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This study aims to compare and analyze the accuracy levels of four financial distress prediction models—Altman Z-Score, Springate, Grover, and Zmijewski—in anticipating the potential bankruptcy of companies subjected to delisting from the Indonesian Stock Exchange (IDX). The delisting phenomenon, which is strongly linked to severe financial deterioration, provided the core motivation for identifying the most reliable predictive instrument, utilizing secondary data from the annual financial reports of delisted companies during the 2019-2023 observation period. Descriptive analysis techniques were employed to calculate the accuracy rate and Type Error for each model. The comparative results consistently indicate that the Springate Model is the most effective, consistent, and accurate model for predicting financial distress in delisted firms, achieving an accuracy rate of 89% in both the first and second years prior to delisting, while the Altman Z-Score model exhibited lower accuracy (68.75% and 62.50%). This key finding emphasizes the superiority of the Springate Model as a crucial diagnostic tool for investors and regulatory bodies in assessing corporate bankruptcy risk.

This study aims to analyze the prediction of financial distress using the Altman Z-Score, Springate, and Zmijewski methods at PT Sri Rejeki Isman Tbk in 2019-2023. This type of research is descriptive research with a quantitative approach. Using secondary data with documentation techniques and literature studies in the form of related company financial reports, books, articles, journals and other publications related to the research topic. The sampling technique was carried out using a purposive sampling method. The sample in this study was obtained using a purposive sampling technique and obtained as many as 5 financial reports from the company PT Sri Rejeki Isman Tbk for the period 2019-2023. The results of the study show that the results of calculations using the Altman Z-Score method indicate that in 2019-2023 PT Sri Rejeki Isman Tbk experienced fluctuations in the company consistently still in the category of bankruptcy, the Springate method shows that the company experienced a decline in its financial performance, and the Zmijewski method shows that companies that experience fluctuations in financial performance conditions, Although there are fluctuations in the X-Score value and improvements in certain years.

Unstable financial conditions in insurance companies can serve as an early indicator of potential bankruptcy, which may have wide-ranging impacts on policyholders, shareholders, and the overall stability of the financial sector. Therefore, early detection of bankruptcy risk is critically important. This study aims to evaluate the effectiveness of the Springate model in identifying potential bankruptcy among insurance companies listed on the Indonesia Stock Exchange during the 2022–2024 period. The Springate model was chosen due to its simplicity and its ability to provide quantitative insights into a company's financial condition. Data were collected from the annual financial statements of 16 companies selected through purposive sampling based on the completeness and consistency of their financial reporting. The model applies the S-Score calculation as the basis for classifying companies into financial distress or non-financial distress categories. The analysis revealed that six companies consistently exhibited signs of financial difficulty, with three of them identified as being in a state of financial distress for three consecutive years. Meanwhile, the other ten companies demonstrated stable and healthy financial conditions throughout the observation period. These findings indicate that the Springate model is reasonably practical as an early detection tool for bankruptcy risk, particularly in the insurance sector, which is influenced by various internal factors such as risk management, as well as external factors like economic fluctuations and government regulations. Therefore, this model can be utilized as a decision-support tool for both management and investors in making strategic financial decisions.

This research is motivated by significant fluctuations and a decline in profits for companies in the clothing and luxury goods subsector listed on the IDX for the period 2021-2023, indicating potential financial difficulties. This research aims to evaluate the company's condition using four prediction models, namely Altman-ZScore, Springate, Fulmer, and Taffler, and to determine the most suitable model for the sector. The method applied is quantitative comparative with a purposive sample of 11 companies, using financial statement data from 2021-2023 and analyzed using the formulas for each predictive model. The research findings indicate that the four models produce different predictions regarding the company's financial condition. Additionally, there are differences in the accuracy levels of the four models. The Springate and Taffler models achieved the highest accuracy rate of 85%, followed by Altman at 67% and Fulmer at 64%. The findings of this study confirm that the Springate model is the most reliable tool for early warning for companies and stakeholders, enabling faster preventive measures to prevent bankruptcy.

Bankruptcy is a condition where a business cannot operate effectively due to severe financial difficulties it is currently experiencing.  This research purposes to analyze the ratio of the Altman, Springate, and Grover models in analyzing bankruptcy in food and beverage corporations listed on the IDX with reference to signaling theory. The data analysis approach used are One Way Anova test and accuracy level test. Using 20 company samples with purposive sampling method. The final proceeds of the research explain that there are significant differences between the Altman and Springate models, significant differences between the Altman and Grover models, and no significant differences between the Springate and Grover models in predicting bankruptcy in food and beverage companies for the period 2019-2023. The very accurate prediction model was achieved by the Grover model.

The purpose of this study is to determine the trends and developments in corporate tax avoidance studies using a mixed method approach. This method includes a quantitative approach in bibliographic analysis and literature review. Data were collected through searching national and international journals indexed in Emerald, Springer, and Scopus by referring to relevant publications. Data analysis includes: (1) mapping trends and developments in corporate tax avoidance research with Vosviewer software based on bibliographic studies; and (2) identifying trends and issues in corporate tax avoidance through literature review. The results of the Vosviewer analysis found five large groups with 40 main topics after selection. All topics are explained comprehensively through a literature review. This study provides a comprehensive picture of themes that are often studied in previous literature, thus becoming a reference for further research.

This study systematically analyses the predictive ability financial ratios have in relation to the emergence of financial distress among non-cyclical companies on the Indonesia Stock Exchange during the period 2020-2023. Secondary data was collected from a sample of 151 secondary data companies listed on the Indonesia Stock Exchange, spanning the years from 2020 to 2023. In order to ascertain the relationship between the independent variables (X1, X2, X3) and the dependent variable, Multiple Linear Regression models are utilised by employing the Eviews calculation application. As a model, the Springate model is employed, which is used to measure financial distress. The financial ratios selected for analysis encompass the liquidity ratio, the leverage ratio, and the profitability ratio. The findings of this study suggest that the profitability ratio exerts a substantial positive effect, or a moderate effect, on the phenomenon of financial distress. In contrast, the liquidity ratio and leverage ratio demonstrate an absence of statistically significant influence on the phenomenon of financial distress. Extensive analysis of the results indicates that financial distress, as measured by Springate, does not exert a substantial influence on the findings obtained from this study. The incorporation of diverse samples and models in subsequent studies is likely to introduce variations into the research outcomes.

Manufacturing companies in the textile and garment sector play an important role in the national economy, contributing to global development every year, creating jobs and encouraging domestic and foreign investment. However, the influence of globalization triggered by the influence of internal and external parties can cause many companies to experience financial difficulties. So researchers are interested in conducting research using secondary data in the form of annual financial reports. This study aims to evaluate financial ratios related to the company's financial distress conditions and identify factors causing financial difficulties in companies in the textile and garment sector listed on the Indonesia Stock Exchange in 2022-2023. This study uses the Springate (S-Score) method and logistic regression analysis with the results of the analysis showing that liquidity has a significant negative effect on financial distress, leverage has a positive insignificant effect on financial distress, and profitability has a significant negative effect on financial distress, and activity has a positive insignificant effect on financial distress.

Revitalization of Sendang spring tourism has an important role in developing local culture-based ecotourism. This community service program aims to implement the Sendang spring tourism revitalization strategy to support the development of local culture-based ecotourism and identify revitalization strategies that are in accordance with ecotourism principles and maintain the cultural values ​​of the surrounding community. This service is carried out through various activities that include environmental conservation, strengthening the local economy, and preserving the culture of the surrounding community. The method used in this program is a participatory approach with community involvement in observation, training, and direct assistance. The results of the activities show that revitalization through a culture-based ecotourism approach can increase public awareness of environmental conservation, strengthen the local economy, and maintain cultural values. The main challenge in this program is building a balance between resource utilization and sustainable conservation efforts. The conclusion of this service confirms that the revitalization of Sendang spring contributes to increasing the attractiveness of ecological and cultural-based tourism, and provides benefits to the community in a sustainable manner.