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Record W7009769429

Evaluasi Jumlah Tenaga Kerja pada Perumda Air Minum Tirta Sago Berdasarkan Beban Kerja

2023· dissertation· id· W7009769429 on OpenAlexaff

Bibliographic record

VenueAndalas University eThesis (Andalas University) · 2023
Typedissertation
Languageid
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsWork (physics)Work flowContext (archaeology)Homogeneous
DOInot available

Abstract

fetched live from OpenAlex

Sumber daya manusia (SDM) adalah individu produktif yang bekerja sebagai
\npenggerak suatu organisasi, baik itu di dalam institusi maupun perusahaan yang
\nmemiliki fungsi sebagai aset sehingga harus dilatih dan dikembangkan
\nkemampuannya. Selama menjalankan aktivitas kerja, manusia mengalami dua jenis
\nbeban kerja, yaitu beban kerja fisik dan beban kerja mental. Perumda Air Minum
\nTirta Sago merupakan salah satu perusahaan daerah yang memiliki jumlah
\nkaryawan sebanyak 97 orang. Masalah yang terdapat pada Perumda Air Minum
\nTirta Sago yaitu tenaga kerja yang sudah ada belum diketahui apakah beban kerja
\nyang dilakukan oleh pegawai sudah optimal atau belum serta jumlah pegawai tiap
\ndepartemen masih ada yang belum sesuai dengan pekerjaan yang dilakukan oleh
\npegawai Perumda Air Minum Tirta Sago. Penelitian ini akan dilakukan untuk
\nevaluasi jumlah tenaga kerja yang dibutuhkan Perumda Air Minum Tirta Sago
\nberdasarkan beban kerja yang optimal menggunakan metode Work Sampling dan
\nanalisis beban kerja. Berdasarkan beban kerja yang dilaksanakan bagian kas dan
\npenagihan membutuhkan 12 karyawan sedangkan kondisi aktual saat ini hanya 10
\norang. Bagian pembukuan dan rekening membutuhkan 6 karyawan sedangkan
\nkondisi aktual saat ini hanya 4 orang. Bagian umum dan kepegawaian membutuhkan
\n6 karyawan yang sesuai dengan kondisi aktual saat ini 6 orang. Bagian kas dan
\npenagihan membutuhkan 1 karyawan sedangkan kondisi aktual saat ini 3 orang.
\nOleh karena itu, dapat direkomendasikan untuk melakukan penambahan karyawan
\natau melakukan pemerataan karyawan pada bagian yang berlebih.

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, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0080.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.005

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 head0.041
GPT teacher head0.320
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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