<![CDATA[The implementation of effective learning in vocational high schools requires understanding of students' learning style characteristics to optimize learning outcomes. Basic Automotive Engineering (DDTO) as a foundational subject in automotive engineering has its own complexity in accommodating diverse student learning preferences. This study aims to describe the learning style profile of students, describe the level of learning outcomes, and analyze the relationship between learning styles and learning outcomes in DDTO subject for Class X Light Vehicle Engineering students at SMKN 1 West Sumatra. The study employed a descriptive quantitative approach with correlational design. Research subjects were 60 students from Class X Light Vehicle Engineering Program at SMKN 1 West Sumatra in academic year 2023/2024, selected using purposive sampling technique. Research instruments consisted of validated learning style questionnaire with 50 items covering visual, auditory, and kinesthetic dimensions, and academic achievement data from Mid-Semester Examination (UTS) scores. Data analysis used descriptive statistics with mean score calculations, frequency distribution, and categorization interpretation. Research findings show: (1) students' learning styles are dominated by kinesthetic style (45%) with high intensity (63.3%), followed by visual style (30%) and auditory style (25%), with Class TKR 1 showing more balanced distribution while Class TKR 2 demonstrates stronger kinesthetic dominance (53.3%); (2) learning outcomes show extreme disparity between Class TKR 1 with 93.33% mastery level (28 out of 30 students) and Class TKR 2 with only 13.33% mastery level (4 out of 30 students), despite both classes having similar learning style patterns, indicating that factors other than learning styles also influence students' academic achievement. Research findings imply the importance of implementing integrated theory-practice learning approaches, developing high-quality technical visual media, and optimizing auditory modalities for specific applications such as engine sound-based diagnosis.]]>
