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Record W2917321746 · doi:10.33050/icit.v4i2.86

SISTEM INFORMASI ABSENSI BERBASIS WEB PADA BADAN PENANGGULANGAN BENCANA DAERAH KOTA TANGERANG

2018· article· id· W2917321746 on OpenAlexaff
Mulyati Mulyati, Rasyid Tarmizi, Angga Panugali

Bibliographic record

VenueICIT Journal · 2018
Typearticle
Languageid
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsComputer scienceHumanitiesOperating systemWorld Wide WebArt

Abstract

fetched live from OpenAlex

Badan Penanggulangan Bencana Daerah (BPBD) Kota Tangerang adalah lembaga pemerintahan non departemen yang bertugas menanggulangi bencana di wilayah Kota Tangerang yang berpedoman pada kebijakan Badan Nasional Penanggulan Bencana. Agar kinerja karyawan meningkat diperlukan inovasi di berbagai bidang. Salah satunya mengubah sistem absensi manual dengan sistem informasi absensi berbasis web agar kecurangan-kecurangan seperti pemalsuan tanda tangan dan pengisian jam kedatangan yang berbeda dapat diatasi. Sistem absensi berbasis web ini dirancang menggunakan software adobe dreamweaver CS6, visual paradigm for 6.4 enterprise edition, XAMPP. Setelah program jadi dilakukan pengujian dengan menggunakan black box. Data penelitian dikumpulkan dengan melakukan pengamatan, wawncara dengan pihak terkait, dan membaca buku dan literatur lainnya. Sistem absensi berbasis online ini lebih menghemat waktu dan memudahkan pihak terkait dalam mengelolah data sehingga informasi yang dihasilkan dapat lebih cepat, akurat, dan aman. Kata kunci: Perancangan Sistem Informasi, Absensi, Berbasis Web

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.032

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 head0.251
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations5
Published2018
Admission routes1
Has abstractyes

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