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Record W4400893082 · doi:10.53842/juki.v6i1.436

Sistem Aplikasi Pendukung Keputusan Menggunakan Metode AHP Dalam Menentukan Kinerja Pegawai Kantor Desa Suka Maju Deli Serdang

2024· article· en· W4400893082 on OpenAlex
Rianto Sitanggang, Immanuel H G Manurung, Alexander F.K. Sibero, Husnul Khair

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJUKI Jurnal Komputer dan Informatika · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumanitiesMathematicsArt

Abstract

fetched live from OpenAlex

Performance assessment is something that must be done by a company, whether private or public. Performance appraisal can provide rewards or punishment for an employee's performance based on whether the work they do is good or bad. The assessment carried out in this research was implemented by creating a system which was carried out using one method, namely AHP (Analytic Hierarchy Process). The measurement process is carried out by applying the waterfall method. The results of research carried out by applying the AHP decision support system method can provide efficient results and the level of data accuracy is close to perfect.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.007
Open science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.219
Teacher spread0.207 · 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