Data mining approach for predicting student and institution's placement percentage

Ashok, M.; Apoorva, A

doi:10.1109/csitss.2016.7779381

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Peneliti di USA dengan meganalisis faktor yang mempengaruhi keberahasilan mahasiswa berdasarkan indeks prestasi komulatif (IPK) diketahui bahwa hubungan nilai IPK tidak signifikan [7]. Data mining dalam memprediksi mahasiswa dengan menggunakan ketiga metode NB, DT dan NN, dari hasil ketiga metode tersebut maka diperoleh NN dapat memprediksi lebih baik dibandingkan dengan metode lainnya [8]. Prediksi kinerja siswa dengan membandingkan metode seperti regresi, DT, NN dan NB telah dilakukan dan mempunyai hasil bahwa metode NB memberikan akurasi yang lebih tinggi dibandingkan metode lainnya [9].…”

Section: Bahan Dan Metodeunclassified

Prediction of Student Graduation Time Using the Best Algorithm

Riyanto¹,

Hamid²,

Ridwansyah

2019

IJAIDM

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Data mining has a very important role in the world of education that can help educational institutions in predicting and making decisions related to student's academic status. In predicting student success in the future, a model that can predict well is needed. Decission Tree Model (DT) is a decision model that can predict clearly but has the disadvantage of not being able to accommodate large data, neural network (NN) is a very popular model because it uses non-linear data and can hold large data while support vector machine (SVM) can generalize from nonlinear to linear. Of the three methods, each has weaknesses and strengths, we use the NN, SVM, and DT algorithms to predict the graduation time of academic students in one of the private universities in Indonesia. The results of this study indicate that the three models produce accuracy of more than 80%, the DT model has an accuracy of 84.96%, NN has an accuracy of 84.68% and the SVM model has an accuracy of 85.18% higher than the other two models. The results arising from this study provide important reference material for planning the future success of students and faculty in early warning to students in the future.

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Section: Bahan Dan Metodeunclassified

Prediction of Student Graduation Time Using the Best Algorithm

Riyanto¹,

Hamid²,

Ridwansyah

2019

IJAIDM

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“…They proposed an algorithm for this which was compared with decision tree, naïve bayes and neural network. The proposed algorithm gave better accuracy than the existing ones [7]. Animesh, Vignesh, BysaniPruthvi and Naini formed a system to assist placement office and students to know where they stand.…”

Section: Literature Reviewmentioning

confidence: 95%

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2018

IJET

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“…These are moreover suggested as characteristic capacities of an individual. It discusses comprehensively the opening that exists in employability capacities among students and the need to address them [20]. The employability assumption model will expect an essential part in each educational association as it describes the establishment's quality and students' perspective on employability.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing intelligent hybrid DNN model for predicting students’ employability – A Machine Learning approach

Sun,

2023

EHSS

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The employability of students is an important concern for colleges nowadays, and forecasting it can assist institutions take prompt action to improve the institutional placement ratio. The most effective data mining methods for forecasting students' employability are categorization methods. Students can concentrate on areas where they need to improve to better match the company's skill set by being aware of their deficiencies prior to an interview. Also, forecasting student employability might assist academic staff in developing curricular plans. In order for kids, parents, guardians, organisations, and businesses to profit to some extent, this report offered a fresh, futuristic roadmap. Out of seven trials, the first five were carried out using in-depth statistical calculations, while the latter two were carried out using supervised machine learning techniques. On just one extreme, the Support Vector Machine (SVM) had the highest accuracy rate of 93% when it related to forecasting employment status. The Random Forest (RF), on the other hand, was able to identify the gender of placed kids with a maximum accuracy of 89%. It is also advised to determine the placement of gender and placement status using a number of key criteria. A statistical t-test with a significance threshold of 0.05 demonstrated that the student’s gender had no impact on the pay that was provided during job placement.

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Data mining approach for predicting student and institution's placement percentage

Cited by 8 publications

References 3 publications

Prediction of Student Graduation Time Using the Best Algorithm

Prediction of Student Graduation Time Using the Best Algorithm

Untitled

Developing intelligent hybrid DNN model for predicting students’ employability – A Machine Learning approach

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