, employment, attendance, certification, position, Education, and additional duties. In the end of the mining result, the forward chaining method is used to extract the rules that are generated by C4.5 algorithm. The input premises are examined by forwarding chaining to generate the prediction result.

scholarly journals Diabetes prediction using Data mining Classification Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 5901-5905
Keyword(s):  
Data Mining ◽  
American Diabetes Association ◽  
Medical Information ◽  
Classification Techniques ◽  
Technology Advancement ◽  
The World ◽  
Diabetes Prediction ◽  
Decision Tress ◽  
Using Data ◽  
Test Result

Diabetes is one of the second largest disease in the world. In the recent survey it shows that there are overall 246 million people affected with this and in that women ratio is more. By the report of WHO, this figure is going to reach to 380 million by 2025. According to the American Diabetes Association,6% of the population are not aware that there are victims of diabetes and also every 21 sec at least for an individual diabetic test result is positive. With the technology advancement in the field of medical information, data is well maintained in the databases. This paper focuses on to diagnose data to provide the solution by observing the patterns in the data using various datamining classification techniques such as Naïve basis, Logistic regression, Decision tress etc

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