Non-Written Enrolment System using Classification Methods

Lestari, Mei; Darmawan, Agus; Septiani, Ni Wayan Parwati; Trihapsari, A.

doi:10.1088/1742-6596/1175/1/012055

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…When the results of this thesis are compared to the paper of Lestari et al (2019), the results in the paper are all in favor of the C4.5 algorithm. In the paper the goal is to find the best classification technique to predict a student GPA and based on that GPA their enrollment possibility.…”

Section: Results In Contrast To Earlier Literaturementioning

confidence: 90%

A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

Kiffen¹,

Lelli²,

Feyli³

2021

Preprint

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In this preprint, we introduce a dataset containing students enrolment applications combined with the related result of their filing procedure. The dataset contains 73 variable. Student candidates, at the time of applying for study, fill a web form for filing the procedure. A committee at Tilburg University review each single application and decide if the student is admissible or not. This dataset is suitable for algorithmic studies and has been used in a comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms. They have been used for predicting the decision of the committee in admitting candidates at various bachelor programs. Our analysis shows that, in this particular case, a combination of the approaches outperform a both of them in term of precision and recall.

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Section: Results In Contrast To Earlier Literaturementioning

confidence: 90%

A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

Kiffen¹,

Lelli²,

Feyli³

2021

Preprint

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show abstract

“…1-NN and Naïve Bayes performed the most excellent results, which are very satisfying comparatively. Lestari et al [19] performed a comparison between the two data mining classification methods. They implemented the two algorithms namely Naïve Bayes and C4.5 on enrolment (non-written) system.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier

Biswas¹,

Majumder²,

Mia³

et al. 2019

IJITEE

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Nowadays, In Bangladesh, the dropout rate at post-graduation level or incompletion of the post-graduation degree is considered as a serious problem in the education sector. This work can be used to support for identifying the specific individuals as well as the institutional factors which may next lead to the enrollment or drop out at the post-graduation degree. The real dataset is used to accomplish this work. Here, seven classification algorithms namely Naïve Bayes, Multilayer Perceptron, Logistic, Locally Weighted Learning (LWL), Random Forest, Random Tree, and Part are applied in this context. A confusion matrix is calculated for each classification model. Then, we computed all the seven performance evaluation metrics (accuracy, sensitivity, precision, specificity, F1 score, FPR, and FNR). Each classifier's performances are analyzed and measured from the computed performance evaluation metrics. Naïve Bayes, LWL, and Part classifier perform better than all other working classifiers attaining 86.36% accuracy and on the contrary, Random Tree classifier performs worst achieving 74.24% accuracy. After further analyzing of the result based on performance evaluation metrics, it is observed that LWL classifier performed best in this context among all the classifiers.

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“…They compared their technique with single model-based techniques and determined that it not only gives better accuracies in the abovementioned context but also more useful for understanding the several factors that influence the enrollment of the student in STEM. Lestari et al [13], presented a comparison between the two data mining classification techniques. They executed the two algorithms namely naïve bayes and C4.5 on a non-written enrolment system.…”

Section: Literature Reviewmentioning

confidence: 99%

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

Mia*,

Biswas,

Sattar

et al. 2019

IJITEE

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Bangladesh is a densely populated country where a large portion of citizens is living under poverty. In Bangladesh, a significant portion of higher education is accomplished at private universities. In this twenty-first century, these students of higher education are highly mobile and different from earlier generations. Thus, retaining existing students has become a great challenge for many private universities in Bangladesh. Early prediction of the total number of registered students in a semester can help in this regard. This can have a direct impact on a private university in terms of budget, marketing strategy, and sustainability. In this paper, we have predicted the number of registered students in a semester in the context of a private university by following several machine learning approaches. We have applied seven prominent classifiers, namely SVM, Naive Bayes, Logistic, JRip, J48, Multilayer Perceptron, and Random Forest on a data set of more than a thousand students of a private university in Bangladesh, where each record contains five attributes. First, all data are preprocessed. Then preprocessed data are separated into the training and testing set. Then, all these classifiers are trained and tested. Since a suitable classifier is required to solve the problem, the performances of all seven classifiers need to be thoroughly assessed. So, we have computed six performance metrics, i.e. accuracy, sensitivity, specificity, precision, false positive rate (FPR) and false negative rate (FNR) for each of the seven classifiers and compare them. We have found that SVM outperforms all other classifiers achieving 85.76% accuracy, whereas Random Forest achieved the lowest accuracy which is 79.65%.

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Non-Written Enrolment System using Classification Methods

Cited by 3 publications

References 3 publications

A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

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