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Record W4302470269 · doi:10.53088/jikab.v1i1.7

BNI Syariah Sebelum Pandemi Covid 19 Ditinjau Dari Prediksi Financial Distress

2022· article· en· W4302470269 on OpenAlexaboutno aff
Yudi Siyamto, Yuwita Ariessa Pravasanti

Bibliographic record

VenueJurnal Ilmiah Keuangan Akuntansi Bisnis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Gray (unit)Financial distressPandemicMeaning (existential)DistressSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakBusinessActuarial scienceEconomicsPsychologyFinancial systemMedicineHistoryInfectious disease (medical specialty)Internal medicineDiseaseVirologyNuclear medicineClinical psychology

Abstract

fetched live from OpenAlex

In terms of the ratio of financing to third party funds, BNI Syariah has experienced a significant decline over the last 5 years. If this decline is not immediately resolved, it will be disrupted and the possibility of financial distress in banking can occur. The purpose of the study was to determine the financial distress experienced by BNI Syariah before the pandemic, namely 2016-2019. The research method used in this research is descriptive quantitative approach with calculations using modified Z-Score analysis. The results showed that in the last quarter of March 2016 to September 2018 before the COVID-19 pandemic, BNI Syariah was categorized as a gray area with a Z-Score value of 1.1 < Z" < 2.6, meaning that on this occasion it indicated that BNI Syariah was in the gray zone. gray area, so it cannot be ascertained whether the company is categorized as a healthy company or a company that is likely to go bankrupt, but in the quarter of December 2018 to June 2019 BNI Syariah is categorized as a distress zone, meaning that it indicates that the company has a high probability of going bankrupt.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.307
Teacher spread0.281 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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