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Record W4409783021 · doi:10.52103/jaf.v2i2.1066

Perencanaan Sumber Daya Pemuda Lokal dalam Jabatan Manejerial Di Pembangkit Listrik Tenaga Air (PLTA) (Studi Kasus di PLTA Poso Energy)

2022· article· id· W4409783021 on OpenAlexaff
Rutfiah Mangun, Jeni Kamase, Serlin Serang

Bibliographic record

VenueJournal of Accounting and Finance (JAF) · 2022
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsPolitical science

Abstract

fetched live from OpenAlex

Penelitian ini dilakukan dengan tujuan untuk : (1). Untuk mengetahui perencanaan Sumber Daya Lokal pada tingkat/level manajerial di PLTA Poso Energy, (2). Untuk mengetahuan perencanaan Sumber Daya Manusia di PLTA Poso Energy dalam mengisi kebutuhan tenaga kerja pada tingkat manajerial pada PLTA. Penelitian ini menggunakan data primer dengan metode kualitatif yaitu mewawancarai informan kunci sebanyak 12 orang yang terdiri dari Manajer Umum dan Teknik 1 orang, Supervisor Senior 2 orang, supervisor muda 1 orang,karyawan tetap 3 orang dan karyawan kontrak 5 orang. Hasil dari pembahasan menunjukan bahwa : (1) Bahwa pemuda local pada posisi manajerial belum ada, karena masih kurangnya pemuda local yang professional pada bidang kelistrikan,(2) Pemuda local lebih banyak menempati posisi Supervisor Muda (Sp.M) dan operator lapangan. (3) Kendala pemuda local dalam menempati posisi / level strategis manajerial dikarenakan tingkat pendidikan dan keahlian pemuda local masih kurang bahkan tidak ada. (4) PT. Poso Energy dalam proses perekrutan karyawan tetap berpedoman pada proses seleksi yang ketat. (5) PT. Poso Energy melaksanakan proses seleksi berdasarkan kebutuhan perusahaan dengan standard dan prosedur perekrutmen yang ditetapkan. (6) PT. Poso Energy melakukan seleksi atau rekrutmen karyawan menggunakan 2 (dua) metode yaitu metode tertutup dilaksanakan untuk perekrutmen karyawan tetap dan metode terbuka untuk penyeleksian karyawan kontrak.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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