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Record W4319239191 · doi:10.56248/marostek.v1i1.20

Sistem Pakar Diagnosa Penyakit Chelpagia Menggunakan Metode Dempster Shafer

2022· article· id· W4319239191 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

VenueJurnal Teknik Komputer Agroteknologi Dan Sains · 2022
Typearticle
Languageid
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMedicinePhysicsGynecology

Abstract

fetched live from OpenAlex

Teknologi Informasi membuat ketepatan dan kecepatan penyampaian informasi merupakan kebutuhan semua pihak. penyakit chepalgia (nyeri kepala atau sakit kepala) yang dirasakan oleh pasien anak-anak sampai dengan orang tua. Banyak pasien yang mengalami gejala penyakit chepalgia sebelum bertemu dokter dan mengalami kesulitan dalam berkonsultasi. Maka dari itu perlu adanya sistem untuk mempermudah pasien dalam melakukan tes melalui sistem dengan gejala penyakit yang dikeluhkan oleh pasien agar pasien lebih mudah untuk melakukan konsultasi tanpa harus datang menemui pakar. Tujuan dari penelitian ini adalah untuk mempermudah pasien untuk melakukan tes dan konsultasi melalui sistem pakar diagnosis penyakit chepalgia. Berdasarkan hasil penelitian yang dilakukan maka gejala tersebut yang telah dihitung untuk penyakit jenis Chelpagia Tension Headache, nilai densitas yang paling kuat adalah m15(P01) yaitu sebesar 0.95 atau jika dijadikan persentasi adalah sebesar 95%.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0090.009
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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