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Record W4388849310 · doi:10.35794/emba.v11i4.51553

EVALUASI PROSEDUR AKUNTANSI DALAM PERENCANAAN KEBUTUHAN DAN PENGANGGARAN BARANG MILIK DAERAH BERDASARKAN PP NO. 28 TAHUN 2020 TENTANG PENGELOLAAN BARANG MILIK DAERAH PADA BADAN PERENCANAAN PEMBANGUNAN DAERAH PROVINSI SULAWESI UTARA

2023· article· id· W4388849310 on OpenAlexaff
Brenda Tumewu, Jenny Morasa, Lady Diana Latjandu

Bibliographic record

VenueJurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi · 2023
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusinessBusiness administration

Abstract

fetched live from OpenAlex

Barang Milik Daerah adalah semua barang yang dibeli atau diperoleh atas beban Anggaran Pendapatan dan Belanja Daerah atau berasal dari perolehan lainnya yang sah. Setiap lembaga pemerintahan menyusun Rencana Kebutuhan Barang Milik Daerah sesuai dengan tujuannya masing-masing dengan mempertimbangkan jumlah anggaran yang disalurkan. Penelitian ini bertujuan untuk mengetahui prosedur akuntansi dalam perencanaan kebutuhan dan penganggaran barang milik daerah pada Badan Perencanaan Pembangunan Daerah Provinsi Sulawesi Utara apakah telah sesuai dengan Peraturan Pemerintah No. 28 Tahun 2020. Metode yang digunakan dalam penelitian ini yaitu metode kualitatif deskriptif merupakan metode yang fokus pada pengamatan yang mendalam. Dari hasil penelitian ini dapat menunjukkan bahwa untuk prosedur Akuntansi dalam Perencanaan Kebutuhan dan Penganggaran Barang Milik Daerah telah sesuai dengan Peraturan Pemerintah No. 28 Tahun 2020, dilihat dari Rencana Kebutuhan Barang Milik Daerah (RKBMD) dan prosedur akuntansi dalam hal ini mekanisme atau proses penyusunan perencanaan dan penganggaran barang milik daerah. Kata Kunci : Perencanaan Dan Penganggaran Barang Milik Daerah, PP No.28 Tahun 2020

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0050.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.003

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 head0.024
GPT teacher head0.230
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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