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All Journal Journal of Vocational, Informatics and Computer Education
Arrasyid Alifyan Cahaya
Nusa Putra University
Computer Science & IT Education

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Application of the Simple Additive Weighting (SAW) Method to Determine Work Placement Readiness of Vocational Training Institution Students (Case Study: LPK Global Partner Bridge) Arrasyid Alifyan Cahaya; Arny Lattu; Carti Irawan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.669

Abstract

Purpose – This study applies the Simple Additive Weighting (SAW) method within a Decision Support System (DSS) to assess work placement readiness of students at LPK Global Partner Bridge, a language and communication skills training institution preparing students for overseas employment, and generate structured recommendations. Methods – A quantitative descriptive approach was used with total sampling of 108 students from the 2025 cohort. Data were derived from attendance and final training scores using five criteria: Attendance, Script and Vocabulary, Conversation and Expression, Listening, and Reading. Weights were assigned through expert judgment and normalized proportionally. A threshold of 0.70, based on Indonesia’s Minimum Competency Standard (KKM), was applied. The SAW process included decision matrix construction, Max normalization, and weighted summation to obtain preference values (Vi), implemented in Microsoft Excel. Findings – Preference values ranged from 0.5061 to 0.9864, with a mean of 0.8036. A total of 92 students (85.19%) were classified as Ready, while 16 students (14.81%) were categorized as Not Ready. Research Implications – The model offers a structured and data-driven evaluation framework but is limited to quantitative criteria and excludes non-technical factors such as motivation and psychological readiness. Originality – Unlike prior SAW studies that primarily focus on ranking alternatives, this study extends the method to categorical classification of work placement readiness (Ready / Not Ready), using institutional training data. This approach enables more practical decision support for placement evaluation, providing a replicable framework for vocational training institutions.
Co-Authors Arny Lattu Carti Irawan