Factors Influencing Secondary School Student’s Performance Through Variable Decision Tree Data Mining Technique

The unreasonable phenomenon caused by the lack of effective scientific method, The essay attempts to carry on related analysis and research of combining the data mining technique with sport teaching quality evaluation by mining valid sport teaching quality evaluation index sign system, making full use of the decision tree technique to solve the unreasonableness of the sports teaching quality evaluation and putting forward technical method for sports teaching quality evaluation based on the decision tree, aimed at making it fair, just, reasonable and efficient.

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A Student Performance Prediction Model Using Data Mining Technique

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.15.11214 ◽

2018 ◽

Vol 7 (2.15) ◽

pp. 61

Author(s):

Rohaila Abdul Razak ◽

Mazni Omar ◽

Mazida Ahmad

Keyword(s):

Data Mining ◽

Linear Regression ◽

Decision Tree ◽

Student Performance ◽

Prediction Accuracy ◽

Significant Variable ◽

Data Mining Technique ◽

Mining Technique ◽

Predicting Performance ◽

Using Data

Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students’ performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.

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Performance Analysis Based on Data Mining Technique in Predicting the Diabetic Disease - Decision tree and Naïve Bayes

2019 1st International Conference on Advances in Information Technology (ICAIT) ◽

10.1109/icait47043.2019.8987382 ◽

2019 ◽

Author(s):

Karthikeyan S. M ◽

Gopinath C B ◽

Chethan P. J ◽

Manikanta J

Keyword(s):

Data Mining ◽

Performance Analysis ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Mining Technique ◽

Mining Technique

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Utilization of Machine Learning in Brain Tumor Classification

International Journal of Scientific Research in Mechanical and Materials Engineering ◽

10.32628/cseit1952289 ◽

2019 ◽

pp. 325-330

Author(s):

Sanjeet Pandey ◽

Brijesh Bharadwaj ◽

Himanshu Pandey ◽

Vineet Kr. Singh

Keyword(s):

Data Mining ◽

Decision Tree ◽

Early Stage ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Mining Technique ◽

Diabetes Prediction ◽

System Data ◽

Health System Data ◽

Prediction Of Diabetes

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

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Islamists vs. Far Right Extremists

International Journal of Cyber Warfare and Terrorism ◽

10.4018/ijcwt.2021100105 ◽

2021 ◽

Vol 11 (4) ◽

pp. 74-92

Author(s):

Yeslam Al-Saggaf ◽

Patrick F. Walsh

Keyword(s):

United States ◽

Data Mining ◽

Decision Tree ◽

The United States ◽

Far Right ◽

Data Mining Technique ◽

Mining Technique ◽

Violent Extremism ◽

Similarities And Differences

In this study, a data mining technique, specifically a decision tree, was applied to look at the similarities and differences between Islamists and Far Right extremists in the Profiles of Individual Radicalisation in the United States (PIRUS) dataset. The aim was to identify differences and similarities across various groups that may highlight overlaps and variations across both Islamists and Far Right extremists. The data mining technique analysed data in the PIRUS dataset according to the PIRUS codebook's grouping of variables. The decision tree technique generated a number of rules that provided insights about previously unknown similarities and differences between Islamists and Far Right extremists. This study demonstrates that data mining is a valuable approach for shedding light on factors and patterns related to different forms of violent extremism.

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Meteorological Data Mining Based on Rough Set Attributes Reduction and Decision Tree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3388 ◽

2014 ◽

Vol 556-562 ◽

pp. 3388-3391

Author(s):

Xiao Ling Li ◽

Xu Wang

Keyword(s):

Data Mining ◽

Decision Tree ◽

Rough Set ◽

Meteorological Data ◽

Information Service ◽

Weather Forecast ◽

Huge Amount ◽

Data Mining Technique ◽

Mining Technique ◽

Meteorological Analysis

With the development of society and economy, meteorological information service has been paid more and more attention. Furthermore, frequent extreme weather because of the environmental deterioration makes the accurate and efficient weather forecast more and more important. However, the characteristic of huge amount of meteorological data leads to the difficulty of analysis. The analysis of meteorological data based on data mining technique is a research hotspot currently. This paper analyses the weather elements using the method of rough set attributes reduction, which can remove a portion of factors that can be ignored in the analysis of rainfall possibility, and improves the efficiency of meteorological analysis and decision making using decision tree.

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Building Data Mining Decision Tree Model for Predicting Employee Performance

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v6i2.79 ◽

2019 ◽

Vol 6 (2) ◽

pp. 75-86

Author(s):

Ira Mellisa

Keyword(s):

Data Mining ◽

Decision Tree ◽

Employee Performance ◽

Decision Tree Model ◽

Data Mining Technique ◽

Tree Model ◽

Mining Technique ◽

High Dependency ◽

Machine Operators ◽

A Company

Human resource is one of the functions of a company that is considered as an asset. Therefo re, the theory of performance qualificat ion was adopted by the company in order to get an overview of employee performance. Furthermore, the company needs an effective method to predict the performance not only for the employees but also for the new applic ants. The goals of this research are to get a decision tree model of the employee performance. By learning employee data, the performance of the new applicants could be predicted. The study would provide the characteristic of new applicants who will give better performance than other applicants . The data from a company in Indonesia will have been used for this research. The data mining technique will be applied to the data of operators (such as admins, clerks, cashiers, machine operators, and security offi cers). The data mining technique was use d is decision tree. The decision tree technique was commonly used for a supervised learning data. The decision tree technique also has advantages compared others, because of its ability to produce information that is easy to understand. The result of this research shown the high dependency of employee performance with employment type (work contract). It also means that employees are encouraged to provide good performance to the company if those employees have become p ermanent employees. This research also showed that there is no relationship between employee performances with gender or position grade.

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