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ANALISIS PENGARUH UKURAN BANK KONVENSIONAL TERHADAP STABILITAS KEUANGAN DI INDONESIA

2025· article· W7131241148 on OpenAlexaboutno aff
Rahmi Rahmi, Sri Astuty, Diah Retno Dwi Hastuti, Basri Bado, Irwandi Irwandi

Bibliographic record

VenueREMITTANCE JURNAL AKUNTANSI KEUANGAN DAN PERBANKAN · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPanel dataQuarter (Canadian coin)Sample (material)PopulationFinancial stabilityCapital adequacy ratioRegression analysisCapital (architecture)

Abstract

fetched live from OpenAlex

Financial stability is a crucial aspect of the economic system, particularly in the banking sector, which serves as the primary intermediary institution. This study aims to analyze the effect of bank size on financial stability in Indonesia during the 2014–2023 period. Bank size is measured through total assets and Third Party Funds (DPK), while economic stability is proxied by the Z-score. The study population includes 92 conventional banks registered with the Financial Services Authority (OJK). The sample was determined using a stratified sampling method based on the Bank Group category, Core Capital (KBMI), in accordance with POJK No. 12/POJK.03/2021, resulting in 48 banks as samples. Data are in the form of quarterly financial reports from the first quarter of 2014 to the fourth quarter of 2023. The analysis was conducted using panel data regression using the Fixed Effect Model (FEM). The results show that total assets have a positive and significant effect on financial stability, while DPK has a negative and significant impact.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.007
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
Teacher spread0.204 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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