MétaCan
Menu
Back to cohort
Record W4402380079 · doi:10.62951/bridge.v2i4.214

Jaringan Saraf Tiruan (JST) Memprediksi Penjualan UMKM Kota Binjai dengan menggunakan Metode Backpropagation

2024· article· en· W4402380079 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

VenueBridge · 2024
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMathematics

Abstract

fetched live from OpenAlex

The development of information technology that is currently developing serves to facilitate, accelerate, benefit and provide other alternatives for people who have businesses and have a big influence in the future. One of the things that is very influential is the sale of MSMEs. MSMEs are productive businesses owned by individuals or business entities that have met the criteria as micro businesses that have an important role in the economy because they provide employment, encourage local economic growth, and create innovation. MSMEs still face challenges such as limited access to financing, digital readiness, and marketing access that hinder the development of MSMEs. Therefore, it is necessary to take action to predict MSME sales in Binjai City using the backpropagation method so that later it can create new innovations and encourage community economic growth. Based on the process carried out using the backpropagation method, it can be seen that the value obtained has reached more than the predetermined target with a target value (t) of 0.26, learningrate 0.2, maximum epoch 10000 target error 0.01.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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