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Abstract

KITA Driving Course, located in Kisaran, North Sumatra, faces challenges in maintaining service quality amidst increasing student enrollment. Despite operating since 2013, the institution lacks a structured, data-driven evaluation system to assess student satisfaction. This study applies a quantitative descriptive approach using the K-Means clustering algorithm to classify student satisfaction levels. Data were collected from 100 respondents through a questionnaire based on the five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The K-Means algorithm grouped the satisfaction data into three categories: highly satisfied, satisfied, and dissatisfied. The majority of students were classified as satisfied (18 students), followed by highly satisfied (11 students), and dissatisfied (10 students). The findings indicate that the K-Means algorithm provides valuable insights into student satisfaction patterns, enabling targeted service improvements. The analysis also reveals that responsiveness and assurance were key areas of dissatisfaction, suggesting that instructor response time and safety assurance should be prioritized. These insights can help improve service strategies and can be adapted by other educational institutions for quality optimization through data analysis.

Keywords

Data Mining K-Means Clustering Satisfaction Level Driving Course Service Evaluation

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c): Shelvina Ayu Wardani, Ruri Ashari Dalimunthe, Abdulkarim Syahputra (2025)
How to Cite
Wardani, S., Dalimunthe, R., & Syahputra, A. (2025). Application of K-Means Data Mining to Measure the Level of Satisfaction of KITA Car Driving Course. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 25(1), 1-12. https://doi.org/https://doi.org/10.24036/invotek.v25i1.1248

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