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Record W2940669295 · doi:10.59697/jtik.v2i2.660

PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN PEMBERANGKATAN HAJI DENGAN METODE DECISION TREE PADA KANTOR KEMENTERIAN AGAMA KOTA BINJAI

2018· article· id· W2940669295 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

VenueJTIK (Jurnal Teknik Informatika Kaputama) · 2018
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicDecision Support System Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMathematicsTree (set theory)HumanitiesCombinatoricsArt

Abstract

fetched live from OpenAlex


 Pengetahuan tentang tata cara dan aturan tentang pelaksanaan haji bagi para calon jamaah haji merupakan hal terpenting sehingga semua proses yang wajib dalam pelaksanaan haji dilakukan oleh para jamaah haji. Pengolahan data calon jamaah haji pada Kantor Kementerian Agama Kota Binjai adalah suatu tugas dari sub bagian penyelenggaraan haji dan umroh. Untuk mengolah data jamaah tersebut masih banyak kekurangan karena proses masih dilakukan secara manual. Berdasarkan permasalahan diatas,maka penulis ingin melakukan penelitian membangun Perancangan Sistem Pendukung Keputusan Pemberangkatan Haji dengan Metode Decision Tree pada Kantor Kementerian Agama Kota Binjai. Dengan penerapan aplikasi ini diharapkan dapat memudahkan informasi terhadap pengelola haji khususnya kantor Kementerian Agama Kota Binjai .Sistem ini dapat menyajikan informasi yang tepat dan akurat untuk kebutuhan dalam menyajikan informasi dan data penyelenggaraan calon jamaah haji sehingga memudahkan pegawai dalam menginput data.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0070.010
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.053

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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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