Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student's Graduation in XYZ University

Yaqin, Ainul; Rahardi, Majid; Abdulloh, Ferian Fauzi

doi:10.14569/ijacsa.2022.0130652

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…SMOTE is one of the algorithms that can be used to balance a dataset, using an oversampling approach, in which this algorithm will generate synthesis data from the minority class so that the minority class has the same amount of data as the majority class [15], [22].This synthetic data is obtained based on the value of k-neighbours from minority data. In this study, researchers will compare three variants of the SMOTE algorithm, namely, SMOTE, SMOTE ENN and SMOTE Tomek.…”

Section: Smotementioning

confidence: 99%

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Abdulloh¹,

Rahardi²,

Aminuddin³

et al. 2022

IJACSA

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Tracer Study is a mandatory aspect of accreditation assessment in Indonesia. The Indonesian Ministry of Education requires all Indonesia Universities to anually report graduate tracer study reports to the government. Tracer study is also needed by the University in evaluating the success of learning that has been applied to the curriculum. One of the things that need to be evaluated is the level of absorption of graduates into the working industry, so a machine learning model is needed to assist the University Officials in evaluating and understanding the character of its graduates, so that it can help determine curriculum policies. In this research, the researcher focuses on making a reliable machine learning model with a tracer study dataset format that has been determined by the Government of Indonesia. The dataset was obtained from the tracer study of Amikom University. In this study, SVM will be tested with several variants of the algorithm to handle imbalanced data. The study compared SMOTE, SMOTE-ENN, and SMOTE-Tomek combined with SVM to detect the employability of graduates. The test was carried out with K-Fold Cross Validation, with the highest accuracy and precision results produced by SMOTE-ENN SVM model by value of 0.96 and 0.89.

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Section: Smotementioning

confidence: 99%

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Abdulloh¹,

Rahardi²,

Aminuddin³

et al. 2022

IJACSA

Self Cite

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show abstract

“…Gender [18], [19], [20] Grades of courses [21], [18], [22] Courses [21], [18], [23], [22] GPA [21], [18], [23], [22], [19], [20] Table 1 is a synthesis of documents and research related to prediction for improving GPA levels. The factors used were Gender, Grades of courses, Courses, and GPA.…”

Section: Factors Related Workmentioning

confidence: 99%

Machine Learning Ecosystem to Enhance Grade Point Average

Issaro¹,

Piriyasurawong²

2022

ujer

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This study aims to 1) develop MachineLearning Ecosystem models to enhance grade point averages, and 2) predict grade point averages by modeling Machine Learning Ecosystem techniques, namely, Decision Trees (DT), Naï ve Bayes (NB), and a Neural Network (NN). Findings from an efficiency comparison of the three models of a Machine Learning Ecosystem in predicting grade point averages showed that DT achieved the highest accuracy of 100.00%. In contrast, NN achieved the second-highest accuracy of 85.83%, and NB achieved the lowest accuracy of 81.67%. For the F-Measure, the F-Measure with DT, NN, and NB was 100.00%, 76.59%, and 70.59%, respectively. Thus, DT was the most appropriate model for a Machine Learning Ecosystem to predict the students' GPA.

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“…This section of the paper focuses on imbalanced application domains and the suitability of the classifier for binary and multiclass imbalanced application domains [11,12]. It also highlights the issues raised due to data imbalance [13,14].…”

Section: Imbalanced Application Domainsmentioning

confidence: 99%

Novel Oversampling Algorithm for Handling Imbalanced Data Classification

More¹,

Rana²

2022

IJACSA

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In the current age, the attention of researchers is immersed by numerous imbalanced data applications. These application areas are intrusion detection in security, fraud recognition in finance, medical applications dealing with disease diagnosis pilfering in electricity, and many more. Imbalanced data applications are categorized into two types: binary and multiclass data imbalance. Unequal data distribution among data diverts classification performance metrics towards the majority data instance class and ignores the minority data, instance class. Data imbalance leads to an increase in the classification error rate. Random Forest Classification (RFC) is best suitable technique to deal with imbalanced datasets. This paper proposes the novel oversampling rate calculation algorithm as Improvised Dynamic Binary-Multiclass Imbalanced Oversampling Rate (IDBMORate). Experimentation analysis of the proposed novel approach IDBMORate on Page-block (Binary) dataset shows that instances of positive class is increased from 559 to 1118 whereas negative instance class remains same as 4913. In case of referred multiclass dataset (Ecoli), IDBMORate produces the consistent result as minority classes (om, omL, imS, imL) instances are oversampled majority class instances remains unchanged. IDBMORate algorithm reduces the ignorance of minority class and oversamples its data without disturbing the size of the majority instance class. Thus, it reduces the overall computation cost and leads towards the improvisation of classification performance.

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Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student's Graduation in XYZ University

Cited by 14 publications

References 18 publications

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia

Machine Learning Ecosystem to Enhance Grade Point Average

Novel Oversampling Algorithm for Handling Imbalanced Data Classification

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