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Record W4412354169 · doi:10.62712/juribmas.v4i1.441

Optimalisasi Metode Pembelajaran Interaktif dalam Kursus Komputer di LKP Karya Prima Secara Hybrid

2025· article· id· W4412354169 on OpenAlexaff
Muhammad Zen, Rusmin Saragih, Imeldawaty Gultom, Ayu Puspita Sari Sinaga, Eka Pandu Cynthia

Bibliographic record

VenueJurnal Hasil Pengabdian Masyarakat (JURIBMAS) · 2025
Typearticle
Languageid
FieldEngineering
TopicEngineering and Technology Innovations
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Permasalahan utama dalam pelaksanaan kursus keterampilan di Lembaga Karya Prima adalah metode pembelajaran yang masih bersifat satu arah, sehingga partisipasi peserta kurang optimal. Tujuan pengabdian ini adalah untuk mengoptimalkan metode pembelajaran interaktif dalam meningkatkan efektivitas kursus keterampilan. Metode pelaksanaan meliputi sosialisasi, workshop, dan praktikum interaktif yang melibatkan 25 peserta dari berbagai latar belakang usia produktif. Evaluasi dilakukan melalui observasi langsung, pre-test dan post-test, serta refleksi harian peserta. Hasil kegiatan menunjukkan peningkatan keterampilan praktis peserta sebesar 75% dan peningkatan soft skill seperti komunikasi dan kerja tim sebesar 60%. Sebagian peserta juga mampu memanfaatkan keterampilan yang diperoleh untuk membuka usaha kecil secara mandiri. Kegiatan ini membuktikan bahwa metode pembelajaran interaktif mampu meningkatkan efektivitas proses kursus serta memberi dampak nyata terhadap kemandirian ekonomi peserta.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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 designNot applicable
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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Citations0
Published2025
Admission routes1
Has abstractyes

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