Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using NaÃ¯ve  Bayes Algorithm

Herlambang, Admaja Dwi; Wijoyo, Satrio Hadi; Rachmadi, Aditya

doi:10.25126/jitecs.20194169

Cited by 7 publications

(4 citation statements)

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“…In this paper, the researchers classified the students' achievement into five categories "very good," "good," "fair," "poor", and "failed." The created model had a moderate accuracy which could be a guide for reference in the future (Herlambang et al, 2019).…”

Section: Application Of Data Mining In Tvetmentioning

confidence: 98%

“…Most previous studies focus on the student's performance using data mining approaches. Specifically, most researchers only apply one data mining approach in their data analysis (Babu et al, 2020;Messele & Addisu, 2020;Herlambang et al, 2019). Therefore, the main objective of this study is to utilize and compare several data mining approaches, namely Decision Tree, Neural Network, Logistic Regression and Naïve Bayes, to investigate the students' inclination to TVET enrolment.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Hong¹,

Ch’ng²,

Roslan³

2022

JST

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Technical and Vocational Education and Training (TVET) is an education system that delivers necessary information, skills, and attitudes related to work or self-employment. However, the TVET program is not preferred by most Malaysian students due to several factors such as students’ interest, parental influence, employers’ negative impression, facility in vocational institutions, inexperienced TVET instructors, and society’s negative perception. Consequently, it raises the issue of skilled workers shortage. The gravest threat will be far-reaching, pushing our economy into depreciation. Therefore, it is important to identify the students’ traits and interests before conducting further investigation to turn and thrive in this phenomenon. This study aims to utilise several classifiers (Decision Tree, Neural Network, Logistic Regression and Naïve Bayes) to predict students’ inclination to join TVET programmes. A total of 428 secondary school students from Kedah, Malaysia, are chosen as our survey respondents. The best classifier is determined according to the lowest misclassification rate. The findings revealed that the Decision Tree-based Gini Index with three branches prevail against other classifiers with a misclassification rate of 0.1938. Therefore, the classifier could act as a steer for the Kedah Department of Education (DOE), related parties, and the TVET agency in implementing effective strategies to enliven and inspire students to join TVET programs.

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Section: Application Of Data Mining In Tvetmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Hong¹,

Ch’ng²,

Roslan³

2022

JST

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“…After we choose the quiz model we want, then we are directed to the question editing panel. In this panel we can add the correct answer key and can also add a response/ comment if the tester chooses the correct answer or the wrong answer (Herlambang & Rachmadi, 2023).…”

Section: Desktop Application-based Interactive Testmentioning

confidence: 99%

Innovation of Interactive Tests in Maharah Kitabah in Class VIII Arabic Textbooks

Nurazim,

Baroroh

2024

Al-Lisan

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Tests or evaluations applied by teachers or educators in various educational institutions are still considered less interesting and tend to be monotonous, so there is no intense interaction between educators and students in the evaluation process carried out. Often, students feel bored with ordinary tests or text forms that are rigid and monotonous, so there needs to be an update in the model or form of tests that are interactive, innovative, fun, and not boring for students. The purpose of this research is to develop various models of interactive Arabic tests or quizzes so that Arabic learning becomes more interesting and fun. This study uses literature research with a qualitative descriptive approach. Secondary data used as a data source, with data collection techniques in the form of documentation. And the result of this research is that there are various forms or models of interactive tests that can be utilized by teachers or instructors of the Arab language, either by developing their own using several computer-based applications or by utilizing website-based test or quiz applications that already exist on the Internet, where some of these applications can be accessed for free (free).

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“…In linear regression, classification is used for developing vocational education research [35]. In naïve bayes, it can be used to predict students' learning achievement [40], [45]. Both methods may be used as an optional solution or merely as a guide for designing a better VET program.…”

Section: Curriculummentioning

confidence: 99%

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

Dardiri

Dwiyanto

Utama

2020

Int. J. Adv. Intell. Informatics

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Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.

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Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using NaÃ¯ve Bayes Algorithm

Cited by 7 publications

References 5 publications

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Innovation of Interactive Tests in Maharah Kitabah in Class VIII Arabic Textbooks

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

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