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Record W4404578742 · doi:10.62951/modem.v2i4.233

Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Usaha Masyarakat Berdasarkan Jenis Izin Usaha

2024· article· en· W4404578742 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

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

Abstract

fetched live from OpenAlex

Business legality is the identity of a business that legalizes a business so that it is recognized by the community. Business legality must be valid according to applicable laws and regulations so that the business can be protected by various documents that are valid in the eyes of the law. One of the supporting factors for the sustainability of a business is influenced by the existence of legal elements of the business being run. Business permits that must be owned by the community are a business establishment deed, business entity NPWP, trade business license (SIUP), company domicile certificate (SKDP) and business registration number (NIB). The increase in community businesses in Sei Bingai District, Langkat Regency has triggered many business permits that are not directly supervised by the local government. Community business permits are important documents in supervising the running of these community businesses. The types of businesses in Sei Bingai District also vary, such as tourism, C mining, trade, factories and so on.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.002

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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