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Study of Potential Insolvency Among Cement Sector Firms Listed on the Indonesian Stock Exchange

2025· article· en· W4412826064 on OpenAlexvenueno aff
Muntu Abdullah, Sullvariany Tamburaka, Meliana Rohenni Manullang, Nur Asni

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Distress and Bankruptcy Prediction
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianInsolvencyStock exchangeBusinessAccountingFinancial systemFinanceLinguistics

Abstract

fetched live from OpenAlex

This research endeavors to evaluate the likelihood of insolvency for cement manufacturing businesses registered on the Indonesian stock exchange. The unpredictability of the business climate has an effect on the performance of manufacturing firms. This can have repercussions for the possibility of cement sub-sector manufacturing companies going bankrupt. In order to anticipate the potential bankruptcy of a company, financial ratio information can provide signals to investors, analysts, creditors, and company management. For this study, secondary data were obtained by downloading them from the website of the Indonesian stock market. For the purpose of determining whether or not cement subsector manufacturing companies are at risk of going bankrupt, the Almant Z-score statistical method is utilized. Based on the findings, it was determined that PT Indocement Tunggal Perkasa Tbk and PT Semen Batu raja Tbk were among the companies that were classified as being in a stable zone. There was a vulnerable zone or a gray region in which PT Semen Indonesia (Persero) Tbk was operating. The remaining companies, namely PT Solusi Bangun Indonesia Tbk, PT Waskita Beton Precast Tbk, PT Waskita Karya (Persero) Tbk, and PT Wijaya Karya Beton Tbk, are on the verge of defaulting on their financial obligations. This research contributes to strengthening signaling theory and previous research and provides useful information to investors, creditors, analysts, government, company management regarding the threat of potential bankruptcy of manufacturing companies. So that interested parties can take strategic steps in providing warnings for manufacturing companies that are facing financial problems on the Indonesia Stock Exchange.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.252
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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