The Illomata International Journal of Management Ilomata International Journal of Tax & Accounting P-ISSN: 2714-9838; E-ISSN: 2714-9846 Volume 4, Issue 4, October 2023 Page No. 928-950 928 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Wiwik Sugiarti1, Nikmah2 12University of Bengkulu, Indonesia Correspondent: wiwiksugiartill11@gmail.com1 Received : September 15 2023 Accepted : October 25, 2023 Published : October 31, 2023 Citation: Sugiarti, W., Nikmah. (2023). The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach. Ilomata International Journal of Tax and Accounting, 4(4), 928-950. https://doi.org/10.52728/ijtc.v4i4.969 ABSTRACT: This research aims to predict the potential financial distress in companies with special notation on the Indonesia Stock Exchange during the period from January 1, 2021, to December 2022, using the Modified Altman Model (Z-Score) and the Springate Model (S-Score) approaches. Data for the study were obtained from the official website of the Indonesia Stock Exchange, employing purposive sampling as the sampling technique. Based on the criteria, a total of 280 research observations were obtained. The results indicate that both models can predict the potential financial distress of companies using financial ratios. Furthermore, the research findings reveal differences in the accuracy level of predicting potential financial distress between the Modified Altman Z-Score and Springate models. The Modified Altman Z-Score model demonstrates higher accuracy compared to the Springate model in predicting the potential financial distress of companies with special notation. This research provides important information for companies with special notation codes that experience financial distress, to immediately improve financial conditions, and provides a basis for strategic decision making to ensure the sustainability of the company and for investors and other interested parties can be used as a basis for investment decision making. Keywords: Potential Financial Distress, Special Notation Companies, Altman Z-Score, and Springate This is an open access article under the CC-BY 4.0 license. INTRODUCTION Improving the welfare of shareholders and maintaining the continuity of business operations are the main objectives of the company. To achieve this goal, companies must maintain their financial performance in order to remain competitive and avoid potential bankruptcy (Kisman & Krisandi, 2019). A company can go bankrupt for various reasons, one of which is financial distress (Cındık & Armutlulu, 2021; Dudley et al., 2022). Financial distress can be caused by internal factors, such as ineffective management of assets and liabilities, and external factors such as inflation, tax regulations, laws, and changes in foreign currencies (Kisman & Krisandi, 2019b) https://www.ilomata.org/index.php/ijtc mailto:wiwiksugiartill11@gmail.com https://doi.org/10.52728/ijtc.v4i4.969 The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 929 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc Financial distress is something that must be anticipated immediately because it can hamper the smooth running of a company's operational activities (Eliu, 2014; Kartika et al., 2020; López- Gutiérrez et al., 2015; Nuswantara et al., 2023) A strategy that can be used to increase anticipation of financial distress is the Financial distress Prediction (FDP) approach. The FDP approach can help company management control financial risk, and help them modify investment plans to minimize risk as well as help investors understand the profitability of the company, (Li & Wang, 2023a). Financial distress conditions faced by a company can be a signal to investors that the company is experiencing serious problems that if allowed to continue can lead to bankruptcy. In December 2018, the Indonesia Stock Exchange has introduced a "Special Notation" feature that can be utilized by potential investors to find out the state of a company. This notation can provide clues about the initial condition of a company based on evaluations conducted by the Indonesia Stock Exchange. Research on the potential for financial distress has been conducted with varied results but is more focused on companies in certain industrial sectors in general (Lestari et al., 2021) (Munira et al., 2021)(Martini et al., 2023)(Effendi, 2018); (Pulungan & Hartini, 2018); (Suidarma et al., 2022) (Zhu et al., 2023); (Tan & Wibisana, 2020). This study differs from previous studies as it predicts the potential for financial distress in companies included in the group of companies with a 'special notation' that have been evaluated by the Indonesia Stock Exchange, and the evaluation results indicate that the company is in trouble. The FDP approach in this study uses two models, namely the Modified Altman model (Z-Score) and the Springate model (S-Score). The main objectives of this study are (1) predicting the potential for companies with special notations on the Indonesia Stock Exchange to experience financial distress; (2) compare the two models to determine which model is more accurate in predicting financial distress. Signalling Theory Signalling theory was first introduced by (Spence michael, 1973) in his research entitled Job Market Signaling. The information that the company discloses signals to investors about the condition of the company and can reduce information asymmetry. Quality and integrated financial reporting information will reduce information asymmetry for principals, agents, and third parties. (Suranta et al., 2023) Outsiders will also react positively to good signals because market reaction depends heavily on the fundamental signals of the company. Investors will only invest in a company if they believe that it can generate more value for their money than if they put it elsewhere. Therefore, investors' focus will be on the company's performance presented in the company's financial statements. Signal theory that explains how companies provide positive and negative signals from financial statements (Gandhy & Fardinal, 2019). Based on signal theory, financial distress conditions experienced by companies can be a signal to investors that the company is experiencing serious problems that if allowed to continue can lead to bankruptcy. Financial distress Financial distress is generally understood to be the state in which a business is unable to pay its creditors and satisfy its own requirements(Friedl & Drescher, 2013) Financial distress occurs when a company faces financial problems and faces financial risks (Li & Wang, 2023b). Financial distress has a different meaning from bankruptcy, where financial distress conditions in companies occur https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 930 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc before bankruptcy and become the cause of bankruptcy. So not all companies that experience financial distress will end in bankruptcy (Nikmah & Sulestari, 2021). Financial distress can be caused by internal and external factors, such as long-term losses in the company's operational activities and government policies that can increase business expenses (Purwaningsih & Aziza, 2019). (Sari et al., 2021) claimed that financial hardship may arise from a company's inability to control and sustain stable financial performance and that financial crisis and bankruptcy scenarios can be anticipated from the company's financial statements by producing financial ratio analysis that is pertinent to the business. Research (Widarjo & Setiawan, 2009) proves that a business is in financial distress if it experiences losses in two consecutive fiscal years (periods). Information about financial distress is used by companies to accelerate management actions in preventing problems before bankruptcy such as mergers or takeovers so that companies can pay off debts and improve company performance, as well as early warnings before bankruptcy (Piatt & Piatt, 2002). Altman Model (Z-Score ) Edward I. Altman originally presented the Altman model in 1968. This model is designed to assess a company's bankruptcy risk and can also be used to measure its overall financial performance. (Sari, 2016). Altman's Z-Score is a multivariate formula utilized to gauge financial distress and evaluate a company's financial well-being. To make his model applicable to all types of firms, including both manufacturing and non-manufacturing ones, Altman made modifications to it. The formula of the modified Altman Z-Score model (Altman,1995) is called the Modified Altman (Z- Score): Information: X1 = Working Capital / Total Assets X2 = Retained Earning / Total Assets X3 = Earnings Before Interest and Taxes / Total Assets X4 = Book Value of Equity / Total Liabilities Springate Model (S-Score) According to (Denhas & Subroto, 2014) Gorgon L.V. Springate created this model for the first time in 1978. In its formulation, by employing four out of the 19 financial parameters and the Multiple Discriminant Analysis formulation approach, Springate assessed the company's level of financial distress (MDA). The model can predict financial distress with an accuracy rate of 92.5%, with the formula: Information: X1 = Working Capital / Total Assets https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 931 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc X2 = Profit Before Interest and Tax / Total Assets X3 = Profit Before Tax / Current Debt X4 = Sales / Total Assets (Lestari et al., 2021) analyzed financial distress in tourism, hospitality and restaurant sub-sector companies with Altman (Z-Score), Springate (S-Score), Zmijewski (X-Score), and Grover (GScore) analysis methods, the results of the study proved that several companies experienced financial distress and the Springate (S-Score) model has the highest accuracy rate, reaching 68.75%. In line with (Effendi, 2018b) research on issuers in the transportation service sector using the Altman, Springate, Zmijewski, Foster, and Grover methods, and proved that the majority of issuers experienced financial distress and the Springate model showed the highest prediction model accuracy results. (Munira et al., 2021b) measured the potential for bankruptcy in mining companies using the Altman Modified Z-Score and Springate methods, and the results proved that there were several companies that experienced financial distress, and overall the Altman Z-Score method had a higher accuracy rate of 66.49%. (Martini et al., 2023b) examined the comparison of financial distress predictions using Altman, Springate, Zmijewski, and Grover models at PT Garuda Indonesia (Persero) Tbk, and the results indicated that PT Garuda Indonesia (Persero) experienced financial distress, and prediction models gave varying results. Previous research has produced a variety of findings and levels of accuracy, depending on a number of specific factors and variables used in each industry or company studied. Conceptual Framework and Hypothesis Figure.1 Conceptual Framework Based on the conceptual framework and research objectives that have been described earlier, hypotheses can be developed from this study as follows: H1: Altman model modified by Z-Score is able to predict the potential for financial distress in companies in a special notation of the Indonesia Stock Exchange H2 : Springate S-Score model is able to predict potential financial distress in companies in a special notation of the Indonesia Stock Exchange Companies in IDX Special Notation Prediction of financial distress Potential for financial distress Not potentially for financial distress Altman (Z-Score) Springate (S-Score) https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 932 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc H3: There is a difference in the accuracy of financial distress prediction models between Altman prediction models modified Z-Score and Springate S-Score METHOD This research uses secondary data in the form of company financial statements obtained from through www.idx.co.id website. Sampling using purposive sampling techniques, namely: companies are listed in the special notation of the Indonesia Stock Exchange during the year from January 1, 2021 to December 2022; the company obtains special notation codes except with L and S codes; Have complete financial statements that have been audited and published during the 2018-2022 observation period and can be accessed and financial statements are presented in rupiah. This research uses secondary data in the form of company financial statements with special notation codes obtained through www.idx.co.id website. Companies with special notation are companies that receive warnings from the Indonesia Stock Exchange because they have problems that are not in accordance with existing regulations. Special notation is marked by several codes in the form of letters totaling 17 codes that have different meanings according to the company's problems. Sampling using purposive sampling techniques, with the following criteria: 1. The company is listed in the special notation of the Indonesia Stock Exchange during 2021- 2022 2. The company gets special notation codes except with L and S codes (they cannot be used as samples because they do not meet the requirements of both analysis models to be used) 3. Have complete financial statements that have been audited and published during the observation period 2018-2022 and can be accessed 4. Financial statements using rupiah currency 5. Have all the data needed for each prediction model. The analysis method involves the application of the Modified Altman Z-Score model and the Springate model, with the analysis process conducted in the following stages: 1) Z-Score calculation using Altman Modified Z-Score method : Z’’ = 6,56X1 + 3,26X2 + 6,72X3 + 1.05X4 Table 1. Altman Model Modified Z-Score Altman Modifikasi Z-Score Keterangan > 2,6 Non Distress 1,1 - 2,6 Grey area <1,1 Distress https://www.ilomata.org/index.php/ijtc http://www.idx.co.id/ The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 933 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 2) S-Score calculation using Springate model : S-score = 1.03 X1 + 3.07 X2 + 0.66 X3 + 0.4X4 Table 2. Model Springate S-Score 3) Accuracy testing of the prediction mode Testing the accuracy of prediction models is used to determine valid estimates and errors in the results of calculating the score of each prediction model, this stage is a way to determine which model is more precise in forecasting financial trouble for businesses listed on the IDX with specific note between 2018-2022, the accuracy level is calculated as follows (Munira et al., 2021b): Information: a. The number of correct predictions is the number of companies in the special notation of the Indonesia Stock Exchange that are predicted to experience financial distress and indeed experience financial distress, and if calculated using the modified Altman model (Z- Score), and the Springate model states the same thing as the statement of the Indonesia Stock Exchange. b. The number of samples is the number of companies sampled multiplied by the length of the year of observation. 4) Calculate the error rate of a prediction model After calculating the accuracy rate, the prediction error rate for each model used is calculated. Error types are divided into two types, namely Type I and Type II errors. The error rate is calculated as follows, (Munira et al., 2021) Information: a. Type I error is an error that occurs when a model predicts that the sample studied is not experiencing financial difficulties, but in fact the sample is recorded as a company experiencing financial difficulties. Springate S-Score Keterangan > 1,062 Non Distress 0,862 – 1,062 Grey area <0,862 Distress Accuracy Rate = Number of Correct Predictions x 100% Number of Samples Error Type I = Number of Errors I x 100% Number of Samples Error Type II = Number of Errors II x 100% Number of Samples https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 934 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc b. Type II error is an error that occurs when a prediction model estimates that the sample studied has financial difficulties, but in fact the sample is recorded as a company that does not experience financial difficulties. RESULT AND DISCUSSION There are 125 companies included in the special notation of the Indonesia Stock Exchange (IDX) from January 1, 2021 to December 2022, and based on the criteria, 56 sample companies or 280 observations were obtained. There were 125 companies included in the special notation of the Indonesia Stock Exchange (IDX) from January 1, 2021 to December 2022, and only 56 companies met the criteria sampled during the 5-year observation period or as many as 280 observations. Descriptive Statistics The results of descriptive statistical tests in this study can be seen in the following table: a. Modified Altman Model (Z-Score) Table 3. Altman Descriptive Statistics Modification (Z-Score) Descriptive Statistics N Minimum Maximum Mean Std. Deviation Working Capital to Total Assets (X1) 280 -73.91 .95 -.8356 604.904 Retained Earnings to Total Assets (X2) 280 -126.68 .76 -37.102 1.591.732 Earnings Before Interest and Tax to Total Assets (X3) 280 -15.19 3.82 -.2766 140.954 Book Value of Equity to Book Value of Total Debt (X4) 280 -.99 431.53 5.422 2.910.697 Valid N (listwise) 280 Source : Data processed by the author (2023) b. Springate Model Table 4. Springate Descriptive Statistics Descriptive Statistics N Minimum Maximum Mean Std. Deviation Working Capital to Total Assets (X1) 280 -73.91 .95 -.8356 604.904 Earnings Before Interest and Tax to Total Assets (X2) 280 -15.19 3.82 -.2766 140.954 Net Profit Before Taxes to Current Liabilities (X3) 280 -71.04 15.89 -.7178 549.840 Sales to Total Assets (X4) 280 -1.03 28.82 .8795 225.790 Valid N (listwise) 280 Source : Data processed by the author (2023) https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 935 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc Working Capital to Total Assets (WCTA) is a ratio that reflects a company's net working capital to total assets. The minimum value range from -73.91 to a maximum of 0.95 showed a significant variation of 604.9 in the study sample. The average WCTA ratio of -0.83 indicates an unfavorable value distribution. This indicates that the likelihood of financial distress increases with an average that is close to the minimum value. Retained Earnings to Total Assets is a ratio that describes a company's ability to generate retained earnings from total assets. The minimum range from -126.68 to a maximum of 0.76 showed a significant variation of 1.59 in the study sample. The average of this ratio of -37.1 indicates an unfavorable distribution of profitability. Companies tend to be less efficient in generating enough revenue to cover their costs. Earnings Before Interest and Tax to Total Assets is a productivity indicator that shows the distribution of earnings before interest and tax to total assets. The minimum value range from - 15.19 to a maximum of 3.82 showed a significant variation of 140.9 in the study sample. This average ratio of -0.27 indicates that most companies in the sample have low levels of productivity, indicating inefficiencies in managing their assets. Book Value of Equity to Book Value of Total Debt, this ratio indicates the maximum amount of asset loss that can occur before total liabilities exceed the book value of equity. The minimum value range of -0.99 to a maximum of 431.53 shows a significant variation of 2.91 in the research sample. The average of this ratio of 5.42 indicates a poor distribution value, as there is quite a lot of debt compared to the company's capital.. Earnings Before Interest and Tax to Total Assets is a productivity indicator that shows the distribution of earnings before interest and tax to total assets. The minimum value range from - 15.19 to a maximum of 3.82 showed a significant variation of 140.9 in the study sample. This average ratio of -0.27 indicates that most companies in the sample have low levels of productivity, indicating inefficiencies in managing their assets. Sales to Total Assets is a ratio that describes the efficiency of using assets to generate sales. The minimum value range from -1.03 to a maximum of 28.82 showed a significant variation of 225.7 in the study sample. An average of 0.87 indicates an under-distribution of value, indicating potential financial distress due to low sales compared to asset usage. Data Testing and Hypothesis Testing The following are the results of calculations (Z-Score) and S-Score as well as predictions of financial distress using both the modified Altman model and the Springate model in companies included in special notation from January 1, 2021 to December 2022 with a period of 5 years (2018-2022). https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 936 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc a. Modified Altman model (Z-Score ) Table 5. Altman Calculation Results modification (Z-Score ) Altman (Z-Score) No Company Code 2018 2019 2020 2021 2022 Z- score Kategori Z- score Kategori Z- score Kategori Z- score Kategori Z- score Kategori 1 KIAS 4,12 Non-FD -1,72 FD 2,09 Grey Area 3,40 Non-FD 1,99 Grey Area 2 RMBA 1,21 Grey Area 1,74 Grey Area 4,20 Non-FD -0,06 FD 2,74 Non-FD 3 AKKU 3,88 Non-FD -0,54 FD 3,30 Non-FD -3,19 FD -2,49 FD 4 HDTX -19,13 FD -18,84 FD -21,16 FD -23,89 FD -32,31 FD 5 WSBP 3,17 Non-FD 2,90 Non-FD -4,86 FD -12,32 FD -9,36 FD 6 BIMA -8,31 FD -1,68 FD -4,66 FD -5,43 FD -3,19 FD 7 TALF 7,89 Non-FD 5,67 Non-FD 4,15 Non-FD 3,95 Non-FD 3,80 Non-FD 8 LMSH 10,81 Non-FD 7,47 Non-FD 7,68 Non-FD 9,56 Non-FD 11,04 Non-FD 9 ARKA -2,05 FD 0,47 FD -0,95 FD -0,09 FD 0,54 FD 10 IKAI 0,28 FD 0,31 FD -0,41 FD -0,39 FD 0,54 FD 11 INTA -0,72 FD -1,68 FD -11,02 FD -12,68 FD -5,37 FD 12 TIRT -0,48 FD -1,15 FD -17,35 FD -15,93 FD -16,31 FD 13 JGLE 2,81 Non-FD 2,93 Non-FD 2,69 Non-FD 2,83 Non-FD 0,76 FD 14 JSPT 4,36 Non-FD 3,23 Non-FD 2,16 Grey Area 1,69 Grey Area 2,18 Grey Area 15 MIRA -8,79 FD -9,55 FD -11,66 FD -12,52 FD -16,58 FD 16 MKNT 1,68 Grey Area 3,07 Non-FD 2,58 Grey Area 2,32 Grey Area 1,06 FD 17 MDIA 4,91 Non-FD 3,57 Non-FD 3,36 Non-FD 4,35 Non-FD 2,58 Grey Area 18 MDRN -12,26 FD -11,85 FD -32,45 FD -26,33 FD -25,32 FD 19 LCKM 14,57 Non-FD 15,13 Non-FD 17,04 Non-FD 17,83 Non-FD 20,54 Non-FD 20 SAFE -11,88 FD -10,82 FD -12,78 FD -16,19 FD -14,38 FD 21 POSA -4,45 FD -4,89 FD -6,73 FD -9,71 FD -11,77 FD 22 PNSE 2,22 Grey Area 1,91 Grey Area 0,41 FD -0,20 FD 0,03 FD 23 ANDI 0,42 FD 1,63 Grey Area 0,68 FD 1,21 Grey Area 1,19 Grey Area 24 BIKA 3,83 Non-FD 3,40 Non-FD 0,13 FD 0,95 FD -0,96 FD 25 CMPP -15,52 FD -10,96 FD -14,15 FD -18,93 FD -19,89 FD 26 CNKO -11,10 FD -11,03 FD -22,19 FD -18,48 FD -24,56 FD 27 CTTH 0,74 FD 0,01 FD -1,33 FD -1,51 FD -2,20 FD 28 DADA 4,15 Non-FD 2,45 Grey Area 2,96 Non-FD 4,68 Non-FD 4,07 Non-FD 29 DPUM 3,74 Non-FD 1,79 Grey Area -2,76 FD 0,59 FD 0,46 FD 30 DEAL 1,72 Grey Area 0,87 FD -2,76 FD -7,06 FD -6,07 FD 31 BTEK 1,52 Grey Area 0,99 FD -1,04 FD -0,55 FD -0,76 FD 32 BUVA 0,30 FD -0,56 FD -9,30 FD -9,93 FD -10,66 FD 33 GMTD 4,47 Non-FD 4,20 Non-FD 3,39 Non-FD 3,60 Non-FD 3,03 Non-FD 34 IIKP 10,63 Non-FD 18,11 Non-FD 13,74 Non-FD 11,30 Non-FD 7,84 Non-FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 937 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 35 GLOB - 133,62 FD - 687,79 FD - 630,13 FD - 583,35 FD - 1000,88 FD 36 HADE 39,76 Non-FD -48,97 FD -34,32 FD -34,52 FD -35,02 FD 37 TOPS 2,55 Grey Area 3,07 Non-FD 2,08 Grey Area 2,36 Grey Area 1,30 Grey Area 38 TRIO - 157,46 FD - 233,44 FD - 323,41 FD - 383,69 FD -395,95 FD 39 VIVA -1,07 FD -3,36 FD -5,18 FD -6,39 FD -9,00 FD 40 SMRU 0,56 FD -1,44 FD -5,15 FD -9,33 FD -8,60 FD 41 SONA 7,03 Non-FD 9,59 Non-FD 10,20 Non-FD 13,39 Non-FD 5,58 Non-FD 42 TARA 15,87 Non-FD 15,28 Non-FD 23,81 Non-FD 49,79 Non-FD 54,10 Non-FD 43 TAXI -13,54 FD -22,00 FD -33,85 FD -21,74 FD -47,83 FD 44 DIGI 20,67 Non-FD 12,37 Non-FD 1,48 Grey Area -4,30 FD -14,83 FD 45 TAMA -0,62 FD -1,96 FD -2,28 FD -2,29 FD -1,36 FD 46 TIRA 2,06 Grey Area 2,21 Grey Area 1,73 Grey Area 1,32 Grey Area 1,53 Grey Area 47 WOWS -0,57 FD 5,16 Non-FD 5,86 Non-FD 5,69 Non-FD 5,30 Non-FD 48 NASA 13,83 Non-FD 19,25 Non-FD 19,24 Non-FD 18,53 Non-FD 19,76 Non-FD 49 REAL 6,31 Non-FD 102,25 Non-FD 144,17 Non-FD 151,33 Non-FD 456,48 Non-FD 50 PPRO 2,92 Non-FD 1,57 Grey Area 1,19 Grey Area 2,06 Grey Area 2,09 Grey Area 51 KBAG -1,57 FD 3,63 Non-FD 8,68 Non-FD 8,87 Non-FD 11,79 Non-FD 52 KREN 9,20 Non-FD 9,60 Non-FD 8,89 Non-FD 6,73 Non-FD 6,40 Non-FD 53 KOTA 3,11 Non-FD 7,64 Non-FD 4,23 Non-FD 4,18 Non-FD 4,29 Non-FD 54 AGAR 2,43 Grey Area 3,60 Non-FD 4,33 Non-FD 4,21 Non-FD 3,16 Non-FD 55 SBAT 0,35 FD -0,48 FD -1,25 FD -0,80 FD -3,74 FD 56 SCPI 5,36 Non-FD 7,07 Non-FD 5,33 Non-FD 10,53 Non-FD 9,44 Non-FD Source : Data processed by the author (2023) Based on table 5. above shows that the results of the analysis using Altman Modified Z-Score in 2018 there were 24 companies experiencing Financial Distress, there were 8 companies in gray areas, and 24 other companies were included in the category of companies that were Non- Financial Distress. In 2019, there were 26 companies experiencing financial distress. Meanwhile, 7 companies are in the grey area stage, and 23 other companies are included in the category of healthy companies or Non-Financial Distress. In 2020, there were 29 companies experiencing Financial Distress. There are 7 companies that are in the grey area stage. While 20 other companies are included in the category of healthy companies (Non Financial Distress). In 2021, there were 31 companies experiencing Financial Distress, and there were 6 companies that were in the gray area. Meanwhile, as many as 19 companies are included in the category of healthy companies (Non Financial Distress). Furthermore, in 2022 there are 32 companies that are declared to be experiencing financial difficulties, and 7 companies that are at the stage (gray area). While the other 17 companies are healthy companies (Non Financial Distress). These results show that the Altman model modified z-score is able to predict financial distress in companies with a special notation code of the Indonesia Stock Exchange, thus the first hypothesis (H1) is accepted. https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 938 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc b. Springate Model Table 6. Springate Model Calculation Results Springate (S-Score) No Company Code 2018 2019 2020 2021 2022 S- Score Kategori S- Score Kategori S- Score Kategori S- Score Kategori S-Score Kategori 1 KIAS -0,072 FD -2,129 FD -0,245 FD 0,405 FD 0,401 FD 2 RMBA 0,709 FD 0,780 FD -0,354 FD 0,585 FD 1,045 Grey Area 3 AKKU -1,670 FD -1,817 FD -0,354 FD -2,452 FD -0,971 FD 4 HDTX -4,294 FD -0,794 FD -0,735 FD -0,622 FD -1,163 FD 5 WSBP 0,736 FD 0,648 FD -2,241 FD -2,009 FD -0,418 FD 6 BIMA 0,244 FD 0,323 FD -0,995 FD -0,726 FD -0,117 FD 7 TALF 1,015 Grey area 0,729 FD 0,543 FD 0,542 FD 0,610 FD 8 LMSH 1,352 Non-FD -0,072 FD 0,374 FD 1,253 Non-FD 0,667 FD 9 ARKA 0,001 FD 0,259 FD -0,267 FD 0,210 FD 0,451 FD 10 IKAI 0,319 FD -0,487 FD -0,537 FD -0,291 FD -0,288 FD 11 INTA -0,076 FD -0,487 FD -2,179 FD -1,793 FD -0,248 FD 12 TIRT 0,258 FD -0,024 FD -4,469 FD -2,506 FD -1,950 FD 13 JGLE 0,258 FD -0,134 FD -0,154 FD -0,064 FD -2,638 FD 14 JSPT 1,001 Grey area 0,351 FD -0,303 FD -0,423 FD 0,071 FD 15 MIRA 0,374 FD 0,101 FD -0,392 FD -0,282 FD -0,791 FD 16 MKNT 2,262 Non-FD 1,869 Non-FD 2,520 Non-FD 1,716 Non-FD 1,736 Non-FD 17 MDIA 0,459 FD 0,366 FD 0,366 FD 0,411 FD 0,147 FD 18 MDRN -0,990 FD -0,630 FD -3,239 FD 1,324 Non-FD 1,004 Grey Area 19 LCKM 1,423 Non-FD 1,155 Non-FD 1,397 Non-FD 1,154 Non-FD 1,061 Grey Area 20 SAFE -1,006 FD -0,491 FD -0,941 FD -1,175 FD -0,469 FD 21 POSA -3,859 FD -1,140 FD -1,234 FD -1,580 FD -1,744 FD 22 PNSE 0,008 FD -0,034 FD -0,967 FD -0,778 FD -0,188 FD 23 ANDI 0,231 FD 0,421 FD -0,132 FD 0,140 FD 0,007 FD 24 BIKA 0,438 FD 0,298 FD -0,033 FD 0,409 FD -0,364 FD 25 CMPP -1,746 FD 0,439 FD -2,798 FD -2,740 FD -2,371 FD 26 CNKO -1,987 FD -0,364 FD -2,218 FD -1,276 FD -1,320 FD 27 CTTH 0,304 FD 0,086 FD -0,105 FD 0,001 FD -0,176 FD 28 DADA 0,792 FD 0,575 FD 0,318 FD 0,560 FD 0,430 FD 29 DPUM 0,792 FD -2,243 FD -5,765 FD -1,034 FD -0,250 FD 30 DEAL 0,470 FD 0,167 FD -1,122 FD -1,028 FD -0,417 FD 31 BTEK 0,282 FD -0,137 FD -1,960 FD -0,491 FD -0,589 FD 32 BUVA -0,157 FD -0,387 FD -2,896 FD -1,878 FD -1,718 FD 33 GMTD 0,447 FD -0,127 FD -0,347 FD 0,049 FD 0,181 FD 34 IIKP -0,657 FD 3,321 Non-FD - 28,594 FD - 18,935 FD -13,449 FD 35 GLOB -4,881 FD - 55,772 FD - 67,513 FD - 66,375 FD - 119,959 FD 36 HADE 4,187 Non-FD - 56,085 FD -2,168 FD -0,111 FD 0,126 FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 939 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 37 TOPS 0,415 FD 0,105 FD 0,063 FD 0,396 FD 0,150 FD 38 TRIO -1,126 FD -7,166 FD - 20,633 FD - 20,669 FD -4,592 FD 39 VIVA -0,622 FD -0,654 FD -0,998 FD -1,141 FD -1,738 FD 40 SMRU -0,169 FD -0,809 FD -1,657 FD -1,487 FD -0,647 FD 41 SONA 1,794 Non-FD 1,886 Non-FD -0,890 FD -0,758 FD 0,174 FD 42 TARA -0,025 FD -0,015 FD -0,254 FD 0,732 FD -0,100 FD 43 TAXI -3,661 FD -2,529 FD -2,847 FD 17,387 Non-FD -0,915 FD 44 DIGI 0,712 FD 0,726 FD -2,217 FD -2,660 FD -3,573 FD 45 TAMA 0,060 FD -0,323 FD -0,562 FD -0,418 FD -0,450 FD 46 TIRA 0,476 FD 0,483 FD 0,376 FD 0,223 FD 0,398 FD 47 WOWS 0,012 FD 0,511 FD 0,351 FD -0,116 FD 0,043 FD 48 NASA 0,048 FD 0,021 FD -0,089 FD -0,020 FD 0,049 FD 49 REAL 1,206 Non-FD 0,792 FD 0,829 FD 0,949 Grey Area 0,722 FD 50 PPRO 0,497 FD 0,319 FD 0,196 FD 0,286 FD 0,302 FD 51 KBAG -0,162 FD 0,103 FD 0,609 FD 0,559 FD 0,896 Grey Area 52 KREN 2,701 Non-FD 2,117 Non-FD 1,335 Non-FD 1,336 Non-FD 1,932 Non-FD 53 KOTA 0,288 FD 0,026 FD -0,182 FD -0,100 FD -0,174 FD 54 AGAR 1,419 Non-FD 1,042 Grey Area 1,093 Non-FD 1,261 Non-FD 1,306 Non-FD 55 SBAT 0,142 FD -0,334 FD -0,268 FD -0,471 FD -0,986 FD 56 SCPI 1,629 Non-FD 2,170 Non-FD 1,733 Non-FD 2,132 Non-FD 2,177 Non-FD Source : Data processed by the author (2023) Table 6. above is the result of calculations using the Springate method in 2018 there were 45 companies experiencing financial distress, and 2 companies were in gray areas. While 9 other companies are included in the category of Non-Financial Distress companies. In 2019, there were 49 companies experiencing financial difficulties, with 1 company being in the grey area. While 6 other companies are included in the category of Non-Financial Distress companies. In 2020, there were 51 companies experiencing financial difficulties. While the other 5 companies are included in the category of Non-Financial Distress companies. In 2021, there were 47 companies experiencing financial difficulties, and 1 company was in the grey area stage. While 8 other companies are included in the category of Non-Financial Distress companies. Furthermore, in 2022, there are 48 companies experiencing financial difficulties, and 4 companies are in a gray area. While 4 other companies are included in the category of healthy companies (Non Financial Distress). These results show that the Springate model is able to predict financial distress in companies with a special notation code of the Indonesia Stock Exchange (H2: Accepted). Comparison of Company Health Status Based on Prediction Results The two models were then compared to the company's actual health status. The company's financial condition is actually seen from the main factors that can cause financial distress, namely liquidity crisis, negative retained earnings, negative equity and ongoing losses experienced by the company. The following is the result of a comparison of financial distress prediction models with the actual state of the company: https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 940 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc Table 7. Company Status Analysis Results No Comp any Code Ye ar Altm an Z- Scor e Spring ate S- Score Conclus ion N o Comp any Code Year Altm an Z- Scor e Spring ate S- Score Conclus ion 1 KIAS 201 8 Non- FD FD FD 2 RMBA 2018 Grey Area FD FD 201 9 FD FD FD 2019 Grey Area FD FD 202 0 Grey Area FD FD 2020 Non- FD FD FD 202 1 Non- FD FD FD 2021 FD FD FD 202 2 Grey Area FD FD 2022 Non- FD Grey Area FD 3 AKKU 201 8 Non- FD FD FD 4 HDTX 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 Non- FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 5 WSBP 201 8 Non- FD FD Non-FD 6 BIMA 2018 FD FD FD 201 9 Non- FD FD Non-FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 7 TALF 201 8 Non- FD Grey Area Non-FD 8 LMSH 2018 Non- FD Non- FD Non-FD 201 9 Non- FD FD Non-FD 2019 Non- FD FD FD 202 0 Non- FD FD Non-FD 2020 Non- FD FD FD 202 1 Non- FD FD Non-FD 2021 Non- FD Non- FD Non-FD 202 2 Non- FD FD Non-FD 2022 Non- FD FD FD 9 ARKA 201 8 FD FD FD 10 IKAI 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 941 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 11 INTA 201 8 FD FD FD 12 TIRT 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 13 JGLE 201 8 Non- FD FD FD 14 JSPT 2018 Non- FD Grey Area Non-FD 201 9 Non- FD FD FD 2019 Non- FD FD Non-FD 202 0 Non- FD FD FD 2020 Grey Area FD FD 202 1 Non- FD FD FD 2021 Grey Area FD FD 202 2 FD FD FD 2022 Grey Area FD FD 15 MIRA 201 8 FD FD FD 16 MKNT 2018 Grey Area Non- FD Non-FD 201 9 FD FD FD 2019 Non- FD Non- FD FD 202 0 FD FD FD 2020 Grey Area Non- FD FD 202 1 FD FD FD 2021 Grey Area Non- FD FD 202 2 FD FD FD 2022 FD Non- FD FD 17 MDIA 201 8 Non- FD FD FD 18 MDRN 2018 FD FD FD 201 9 Non- FD FD Non-FD 2019 FD FD FD 202 0 Non- FD FD Non-FD 2020 FD FD FD 202 1 Non- FD FD Non-FD 2021 FD Non- FD FD 202 2 Grey Area FD Non-FD 2022 FD Grey Area FD 19 LCKM 201 8 Non- FD Non- FD Non-FD 20 SAFE 2018 FD FD FD 201 9 Non- FD Non- FD Non-FD 2019 FD FD FD 202 0 Non- FD Non- FD Non-FD 2020 FD FD FD 202 1 Non- FD Non- FD Non-FD 2021 FD FD FD 202 2 Non- FD Grey Area Non-FD 2022 FD FD FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 942 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 21 POSA 201 8 FD FD FD 22 PNSE 2018 Grey Area FD FD 201 9 FD FD FD 2019 Grey Area FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 23 ANDI 201 8 FD FD FD 24 BIKA 2018 Non- FD FD FD 201 9 Grey Area FD Non-FD 2019 Non- FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 Grey Area FD FD 2021 FD FD FD 202 2 Grey Area FD FD 2022 FD FD FD 25 CMPP 201 8 FD FD FD 26 CNKO 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 27 CTTH 201 8 FD FD FD 28 DADA 2018 Non- FD FD Non-FD 201 9 FD FD FD 2019 Grey Area FD Non-FD 202 0 FD FD FD 2020 Non- FD FD Non-FD 202 1 FD FD FD 2021 Non- FD FD Non-FD 202 2 FD FD FD 2022 Non- FD FD Non-FD 29 DPUM 201 8 Non- FD FD Non-FD 30 DEAL 2018 Grey Area FD Non-FD 201 9 Grey Area FD FD 2019 FD FD Non-FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 31 BTEK 201 8 Grey Area FD Non-FD 32 BUVA 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 943 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 33 GMTD 201 8 Non- FD FD Non-FD 34 IIKP 2018 Non- FD FD FD 201 9 Non- FD FD FD 2019 Non- FD Non- FD FD 202 0 Non- FD FD FD 2020 Non- FD FD FD 202 1 Non- FD FD FD 2021 Non- FD FD FD 202 2 Non- FD FD Non-FD 2022 Non- FD FD FD 35 GLOB 201 8 FD FD FD 36 HADE 2018 Non- FD Non- FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 37 TOPS 201 8 Grey Area FD Non-FD 38 TRIO 2018 FD FD FD 201 9 Non- FD FD FD 2019 FD FD FD 202 0 Grey Area FD FD 2020 FD FD FD 202 1 Grey Area FD FD 2021 FD FD FD 202 2 Grey Area FD FD 2022 FD FD FD 39 VIVA 201 8 FD FD FD 40 SMRU 2018 FD FD FD 201 9 FD FD FD 2019 FD FD FD 202 0 FD FD FD 2020 FD FD FD 202 1 FD FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 41 SONA 201 8 Non- FD Non- FD Non-FD 42 TARA 2018 Non- FD FD FD 201 9 Non- FD Non- FD Non-FD 2019 Non- FD FD FD 202 0 Non- FD FD FD 2020 Non- FD FD FD 202 1 Non- FD FD FD 2021 Non- FD FD Non-FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 944 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 202 2 Non- FD FD FD 2022 Non- FD FD FD 43 TAXI 201 8 FD FD FD 44 DIGI 2018 Non- FD FD FD 201 9 FD FD FD 2019 Non- FD FD FD 202 0 FD FD FD 2020 Grey Area FD FD 202 1 FD Non- FD FD 2021 FD FD FD 202 2 FD FD FD 2022 FD FD FD 45 TAMA 201 8 FD FD FD 46 TIRA 2018 Grey Area FD Non-FD 201 9 FD FD FD 2019 Grey Area FD Non-FD 202 0 FD FD FD 2020 Grey Area FD Non-FD 202 1 FD FD FD 2021 Grey Area FD FD 202 2 FD FD FD 2022 Grey Area FD Non-FD 47 WOWS 201 8 FD FD FD 48 NASA 2018 Non- FD FD FD 201 9 Non- FD FD Non-FD 2019 Non- FD FD FD 202 0 Non- FD FD Non-FD 2020 Non- FD FD FD 202 1 Non- FD FD FD 2021 Non- FD FD FD 202 2 Non- FD FD FD 2022 Non- FD FD FD 49 REAL 201 8 Non- FD Non- FD FD 50 PPRO 2018 Non- FD FD Non-FD 201 9 Non- FD FD Non-FD 2019 Grey Area FD Non-FD 202 0 Non- FD FD Non-FD 2020 Grey Area FD Non-FD 202 1 Non- FD Grey Area Non-FD 2021 Grey Area FD Non-FD 202 2 Non- FD FD Non-FD 2022 Grey Area FD Non-FD 51 KBAG 201 8 FD FD FD 52 KREN 2018 Non- FD Non- FD Non-FD 201 9 Non- FD FD Non-FD 2019 Non- FD Non- FD Non-FD 202 0 Non- FD FD Non-FD 2020 Non- FD Non- FD FD 202 1 Non- FD FD Non-FD 2021 Non- FD Non- FD FD 202 2 Non- FD Grey Area Non-FD 2022 Non- FD Non- FD FD 53 KOTA 201 8 Non- FD FD Non-FD 54 AGAR 2018 Grey Area Non- FD Non-FD https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 945 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc 201 9 Non- FD FD FD 2019 Non- FD Grey Area Non-FD 202 0 Non- FD FD FD 2020 Non- FD Non- FD FD 202 1 Non- FD FD FD 2021 Non- FD Non- FD Non-FD 202 2 Non- FD FD FD 2022 Non- FD Non- FD FD 55 SBAT 201 8 FD FD FD 56 SCPI 2018 Non- FD Non- FD Non-FD 201 9 FD FD FD 2019 Non- FD Non- FD Non-FD 202 0 FD FD FD 2020 Non- FD Non- FD Non-FD 202 1 FD FD FD 2021 Non- FD Non- FD Non-FD 202 2 FD FD FD 2022 Non- FD Non- FD Non-FD Source : Data processed by the author (2023) Table 7. above presents information on the financial health of companies included in the special notation of the Indonesia Stock Exchange, the results of analysis using Altman approaches modified by Z-Score and Springate. The table also shows the real state of the company from 2018- 2022. Table 8. Analysis Results No Methode Year Prediction Result Total Financial Distress Non Financial Distress Financial Distress Non Financial Distress 1 Altman Modified Z- Score 2018 32 24 37 19 56 2019 33 23 39 17 56 2020 36 20 46 10 56 2021 37 19 45 11 56 2022 39 17 46 10 56 2 Sprinagte 2018 47 9 37 19 56 2019 50 6 39 17 56 2020 51 5 46 10 56 2021 48 8 45 11 56 2022 52 4 46 10 56 Source : Data processed by the author (2023) Table 8. shows the results of comparative analysis between two methods namely Altman Modification Z-Score and Springate. The comparison results show that there are several differences between the results of the analysis using the two methods and the actual state of the company. Accuracy of Prediction Models Here's a comparison of the accuracy of the prediction model and the error rate: https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 946 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc Table 9. Comparison of the Accuracy of Financial Distress Prediction Models Analysis Method Accuracy Calculation Method Sample (total company x 5 years) Total Observation Year (2018-2022) Altman Modified Z-Score Accuracy Level 280 213 70% Eror I Type 280 50 18% Eror II Type 280 16 6% Springate Accuracy Level 280 213 60% Eror I Type 280 14 5% Eror II Type 280 45 16% Source : Data processed by the author (2023) Based on the table. 9 it is seen that there is a difference in the accuracy of financial distress prediction models between the Altman and Springate models. The accuracy rate of both models is obtained from the number of samples that are predicted to be true to experience financial distress divided by the total sample and multiplied by one hundred percent, then reduced by the type two error rate. The results of the calculation of the accuracy level obtained the results of the accuracy rate of the Altman model of 70%, with error type 1 of 18% and error type 2 of 6%. While the Springate model has an accuracy rate of 60%, with error type 1 of 5% and error type 2 of 16%. Based on these calculations, it can be concluded that the model that has the highest level of accuracy in predicting financial distress in companies with a special notation of the Indonesia Stock Exchange (IDX), is Altman modified Z-Score with an accuracy rate of 70%. These results show that there is a difference in the level of accuracy between the modified Altman z-score model and the Springate model, so that the third hypothesis in this study (H3) is accepted. Type 1 errors occur when the prediction model states the company does not experience financial dsitress but actually the company experiences financial distress and type 2 errors occur when the prediction model states the company experiences financial dsitress but it turns out that the company does not experience financial. Knowing the difference between Error type 1 and type 2 helps investors understand how well the model is at signaling a company's financial condition. The theoretical foundation of this research includes the principles of signal theory, where investors look for indicators or signals to make better investment decisions. By understanding the difference between Error type 1 and type 2, investors can optimize their investment decisions by considering the risk of errors that may occur in the interpretation of statistical models such as Altman Modified Z-Score and Springate. Based on these calculations, it can be concluded that the model that has the highest level of accuracy in predicting financial distress in companies with a special notation of the Indonesia Stock Exchange (IDX), is Altman modified Z-Score with an accuracy rate of 70%. These results show that there is a difference in the level of accuracy between the modified Altman z-score model and the Springate model, so that the third hypothesis in this study (H3) is accepted. The statistical analysis with signal theory indicates that companies with specific notation codes can convey negative signals to management, investors, and other stakeholders through financial https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 947 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc statement analysis using the Altman Modified Z-Score and Springate models. From the analysis results, it is found that the majority of companies in the sample are experiencing financial distress. The analysis was conducted over the past five years, providing a current overview of the financial condition of these companies. Positive signals, as detected by the statistical models, can serve as a serious warning for the company's management to promptly address the financial condition. These findings also provide a robust foundation for strategic decision-making that can help ensure the company's sustainability in the future CONCLUSION The results of this study indicate that companies with special notation codes on the Indonesia Stock Exchange may potentially experience financial distress based on the analysis methods of the modified Altman Z-Score and Springate S-Score models. Both models have proven to be capable of predicting the health condition of the company using established financial ratios. The results of these two models show significant differences in analyzing the potential financial distress of companies with special notation. There are several predictions that are in accordance with the actual financial situation. But there are also those whose prediction results are not in accordance with the actual financial condition of a company. Although there are some prediction errors, the prediction results can still be used as indicators to see the sustainability of the company in the future and can be used as an indicator A signal for company management to immediately improve the company's financial condition, and can be used as a signal for investors and other interested parties in making decisions. The results of the analysis predicting the potential financial distress using Altman Modified Z- Score and Springate indicate that both methods have different criteria and limitations in determining the financial condition of a company. Indicated by the results of different levels of accuracy of the two models. The Altman model has an accuracy rate of 70%, with error type 1 at 18% and error type 2 at 6%. While the Springate model has an accuracy rate of 60%, with error type 1 of 5% and error type 2 of 16%. From these results, it can be concluded that the model with a higher level of accuracy is the Altman model modified z-score with an accuracy rate of 70%, which means the Altman model has better performance in predicting financial distress of a company. This result is in line with research (Munira et al., 2021), (Ridhawati & Suryantara, 2023) which found that the accuracy of the Altman Z-Score model is higher than the Springate method. This research provides important information for companies with special notation codes that experience financial distress, to immediately improve financial conditions, and provides a basis for strategic decision making to ensure the sustainability of the company and for investors and other interested parties can be used as a basis for investment decision making. This study has several limitations such as the analysis in this study considers more internal company factors. The analysis method of this research still uses two commonly used methods, namely the Altman Z-score model and Springte. Future research is expected to be able to make a wider scope such as adding external factors of the company in predicting financial distress and using other precondition models such as Deep Learning Models or Deep Neural Networks (DNN). https://www.ilomata.org/index.php/ijtc The Potential Financial Distress in Special Notation Companies on the Indonesia Stock Exchange: Prediction Model Approach Sugiarti and Nikmah 948 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijtc REFERENCE Annafi, G. D., & Yudowati, S. P. (2021). Analisis Financial Distress, Profitabilitas, dan Materialitas Terhadap Kecurangan Laporan Keuangan. Jurnal Akuntansi Kompetif, 4(3), 255–262. Cındık, Z., & Armutlulu, I. H. (2021). A revision of Altman Z-Score model and a comparative analysis of Turkish companies’ financial distress prediction. 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