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PENENTUAN JARAK EFISIEN PENGANTARAN PASIEN OLEH AMBULANCE KE RSUD KARAWANG DENGAN ALGORITME DIJKSTRA

2017· article· id· W2783178303 on OpenAlex

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

VenueILKOM Jurnal Ilmiah · 2017
Typearticle
Languageid
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsPhysicsDijkstra's algorithmMathematicsCombinatoricsShortest path problemGraph

Abstract

fetched live from OpenAlex

Banyak jalan yang menghubungkan dari berbagai puskemas yang ada di Karawang menuju ke RSUD Karawang dengan jarak tempuh yang berbeda-beda. Penelitian ini bertujuan untuk memberikan solusi kepada pihak ambulance untuk menentukan rute dalam pengantaran pasien ke RSUD Karawang dengan menggunakan algoritma Dijkstra. Algoritma ini digunakan dalam pencarian rute terpendek yang diharapkan dapat menjadi rute terefisien untuk mencapai tujuan dari lokasi yang diinginkan. Algoritma Dijkstra adalah sebuah algoritma rakus (greedy algorithm) yang dipakai dalam memecahkan permasalahan jarak terpendek (shortest path problem) untuk sebuah graf berarah (directed graph) dengan bobot-bobot sisi (edge weights) yang bernilai tak-negatif. Algoritme Dijkstra dapat diimplementasikan/digunakan sebagai alternatif dalam penentuan jarak efisien suatu daerah kedaerah yang lain dalam hal ini adalah penentuan jarak efisien pengantaran pasien oleh ambulan ke RSUD Karawang.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0090.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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