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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) · pp. 1–7

DOI
10.15294/jvce.v11i2.45302
ISSN
2503-2305, 2339-0344
Published
2026-12-31
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 134 co-authorship links · showing the 40 most connected

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  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Eko SupraptonoAffiliation unknown11
Nur QudusAffiliation unknown9
Samsudin AnisAffiliation unknown7
Maria KrisnawatiAffiliation unknown6
Adhi KusumastutiAffiliation unknown6
Dwi WidjanarkoAffiliation unknown5
Anik MaghfirohAffiliation unknown4
Basyirun BasyirunAffiliation unknown4
Mia Hafizah TumanggerAffiliation unknown3
Rini KusumawardaniAffiliation unknown3
Muhammad KhumaediAffiliation unknown3
Nelly Setyowati AnggraeniAffiliation unknown3
Trisnani WidowatiAffiliation unknown3
Sri HandayaniUniversity of Brawijaya3
Okta PurnawirawanUniversity of Brawijaya2
Indah Indi AfifahAffiliation unknown2
Ajeng Rahma SudarniAffiliation unknown2
Noor HudallahAffiliation unknown2
Sita NurmasitahAffiliation unknown2
Roudlotus SholikhahAffiliation unknown2
SRI WAHYUNIAffiliation unknown2
Muh Fakhrihun Na'amAffiliation unknown2
Muhammad Nur FuadAffiliation unknown2
Jawade HafidzAffiliation unknown2
Delta ApriyaniAffiliation unknown2
Retno MarlangenAffiliation unknown2
Dewi MasrurohAffiliation unknown2
Bambang Sugeng SuryatnaAffiliation unknown2
Ely RudyatmiAffiliation unknown1
Shohihatur RohmanAffiliation unknown1
Denny Firman SetiyantoAffiliation unknown1
Pramaditya Putra Komala DewaAffiliation unknown1
Pedro AndhikaAffiliation unknown1
Nur ShabrinaAffiliation unknown1
Godham Eko SaputroAffiliation unknown1
Amalia ShofiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jvce/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (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>