Naive Bayes using to predict students' academic performance at faculty of literature

Pujianto, Utomo; Azizah, Erwina Nurul; Damayanti, Ayuningtyas Suci

doi:10.1109/iceeie.2017.8328782

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…Simultaneously, in this study [3][9], the Naïve Bayes algorithm's accuracy is not good enough compared to other machine learning algorithms. Besides, in this study [4] [10], the accuracy of Naïve Bayes is also still less than 80%; It is because, in the data mining process, all features are included without selecting relevant features. Therefore, good preprocessing and feature selection is needed to select relevant features, so the classifier's performance can be improved [11]- [14].…”

Section: Introductionmentioning

confidence: 78%

A Machine Learning Framework for Improving Classification Performance on Credit Approval

Prastyo

Prasetyo

Arti

2021

IJID

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Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.

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Section: Introductionmentioning

confidence: 78%

A Machine Learning Framework for Improving Classification Performance on Credit Approval

Prastyo

Prasetyo

Arti

2021

IJID

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“…Techniques Highest accuracy appeared [43] J48 100% [46] NavieBayes(NB) 98.86% [40] X-Means 86.17% [35] Support Vector Machine (SVM) 97.98% [14] Ctree 90.37% [49] Decision Tree(DT) 67% [38] Random Forest(RF) 96.4% [34] Logistic Regression(LA) 96.98% [45] Neural Network(NN) 96% [33] K-means 98.9% [36] Rule-Based 71.3% [6] CART 98.3% [4] RepTree 61.4% [6] Iterative Dichotomiser 3(ID3) 95.9% [39] IBK 82.1% [39] Simple Logistic 93.27% [44] JRip 83.46% [35] K-Medoids 84.04%…”

Section: Discussionmentioning

confidence: 99%

“…The study [44] by Pujianto et al in 2017 aimed to assist the students at the faculty of literature and the likelihood of their success in adapting to new environments. In Indonesia, it has always been an issue for elected students to join the literature faculty, especially those who don't have linguistic qualifications in high school.…”

Section: B Detecting Students Behaviormentioning

confidence: 99%

A Comparison of EDM Tools and Techniques

Alshehri¹,

Alhakami²,

Baz³

et al. 2020

IJACSA

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“…Sathick and Jaya [5] collected the information about the grade point average (GPA) of college students, such as attendance, learning level, social situation, geographical location, sleep duration, call record, and indoor activity frequency, and predict their academic performance by a linear regression model with a sparse operator. Pujianto et al [6] recognized the value of learner feelings in the research of student behaviors and learning effect, and advised to understand the state and attitude of learners by analyzing learner feelings. Therefore, several volunteers were asked to express their feelings about various courses, and their psychological changes before, during, and after class of several volunteers were recorded.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of English Scores of College Students Based on Multi-source Data Fusion and Social Behavior Analysis

Zhao¹,

Ren²,

Zheng³

2020

RIA

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Multi-source data fusion is the premise of applying big data technology in specific fields. Inspired by the theory on multi-source data fusion, this paper fuses various data on college students, including motion trajectories, consumptions, and social behaviors, and adopts support vector machine (SVM), a machine learning (ML) classifier to predict the English scores of college students. The behavior trajectories were taken into account, because this type of data represents the social similarity between students. Specifically, the behavioral features of college students were extracted, and subject to principal component analysis (PCA). Based on these features, the correlation between student score and social relation was analyzed, and used to predict the English scores of college students. Experimental results show that our method can accurately reflect the relationship between the social behaviors and course scores of college students.

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Naive Bayes using to predict students' academic performance at faculty of literature

Cited by 11 publications

References 2 publications

A Machine Learning Framework for Improving Classification Performance on Credit Approval

A Machine Learning Framework for Improving Classification Performance on Credit Approval

A Comparison of EDM Tools and Techniques

Prediction of English Scores of College Students Based on Multi-source Data Fusion and Social Behavior Analysis

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