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Record W4384435421 · doi:10.60090/kjm.v2i2.595.165-176

FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT MAHASISWA DI UNIVERSITAS KLABAT UNTUK MEMULAI USAHA

2021· article· id· W4384435421 on OpenAlexaff
Indrajit Indrajit, Lelasari Sijabat

Bibliographic record

VenueKlabat Journal of Management · 2021
Typearticle
Languageid
FieldSocial Sciences
TopicVocational and Entrepreneurial Education
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Pada era persaingan usaha yang sangat ketat dan terbatasnya lapangan pekerjaan dari perusahaan-perusahaan, sehingga menjadi seorang wirausahawan dan memulai usaha sendiri dapat menjadi pilihan yang baik bagi para mahasiswa bagi masa depan mereka. Penelitian ini dibuat untuk menganalisa pengaruh dari kreatifitas, inovasi, motivasi dan pengetahuan pada minat mahasiswa menjadi seorang wirausahawan. Variabel independen yang digunakan dalam penelitian ini terdiri dari faktor internal dan eksternal, dimana faktor internal terdiri dari kreatifitas, inovasi, motivasi, dan pengetahuan sebagai faktor variabel eksternal. Variabel dependen dalam penelitian ini adalah minat mahasiswa di dalam memulai usaha sendiri. Penelitian ini menggunakan data primer yang berasal dari para mahasiswa di Universitas Klabat yang sedang mengambil mata kuliah kewirausahaan dan mereka yang tidak mengambil mata kuliah tersebut sehingga dapat dilihat respon dari para responden dengan latar belakang yang berbeda-beda. Metode analisa statistik yang digunakan adalah analisa regresi berganda menggunakan t-test dan f-test menggunakan program SPSS. Hasil penelitian ini menunjukan bahwa faktor internal dan eksternal mempengaruhi secara signfikan minat mahasiswa untuk memulai usaha. Kata kunci: Inovasi, kewirausahaan, kreatifitas, motivasi, minat mahasiswa, pengetahuan

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.009

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 head0.020
GPT teacher head0.279
Teacher spread0.260 · 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 designObservational
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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