scholarly journals Prediction of student graduation accuracy using decision tree with application of genetic algorithms

2021 ◽  
Vol 1073 (1) ◽  
pp. 012055
Author(s):  
A Maulana
Keyword(s):  
Genetic Algorithms ◽  
Decision Tree ◽  
Student Graduation

Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates

2003 ◽  
Vol 160 (3) ◽  
pp. 291-300 ◽  
Author(s):  
Tom D'heygere ◽  
Peter L.M. Goethals ◽  
Niels De Pauw
Keyword(s):  
Genetic Algorithms ◽  
Decision Tree ◽  
Benthic Macroinvertebrates ◽  
Tree Models ◽  
Input Variables

scholarly journals Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa

2019 ◽  
Vol 18 (1) ◽  
pp. 54-59
Author(s):  
Irfan Ali ◽  
Lana Sularto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Student Behavior ◽  
Network Models ◽  
Time Data ◽  
Neural Network Models ◽  
Student Graduation ◽  
Artificial Neural ◽  
Artificial Neural Network Models

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.

scholarly journals Classification Analysis Using C4.5 Algorithm To Predict The Level of Graduation of Nurul Falah Pekanbaru High School Students

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Fana Wiza ◽  
Bayu Febriadi
Keyword(s):  
High School ◽  
High School Students ◽  
Decision Tree ◽  
Graduation Rates ◽  
Graduation Rate ◽  
School Students ◽  
C4.5 Algorithm ◽  
Student Graduation ◽  
Decision Tree Method ◽  
Tree Method

School as one of the processes for implementing formal education is required to carry out the learning process optimally to produce quality students. Regarding the research process carried out to predict the graduation rate of SMA Nurul Falah students by using the decision tree method. The data used in this study are student data using the criteria for student names, majors, average report cards from semester one (I), two (II), three (III), four (IV), five (V), and the average value of the National Standard School Examination (USBN). The data is then managed using Rapidminer 5.3 software to make it easier to predict student graduation rates. The application of data mining is used to predict the graduation rate by using the decision tree method and C4.5 algorithm as a supporter as well as to find out information on the graduation rate of Nurul Falah High School students. This study aims to predict student graduation rates in order to get useful information and the school can make policies in the coming year.

scholarly journals Predictive Decision Support System using Logistic Regression and Decision Tree Model Combination for Student Graduation Success Determination

2015 ◽  
Vol 2 ◽  
pp. 144-153
Author(s):  
Ace C. Lagman
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Decision Tree ◽  
Educational Data Mining ◽  
The Philippines ◽  
Full Time ◽  
Model Combination ◽  
Process Data ◽  
Data Set ◽  
Student Graduation

More recently, researchers and higher education institutions are also beginning to explore the potential of data mining in analyzing academic data. The goal of such an endeavor is to find means to improve the services that these institutions provide and to enhance instruction. This type of data mining application is more popularly known as educational data mining or EDM. At present, EDM is more particularly focused on developing tools that can be used to discover patterns in academic data. It is more concerned about exploring a huge amount of data in order to identify patterns about the microconcepts involved in learning. This area of EDM is often referred to as Learning Analytics – at least as it is commonly compared to more prominent data mining approaches that process data from large repository for better decision-making. One main topic under educational data mining is student graduation. In the Philippines According to the National Statistics Office, there is an imbalance between student enrolment and student graduation. Almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate on time. This scenario indicates the need to conduct research in this area in order to build models that can help improve the situation. The study focused to extract hidden patterns from the data set using logistic regression and decision tree algorithms that can be used to predict too early identification of students who are vulnerable to not having graduation on time so proper retention policies and measures be implemented by the administration.

scholarly journals The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm

2019 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Asro Pradipta ◽  
Dedy Hartama ◽  
Anjar Wanto ◽  
Saifullah Saifullah ◽  
Jalaluddin Jalaluddin
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Ordinary People ◽  
Tree Model ◽  
Student Profile ◽  
Student Gender ◽  
C4.5 Algorithm ◽  
Student Graduation ◽  
Value Of Accreditation

Graduating on time is one element of higher education accreditation assessment. In the Strata 1 level, students are declared to graduate on time if they can complete their studies <= eight semesters or four years. BAN-PT sets a timely graduation standard of >= 50%. If the standard is not met, it will reduce the value of accreditation. These problems encourage the Universitas Simalungun Pematangsiantar to conduct evaluations and strategic steps in an effort to increase student graduation rates so that the targets of BAN-PT can be achieved. For this reason it is necessary to know in advance the pattern of students who tend not to graduate on time. In this study, C4.5 Algorithm is proposed to predict student graduation. This algorithm will process student profile datasets totaling 150 data. This dataset has a graduation status label. The value of the label is categorical, that is, right and late. The features or attributes used, namely the name of the student, gender, student status, GPA. The results of the C4.5 algorithm are in the form of a decision tree model that is very easy to analyze. In fact, even by ordinary people. This model will map the patterns of students who have the potential to graduate on time and late.

scholarly journals Intrusion Detection System using Datamining Based Enhanced Framework

2019 ◽  
Vol 9 (1) ◽  
pp. 5013-5017
Keyword(s):  
Genetic Algorithms ◽  
Network Security ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Experimental Results ◽  
Network Resources ◽  
Classification Techniques

With the significant increase in the use of computers over the network and the development of applications on different platforms, the focus is on network security. The identification of multiple attacks is actually an important element of network security. The role of the IDS is to track and prevent unauthorized use or damage to network resources and systems. An intrusion detection system using Datamining Based Enhanced Framework (DEF) is presented in this paper. The model is assisted by the K-mean Clustering and Decision Tree (DT) classification techniques in which genetic algorithms (GA) for clusters, max runs and confidence can be used. The experimental results shows the promising outcome of the proposed Datamining Based Enhanced Framework (DEF).

Optimizing twins decision tree classification, using genetic algorithms

2008 ◽  
Author(s):  
Farid Seifi ◽  
Mohammad Reza Kangavari ◽  
Hamed Ahmadi ◽  
Ehsan Lotfi ◽  
Sanaz Imaniyan ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Decision Tree ◽  
Decision Tree Classification
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