scholarly journals Modelling Student’s Performance Using Data Mining Techniques in a Higher Learning Environment in the Pacific

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Statistical Analysis ◽  
Decision Tree ◽  
Student Performance ◽  
Demographic Data ◽  
Classification Model ◽  
Classification Techniques ◽  
The Pacific ◽  
Under Sampling

The students’ performance in higher education has become one of the most widely studied area. Modelling student performance play a pivotal role in forecasting students’ performance where the data mining applications are now becoming most widely used techniques in this study. There are various factors, which determine the student performance. Eight attributes are used as input, which is considered most influential in determining students’ performance in the Pacific. Statistical analysis is done to see which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. Some of classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The WEKA explorer application and R software are used for correlation test between different variables. The dataset used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has done well on both, imbalanced and balanced dataset. Another technique which has done well is Decision tree. Statistical analysis shows that internal assessment has weak positive relationship with student performance while demographic data is not. Further observations are reported in this research in relation to two types of datasets with application to different classification techniques

scholarly journals Applicability of Traditional Classification Techniques on Educational Data

2020 ◽  
Vol 8 (6) ◽  
pp. 1672-1677
Keyword(s):  
Data Mining ◽  
Pilot Study ◽  
Decision Tree ◽  
Student Performance ◽  
Performance Prediction ◽  
Nearest Neighbor ◽  
Class Imbalance ◽  
Classification Techniques ◽  
Statistical Measures ◽  
Traditional Classification

Student performance prediction and analysis is an essential part of higher educational institutions, which helps in overall betterment of the educational system. Various traditional Data Mining (DM) techniques like Regression, Classification, etc. are prominently utilized for analyzing the data coming from educational settings. The usage of DM in the area of academics is called Educational Data Mining (EDM). The current pilot study aims to determine the applicability of these standalone classification techniques namely; Decision Tree, BayesNet, Nearest Neighbor, Rule-Based, and Random Forest (RF). The present pilot study uses the WEKA tool to implement traditional classification techniques on a standard dataset containing student academic information and background. The paper also implements feature selection to identify the high influential features from the dataset. It helps in reducing the dimensionality of the dataset as well as enhancing the accuracy of the classifier. The results of classifiers are compared on basis of standard statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Kappa, etc. The results show the applicability of classification algorithms for student performance prediction which will help under-achievers and struggling students to improve. It is found the output that, J48 algorithm of the Decision tree gave the best results. Further, it is deduced from the comparative analysis that individual classifiers give different accuracy on the same dataset due to class imbalance in a multiclass dataset.

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals Komparasi Algoritma Kasifikasi dengan Pendekatan Level Data Untuk Menangani Data Kelas Tidak Seimbang

2017 ◽  
Vol 3 (1) ◽  
pp. 1-6
Author(s):  
Ahmad Ilham
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Level Data ◽  
Under Sampling

Masalah data kelas tidak seimbang memiliki efek buruk pada ketepatan prediksi data. Untuk menangani masalah ini, telah banyak penelitian sebelumnya menggunakan algoritma klasifikasi menangani masalah data kelas tidak seimbang. Pada penelitian ini akan menyajikan teknik under-sampling dan over-sampling untuk menangani data kelas tidak seimbang. Teknik ini akan digunakan pada tingkat preprocessing untuk menyeimbangkan kondisi kelas pada data. Hasil eksperimen menunjukkan neural network (NN) lebih unggul dari decision tree (DT), linear regression (LR), naïve bayes (NB) dan support vector machine (SVM).

scholarly journals Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

2015 ◽  
Vol 5 (6) ◽  
pp. 1569 ◽  
Author(s):  
Moloud Abdar ◽  
Sharareh R. Niakan Kalhori ◽  
Tole Sutikno ◽  
Imam Much Ibnu Subroto ◽  
Goli Arji
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Heart Diseases ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Analysis Models

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.

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>

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