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Record W2979307047 · doi:10.26740/jdmp.v3n2.p62-71

Manajemen Kurikulum Berbasis Industri Kreatif Pada Kompetensi Keahlian Kriya Kreatif Logam dan Perhiasan SMKN 12 Surabaya

2019· article· id· W2979307047 on OpenAlexaff
Sari Vaporizki

Bibliographic record

VenueJDMP (Jurnal Dinamika Manajemen Pendidikan) · 2019
Typearticle
Languageid
FieldSocial Sciences
TopicEducational Curriculum and Learning Methods
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Penelitian ini dilatarbelakangi oleh masalah pengangguran yang masih didominasi oleh lulusan Sekolah Menengah Kejuruan (SMK). Untuk mengatasi permasalahan tersebut melalui Instruksi Presiden (Inpres) Nomor 9 Tahun 2016 tentang Revitalisasi SMK, pemerintah melakukan revitalisasi pada komponen-komponen yang ada salah satunya kurikulum berbasis industri kreatif yakni kurikulum yang diselaraskan dengan kebutuhan industri. Tujuan penelitian ini untuk mendeskripsikan menelaah serta menganalisis perencanaan, pelaksanaan dan evaluasi kurikulum. Penelitian ini menggunakan pendekatan kualitatif dengan metode penelitian studi kasus. Hasil penelitian ini menunjukkan bahwa: 1) Perencanaan kurikulum berbasis industri kreatif dilakukan melalui rapat koordinasi dengan melibatkan pihak industri melalui kegiatan link and match dan rapat sosialisasi untuk memberikan pemahaman kepada semua pihak; 2) Pelaksanaan kurikulum berbasis industri kreatif melalui pembelajaran yang berbasis kreativitas yang dilaksanakan dikelas (bengkel) dan juga diluar kelas (Prakerin). Untuk mewujudkan pelaksanaan pembelajaran yang kreatif ini maka dibutuhkan kesiapan guru dalam mengajar dengan peningkatan kompetensi guru melalui magang guru, expert dengan perusahaan serta guru melakukan percobaan terlebih dahulu sebelum menyampaikan bahan ajar kepada peserta didik. 3) Evaluasi kurikulum berbasis industri kreatif ini disetiap pembelajaran terdapat assesment dari guru terkait ketercapaian peserta didik, evaluasi di semester 1 dan 2 melalui ujian-ujian baik berupa tes lisan, tes tertulis maupun unjuk kerja dan evaluasi yang melibatkan pihak eksternal yakni industri terkait melalui Ujian Kompetensi Keahlian (UKK).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0550.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.314
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2019
Admission routes1
Has abstractyes

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