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Implementation of a Deep Learning Approach with the Project Based Learning Model in the Element of Fashion Style and Design Development

Journal of Vocational and Career Education · Vol. 11 No. 2 (2026) · hlm. 1–7

DOI
10.15294/jvce.v11i2.45302
ISSN
2503-2305, 2339-0344
Terbit
2026-12-31
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study aims to examine the effects, improvements, and differences in the skills and work attitudes of students through the integration of a Deep Learning approach with the Project Based Learning PjBL model in the Style and Design Development element of the Fashion Design and Production subject at vocational high schools. The study employed a quasi-experimental method with a pretest posttest non-equivalent control group design. The participants consisted of 41 eleventh grade students from the Fashion Design Program at SMK Widya Praja Ungaran, including 21 students in the control class that implemented conventional PjBL and 20 students in the experimental class that implemented PjBL integrated with the Deep Learning approach. The research instruments consisted of a skills assessment rubric and a work attitude scale that had been validated by six experts using the Percentage of Agreement test. Data were analyzed using the N Gain test and the Independent Sample t Test. The findings indicate that the N Gain value of skills in the experimental class reached 65.4%, which falls within the moderate category and is higher than the control class with 53% in the less effective category. The N Gain value of work attitude in the experimental class reached 92% in the effective category, while the control class obtained 72% in the fairly effective category. The results of the t test show significant differences in skills t = 13.175, p = 0.000 < 0.05 and work attitudes t = 3.212, p = 0.003 < 0.05. These findings indicate that the integration of a Deep Learning approach within the PjBL model significantly improves student skills and work attitudes in vocational fashion design learning.

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  • State University of Semarang
  • Lampung University
  • University of Brawijaya
  • Indonesia University of Education
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Rekan penulisOrganisasiArtikel bersama
Eko SupraptonoState University of Semarang9
Nur QudusState University of Semarang6
Samsudin AnisState University of Semarang5
Anik MaghfirohState University of Semarang4
Adhi KusumastutiState University of Semarang4
Mia Hafizah TumanggerState University of Semarang3
Dwi WidjanarkoState University of Semarang3
Okta PurnawirawanUniversity of Brawijaya2
Indah Indi AfifahState University of Semarang2
Sri Endah WahyuningsihState University of Semarang2
Ajeng Rahma SudarniState University of Semarang2
Noor HudallahState University of Semarang2
Rini KusumawardaniState University of Semarang2
Muhammad KhumaediState University of Semarang2
Nelly Setyowati AnggraeniAfiliasi tidak diketahui2
Delta ApriyaniState University of Semarang2
Sri Rizki HandayaniIndonesia University of Education2
Dewi Yulia MasrurohState University of Semarang2
Bambang Sugeng SuryatnaState University of Semarang2
Nur QudusState University of Semarang2
Sumardi SumardiLampung University1
Endang NurcahyaniLampung University1
Risma RasmaniLampung University1
Ely RudyatmiState University of Semarang1
Naufal TinovState University of Semarang1
Vita YulianaState University of Semarang1
Godham Eko SaputroState University of Semarang1
Retno MarlangenState University of Semarang1
Wulansari PrasetyaningtyasState University of Semarang1
Dwi Putri AsihState University of Semarang1
Luluk WitantiState University of Semarang1
Bilqis Lailatul MardliyahState University of Semarang1
Anindya Rahma SuwignyoState University of Semarang1
Agita Ainur RizqiyahState University of Semarang1
Trisnani WidowatiState University of Semarang1
Septian Eko PrasetyoState University of Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jvce/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en">Implementation of a Deep Learning Approach with the Project Based Learning Model in the Element of Fashion Style and Design Development</dc:title>
	<dc:creator xml:lang="en">Turosidah, Umi Chasanah</dc:creator>
	<dc:creator xml:lang="en">Sutopo, Yeri</dc:creator>
	<dc:creator xml:lang="en">Wahyuningsih, Sri Endah </dc:creator>
	<dc:creator xml:lang="en">Ihsani, Ade Novi Nurul </dc:creator>
	<dc:subject xml:lang="en">Deep Learning</dc:subject>
	<dc:subject xml:lang="en">Project Based Learning</dc:subject>
	<dc:subject xml:lang="en">Fashion Design Skills</dc:subject>
	<dc:subject xml:lang="en">Work Attitude</dc:subject>
	<dc:subject xml:lang="en">Vocational High School</dc:subject>
	<dc:description xml:lang="en">This study aims to examine the effects, improvements, and differences in the skills and work attitudes of students through the integration of a Deep Learning approach with the Project Based Learning PjBL model in the Style and Design Development element of the Fashion Design and Production subject at vocational high schools. The study employed a quasi-experimental method with a pretest posttest non-equivalent control group design. The participants consisted of 41 eleventh grade students from the Fashion Design Program at SMK Widya Praja Ungaran, including 21 students in the control class that implemented conventional PjBL and 20 students in the experimental class that implemented PjBL integrated with the Deep Learning approach. The research instruments consisted of a skills assessment rubric and a work attitude scale that had been validated by six experts using the Percentage of Agreement test. Data were analyzed using the N Gain test and the Independent Sample t Test. The findings indicate that the N Gain value of skills in the experimental class reached 65.4%, which falls within the moderate category and is higher than the control class with 53% in the less effective category. The N Gain value of work attitude in the experimental class reached 92% in the effective category, while the control class obtained 72% in the fairly effective category. The results of the t test show significant differences in skills t = 13.175, p = 0.000 &amp;lt; 0.05 and work attitudes t = 3.212, p = 0.003 &amp;lt; 0.05. These findings indicate that the integration of a Deep Learning approach within the PjBL model significantly improves student skills and work attitudes in vocational fashion design learning.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-12-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jvce/article/view/45302</dc:identifier>
	<dc:identifier>10.15294/jvce.v11i2.45302</dc:identifier>
	<dc:source xml:lang="en">Journal of Vocational and Career Education; Vol. 11 No. 2 (2026): December 2026; 1-7</dc:source>
	<dc:source>2503-2305</dc:source>
	<dc:source>2339-0344</dc:source>
	<dc:source>10.15294/jvce.v11i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jvce/article/view/45302/8667</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Journal of Vocational and Career Education</dc:rights>
</oai_dc:dc>