Internship recommendation system using simple additive weighting

Santoso, Priyo Aji; Wibawa, Aji Prasetya; Pujianto, Utomo

doi:10.31763/businta.v2i1.102

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Besides that, SAW was also applied in determining the workplace for vocational school students. The students' behavior was analyzed and the system will make a recommendation to students to choose their preferred company (Santoso, Aij, et al, 2018).…”

Section: Simple Additive Weighting (Saw)mentioning

confidence: 99%

Students’ Tendencies in Choosing Technical and Vocational Education and Training (TVET): Analysis of the Influential Factors using Analytic Hierarchy Process

et.al¹

2021

TURCOMAT

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In the era of Industry Revolution (IR) 4.0, business and industry are being transformed by a new wave of digital technology. In order to boost the economy’s prosperity in Malaysia, skilled workforce or well-trained manpower is vital in accomplishing the goal.However, it requires mainstreaming Technical and Vocational Education Training (TVET)in education system by providingcomprehensive training, effective research consultancy, holistic courses, collaboration, student placement and program attachment opportunity. Coherent from this issue, the government can produce more skill workers that can handle the rapid changing world of work. In Malaysia, there are more than 1000 TVET institutions, where 506 are considered as public institutions. However, itstill receives less attention by the students after secondary education. The identified potential factors are TVET instructors, current policy in Malaysia, social perception, employers’ perception, parents, facility, education cost and student themselves. Therefore, this study aims to rank these factors according to the levels of importance using Analytic Hierarchy Process (AHP) method. AHP is a method used to rank criteria by assigning the weight for each criterion. In this study, primary data is collected using questionnaires from 32 TVET instructors of Institut Kemahiran Belia Negara (IKBN) in northern region of Malaysia. The result of AHP shows that the variable of parents is the most influential factor with the weight of 18.81%, followed by the variable of facilities (18.56%). Conversely, the least influential factor is the variable of social perception with the weight of 7.21%. Hence, the government should implement appropriate strategies to attract more secondary school students to enroll in TVET programs. Due to the growth of skilled workers, our country is expected to transform the landscape of the manufacturing industry over the next decade. Hence, developingthe country’s productivity and curbing youth unemployment.

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Section: Simple Additive Weighting (Saw)mentioning

confidence: 99%

Students’ Tendencies in Choosing Technical and Vocational Education and Training (TVET): Analysis of the Influential Factors using Analytic Hierarchy Process

et.al¹

2021

TURCOMAT

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Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-Commerce

Tadson,

Boasen,

Courtemanche

et al. 2023

Lecture Notes in Computer Science

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The simulation of divayana formula calculation to determine the priority of recommendation in evaluation activities

Divayana

2021

J. Phys.: Conf. Ser.

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Evaluation activities are not carried out to determine whether to pass or not the program being evaluated. Evaluation activities tend to provide recommendations for improving or completion of the constraints that are still found in the program being evaluated. The problem that often occurs in the field is the difficulty in determining recommendation priority for each evaluation aspect. Therefore, a precise and accurate formula is needed to determine the recommendation priority in an evaluation activity. Hence, the evaluation results will be more optimal. The purpose of this study was to demonstrate a DIVAYANA formula calculation simulation in determining recommendation priority. This research approach was evaluative with research stages that follow the DIVAYANA model stages. This research focused on the DIVAYANA formula part which lies at the Nominate stage. The subjects involved in the initial data collection were ten students from the Department of Informatics Education, Universitas Pendidikan Ganesha. The subjects involved in determining the weight for the evaluation component were four experts. The tool used for initial data collection and expert weight determination was questionnaires. The analysis technique of this research was done by checking the correctness of the DIVAYANA formula calculation based on case proof. The results showed that the process of DIVAYANA formula calculating simulation had been running well as evidenced by the accuracy of the results of determining priority recommendation.

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Internship recommendation system using simple additive weighting

Cited by 4 publications

References 13 publications

Students’ Tendencies in Choosing Technical and Vocational Education and Training (TVET): Analysis of the Influential Factors using Analytic Hierarchy Process

Students’ Tendencies in Choosing Technical and Vocational Education and Training (TVET): Analysis of the Influential Factors using Analytic Hierarchy Process

Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-Commerce

The simulation of divayana formula calculation to determine the priority of recommendation in evaluation activities

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