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Record W4319239157 · doi:10.56248/marostek.v1i1.17

Sistem Pakar Diagnosa Penyakit Kaligata Menggunakan Metode Dempster Shafer

2022· article· id· W4319239157 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
KeywordsPhysicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Kaligata merupakan bentol-bentol di kulit disertai ruam kemerahan, terasa gatal, dan terkadang terasa perih menyengat.Biasanya kaligata muncul akibat reaksi alergi. Perkembangan dunia teknologi informasi telah banyak mengalami perubahan yang sangat pesat, seiring dengan kebutuhan manusia akan teknologi informasi. Oleh karena itu sangat diperlukan suatu sistem yang bisa menjadi informasi dan media pengganti pakar dalam mendiagnosa penyakit kaligata, Agar pasien yang memiliki gejala sebelumnya dapat lebih cepat mendapatkan informasi dan konsultasi melalui sistem yang sudah dibuat. Sistem ini dibuat dengan Bahasa pemrograman PHP dan database MYSQL. Hasil dari peneltian ini adalah untuk merancang dan membangun sistem diagnosa penyakit kaligat dengan menggunakan metode dempster shafer dan untuk mempermudah pasien dalam berkonsultasi tanpa harus datang kepakar. Berdasarkan gejala tersebut yang telah dihitung untuk penyakit Kaligata , nilai densitas yang paling kuat adalah Kaligata Fisik yaitu sebesar 0,844.

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 score0.515
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
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0100.011
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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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