Using Classification Data Mining Techniques for Students Performance Prediction

Ketui, Nongnuch; Wisomka, Warawut; Homjun, Kanitha

doi:10.1109/ecti-ncon.2019.8692227

Cited by 21 publications

(13 citation statements)

References 4 publications

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“…Data mining dalam suatu sistem pendidikan begitu juga penting untuk menganalisis sebuah kinerja siswa di bidang akademik dengan melihat faktor-faktor kinerja yang berbeda [1]. dalam pengelolahan pada Data mining baru selain diperkenalkan di sektor bisnis, tetapi juga terbukti diterapkan untuk sector pada pendidikan [2]. Begitupun memprediksi kinerja akademik siswa sangat penting untuk mengembangkan strategi bagi pelajar yang lemah untuk meningkatkan kinerja mereka secara keseluruhan karena kinerja siswa tergantung pada perbedaan faktor sosial, demografi, psikologis, dan keluarga [3].…”

Section: Pendahuluanunclassified

Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning

Saputra

Maulidah²,

Hidayati³

et al. 2022

mib

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In our current study, we are doing a comparison of several algorithms that we have tested, namely in searching for the accuracy level of learning performance in students, the problem of this research is how to get the results of excellent generalization abilities so that a higher accuracy value is obtained. Our goal is to get the best-performing accuracy level results and then to identify features that can affect student learning performance. From the results of the algorithm that we have tested, four of them are Naïve Bayes, Support Vectore Machine, Neural Network and KNN contained in machine learning. The results of the four algorithms for the Naïve Bayes algorithm have an accuracy value of 96.30%, the Support Vectore Machine algorithm has an accuracy of 98.70%, and the Naural Network algorithm has an accuracy of 99.50% and the last one with the KNN algorithm produces an accuracy of 94.80%. it can be concluded that using the Neural Network algorithm is an algorithm with the best performance than using other algorithms in evaluating student learning performance, besides that the Neural Network can be used as an excellent alternative to be used as predictions, especially in the field of education.

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Section: Pendahuluanunclassified

Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning

Saputra

Maulidah²,

Hidayati³

et al. 2022

mib

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“…Tang et al: (2015), Klusener and Fortenbacher (2015), Brinton and Chiang (2015), Shrivas and T iwari b. Articles: Al Shehri et al: (2017), Amaya et al: (2015), Widyaningsih et al: (2019) c. Articles: Okubo et al: (2017), Singh and Kaur (2018), Santoso and Yulia (2019), Sumitha et al: (2016), Al Barrak and Al Razgan d. Articles: Amrieh et al: (2015), Ruby and David (2015), Almasri et al: (2019), Sivakumar et al: (2016), Kumar et al: (2019), Chanlekha and Niramitranon (2018) e. Articles: Livieris et al: (2018), Angiani et al: (2019), Jishan et al: (2015), Livieris et al: (2019c), Al Saleem et al: (2015, f. Articles: Kostopoulos et al: (2015), Athani et al: (2017), Sara et al: (2015), Kasthuriarachchi and Liyanage (2019), Namomsa andSharma (2018) g. Articles: Bergin et al: (2015), Navamani and Kannammal (2015), Ketui et al (2019), Bhegade and Shinde (2016), Pristyanto et al, (2018) h. Articles: Lopez Guarin et al: (2015, Pradeep et al: (2015), Yehuala (2015), Kaur and Singh (2016), Mahboob et al: (2017), Pereira et al, (2018)…”

Section: Attributes Usedmentioning

confidence: 99%

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

Papadogiannis

Poulopoulos

Wallace

2020

International Journal of Educational Research Review

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This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole.

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“…The algorithm in grouping the exam level is divided into two steps: learning to create a template and the leveling of the exam to be graded, tested against an exam like a Training Data set, a process that uses the most common or similar search method to predict a data set that can be divided in any type of information. The Performance Model Assessment is the assessment of the model's ability to grade an exam's cognitive rating, through machine learning focused on decision-making or prediction, correct grading, a test method for comparing the model's performance to the performance of the exam's Cognitive rating, determined by its accuracy, from the Cognitive Rating [11].…”

Section: Related Workmentioning

confidence: 99%

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

Palee¹,

Wannapiroon²,

Nilsook³

2020

IJACSA

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Using Classification Data Mining Techniques for Students Performance Prediction

Cited by 21 publications

References 4 publications

Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning

Komparasi Evaluasi Kinerja Siswa Belajar dengan Mengggunakan Algoritma Machine Learning

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

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