MétaCan
Menu
Back to cohort
Record W4312824044 · doi:10.21063/jtif.2022.v10.1.1-8

PERANCANGAN SISTEM BANTUAN JARING PENGAMAN SOSIAL BERBASIS WEB DI KECAMATAN MAOSPATI, KABUPATEN MAGETAN

2022· article· id· W4312824044 on OpenAlexaff
Agus Sujarwadi

Bibliographic record

VenueJurnal Teknoif Teknik Informatika Institut Teknologi Padang · 2022
Typearticle
Languageid
FieldComputer Science
TopicInformation Retrieval and Data Mining
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Kantor Kecamatan Maospati merupakan kantor camat yang terletak di daerah Maospati, Magetan, Jawa Timur. Kantor camat ini bergerak dalam bidang pelayanan masyarakat, seperti aktivasi Kartu Tanda Penduduk Elektronik (E-KTP), membuat Kartu Keluarga, Pengaduan masyakat, Pelayanan Bantuan Masyarakat Miskin dan Pelayanan pengambilan dokumen adminduk online. Kantor Camat ini mempunyai sistem informasi untuk pengolahan data bantuan Jaring Pengaman Sosial (JPS) masih berbentuk manual dan belum terkomputerisasi sehingga lambat dalam proses pendataan penambah, perubahan, maupun penghapusan data. Tujuan membuat aplikasi sistem pengelolaan data bantuan jaring pengaman masyarakat di Kantor Kecamatan Maospati ini agar dapat membantu memberikan alternatif penyimpanan data ke database di Kecamatan Maospati. Metode dalam penelitian ini dengan memanfaatkan teknik tertentu yaitu Studi lapangan dan Studi kepustakaan. Hasil dari penelitian ini adalah perancangan sistem pengelolaan data bantuan jaring pengaman sosial pada Kecamatan Maospati Kabupaten Magetan

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.054
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0080.009
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0540.041

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Teknoif Teknik Informatika Institut Teknologi PadangSame topicInformation Retrieval and Data MiningFrench-language works237,207