Prediction of Student Graduation with Naive Bayes Algorithm

Hartatik, Hartatik; Kusrini, Kusrini; Prasetio, Agung Budi

doi:10.1109/icic50835.2020.9288625

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…In recent years, many studies have been based on traditional machine learning methods to predict students' graduation development. For example, Hartatik et al [9] utilized the Naive Bayes algorithm to analyze students' academic performance, thereby predicting students' graduation development tasks. In addition, Putri et al [10] adopted the C4.5 decision tree method to improve the accuracy of graduation development predictions concerning student performance and usual performance.…”

Section: Graduation Development Predictionmentioning

confidence: 99%

“…Many efforts have been made in predicting students' graduation development, mainly using traditional machine algorithms and deep learning methods. Traditional machine learning algorithms mainly include the Naive Bayes model [9], C4.5 Algorithm [10], etc. However, with the rise of educational data mining [11] and increased student data processing, traditional algorithms show disadvantages such as weak generalization and insufficient computational abilities.…”

Section: Introductionmentioning

confidence: 99%

“…Benefiting from the merit of deep learning [12][13][14], neural networks such as Back Propagation Neural Network (BP) [15], Generative Adversarial Networks (GAN) [16], Long Short-Term Memory (LSTM) [16], Graph Convolutional Network (GCN) [8] and Attention mechanism [8] have also been applied to the prediction of graduation development. However, the above-mentioned research work only predicts students' graduation development based on their grades [8][9][10]16], credits [8,9] or their regular performances [10,15], ignoring the potential impact of multi-dimensional social relations of college students on graduation development prediction research.…”

Section: Introductionmentioning

confidence: 99%

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Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

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As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student's performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students' features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

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Section: Graduation Development Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

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show abstract

“…Research on the Prediction of Student Graduation using the Naive Bayes Algorithm by Hartatik et al, (2020) [17] found that the algorithm can predict student academic progress. This model accurately predicts student achievement using IPS1,2,3,4, UN level, gender, and residence status.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction for Graduation of Private High School Students with Machine Learning Approach

Kartini,

Fetty Tri Anggraeny,

Aang Kisnu Darmawan

et al. 2023

Technium

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Graduation rates indicate school success. Predicting student graduation helps schools identify students in danger of dropping out and intervene early to enhance academic performance. It can also assist policymakers create graduation and dropout prevention initiatives. However, based on a literature search, predicting student graduation rates from admission test scores is difficult. School grades are a better predictor of timely tertiary graduation than acceptance test scores because college success requires cognitive abilities and self-regulation competencies, which are better indexed by school grades. Self-efficacy, school academic culture, and future expectations can also affect student graduation rates. Finally, the selective admissions modality needs to be refined. This study aims to (1) predict private high school graduation with eight algorithms: Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, Logistic regression, and (2) compare the performance of the eight algorithms. According to research, the Random tree, Naïve Bayes Multinomial, Random forest (RF), and Ada boost algorithms all perform at 99.49% for the first aim. For the second objective, the Random Tree approach outperforms other algorithms in Accuracy (99.49%), Precision (100%), F-Measure (99.74%), and consumption time (0 seconds). Therefore, the Random tree algorithm outperforms others. This research contributes in two ways: scientifically by testing eight algorithms—Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, and Logistic regression—to predict private high school graduation, and secondly by recommending school administrators to develop a selective enrollment model.

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“…Therefore, evaluation of the success of implementing a study program at a university is seen from student learning outcomes. For this reason, in this study, the implementation of the Naive Bayes method use to help predict student academic achievement using the Semester Achievement Index (IPS) score (Hartatik, 2020), (Jananto, 2013).…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Naïve Bayes Algorithm in the Design of Information Systems for the Development of Student Academic Achievement

Hartatik

Yuliani

2022

MULTI RES LIT

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The research conducted in this journal is the design of an information system on the development of student achievement using the Naive Bayes algorithm. The purpose of research and development of this system is to help students find predictions of early graduation and allow the admin to find out progress and forecasts of active student graduation. This research begins with the design of the business process of the actor who will see the prediction of graduation. In designing the application, FR tables, use case diagrams, use case scenarios, class diagrams, and sequence diagrams are created. Then proceed with database design in the form of Entity Relationship Diagram (ERD), Relationship Between Tables (RAT) and table design. After designing the database, a user interface design is made in the form of a mockup. The result of the research in this final project is creating a Student Achievement design of Development Information System with the Naive Bayes Algorithm. This information system comes from application design, database design, user interface design, and system testing design which is then implemented to calculate graduation predictions based on student academic achievement.

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Prediction of Student Graduation with Naive Bayes Algorithm

Cited by 4 publications

References 12 publications

Social-path embedding-based transformer for graduation development prediction

Social-path embedding-based transformer for graduation development prediction

Early Prediction for Graduation of Private High School Students with Machine Learning Approach

Implementation of the Naïve Bayes Algorithm in the Design of Information Systems for the Development of Student Academic Achievement

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