Analysis of Methods Used to Predict Financial Distress Potential

ANALYSIS OF METHODS USED TO PREDICT FINANCIAL DISTRESS POTENTIAL Lorina Siregar Sudjiman Paul Eduard Sudjiman ABSTRAK. Penelitian ini bertujuan untuk menganalis kondisi perusahaan menggunakan metode Altman Z-score dan Zwijewski dalam memprediksi potensi kebangkrutan pada 10 perusahaan subsektor Hotel, Restoran dan Pariwisata yang terdaftar di Bursa Efek Indonesia. Pengambilan sampel menggunakan Teknik purposive sampling dengan 10 sampel perusahaan subsektor Hotel, Restoran dan Pariwisata. Jenis penelitian deskriptif dengan pendekatan kuantitatif. Hasil penelitian menunjukkan bahwa: (1) Hasil penggunaan metode Altman, pada tahun 20114– 2018 pada kesepuluh perusahaan subsektor Hotel, Restoran dan Pariwisata berada dalam kategori sehat, dan tidak sehat, nilai Z-Score berfluktuasi ≥ 2,99 dan Z-score ≤ 1,81 dimana Perusahaan akan bangkrut, seperti yang terjadi pada perusahan PANR dan PGLI, namun pada akhir penjumlahan rata-rata tahun, perusahaan dinyatakan sehat. (2) Hasil penelitian menggunakan metode X-score Zmijewski bahwa seluruh perusahaan Subsektor Hotel, Restoran dan Pariwisata ini sangat memungkinkan ketidak sehat perusahaan terlihat dari dalam kinerja keuangannya. Hasil dari penelitian ini diharapkan perusahaan subsektor Hotel, Restoran dan Pariwisata dapat menjaga likuiditasnya dalam memenuhi semua kewajibannya sehingga menarik minat para investor dan kreditor. Perusahaan diharapkan dapat mengelola aktiva untuk meningkatkan penjualan dan menghasilkan laba. Kata kunci: Laporan Keuangan, Kebangkrutan, X-score Zmijewski, S-score Altman. INTRODUCTION On the Indonesia Stock Exchange (BEI), shares of Tourism issuers are incorporated into the Restaurant, Hotel and Tourism sub-sector which is part of the Trade, Services and Investment sectors. According to the Decree of the Minister of Parpostel no KM 94 / HK103 / MPPT 1987, the understanding of hotels is one type of accommodation for accommodation services, food and beverage providers and other services for the general public which are managed commercially. According to RI Law No. 34 of 21 2000, a restaurant is a place to eat food and drinks provided with a fee, not including catering or catering. Whereas tourism according to RI Law No. 10 of 2009 is a variety of tourism activities and is supported by various facilities and services provided by the community, entrepreneurs and government. And according to Law No. 37 of 2004, bankruptcy is a condition where an institution is declared by a court decision if the debtor has two or more creditors and does not pay at least one debt that is due and collectible. In this study the object to be examined is the hotel, restaurant and tourism subsector companies listed on the IDX, because according to the Secretariat of the Republic of Indonesia Cabinet Secretariat (2017) tourism is a national priority in the 2015-2019 RP JM. With the tourism sector becoming a priority, surely other sectors such as the hotel and restaurant sectors which are directly related to tourism will also experience a significant impact But in recent years, the development of the performance of the shares of the Tourism group tends not to be too good. BPS head Suhariyanto in Suara.com said that the number of tourist arrivals to Indonesia coming through the air entrances in May 2019 experienced a decrease of 11.37 percent compared to the number of visits in the same period last year. The ups and downs of the company are common. Conditions that make investors and creditors feel worried if the company experiences financial distress that can lead to bankruptcy. Signs of bankruptcy in this case can be seen by using accounting data. One of the main indicators used as the basis for valuation is the company's financial statements. A company can be categorized as experiencing financial distress or financial difficulties if the company shows a negative number on operating income, net income and book value of equity and the company is merging (Brahmin, 2007), the company tends to experience liquidity problems (Hanifah, 2013). This has happened to PT. Distribution Indonesia Jaya, reported by Deliana (2018), said that PT Distribution Indonesia Jaya was in debt of 261.29 billion to its creditors. So the company stopped operating November, 2017. Before the company goes bankrupt, it will start with financial distress (Budhijama and Nelmida, 2018). Bankruptcy prediction analysis results can be used to minimize the occurrence of losses for internal parties or external parties due to bankruptcy experienced by the company, as well as predicting the continuation of the life of the company concerned (Anita, 2017). Various analytical methods were developed to predict the beginning of a company's bankruptcy. The author analyzes with two methods in financial distress 22 namely the Altman Z-score method and the Zmijewski method. One mathematical formula for predicting bankruptcy with a fairly accurate certainty of 95% and the most popular and often used by many researchers in conducting research, namely the Altman Z-score that has been developed by a business professor from New York University US Edward I. Altman, in 1968. From the calculation results will be obtained the value of Z (Z-Score) which can describe the company's financial position is in a healthy condition, vulnerable or in a bankrupt condition. Husein & Galuh (2014) in their research said that the Altman and Zmijwski model more precisely shows bankruptcy than Springate and Grover. Edi & May, T., (2018) emphasized in his research that each model in this study had a significant effect, which means that the Altman, Springate, and Grover models could be used in predicting financial distress. Based on the background description and research gap above, the authors are interested in conducting research on the analysis of the methods used to predict potential financial difficulties in the Hotel, Restaurant and Tourism sub-sectors, which are listed on the IDX. Statement of the Problem Based on the background of the problem outlined above, the authors identify the problem as follows: a. What is the condition of the company in financial distress in the Hotel, Restaurant and Tourism sub-sector? b. How is the potential for Financial Distress to occur in the Hotel, Restaurant and Tourism sub-sector companies? THEORETICAL BASIS’ 1. Financial Report Kasmir (2014: 07) states financial statements are reports that show the company's financial condition at this time or in a certain period. And the usefulness of financial statements is to see the condition of a company, both current conditions and used as a predictor for future conditions (Fahmi, 2011: 5). Financial analysis has 2 main tools that can be used, namely: ratio analysis (ratio analysis) and cash flow analysis (cash flow analysis). (Palepu and Healy, 2008: 51). 2. Financial Distress 23 Financial distress or often referred to as financial difficulties, occurs before a company actually goes bankrupt (Ramadhani and Lukviarman, 2009). Financial distress can occur in various companies and can be a signal of bankruptcy or liquidation that the company may experience. Companies that experience financial distress according to (Plat and Platt, 2006) can be seen or determined by various factors a. The existence of dismissal of workers or not making dividend payments. b. Cash flow that is smaller than current long-term debt. c. Net operating income is negative. d. Changes in equity prices. e. Company has ceased operations on the authority of the government and the company is required to carry out restructuring planning. f. The company experienced a technical violation in debt and it is predicted that the company will go bankrupt in the coming period. g. Having negative Earnings per Share (EPS). h. Use interest coverage ratio that experience financial distress, solutions that companies can do, namely Companies that experience financial distress, solutions that companies can do, namely: a) Debt restructuring that is trying to ask for an extension of time from creditors to pay off debt until the company has enough cash to pay off debt. b) Changes in management. This is to avoid the run of potential investors of the company. (Pustylnick, 2012), 3. Bankruptcy Bankruptcy is usually interpreted as a failure of a company in carrying out company operations to generate profits (Supardi and Mastuti in Dwi, Martini, et al., 2012: 505). Toto (2011: 332) also asserted that bankruptcy is a condition in which a company is no longer able to pay off its obligations. And in broad outline the causes of bankruptcy can be divided into two namely internal factors and external factors. Internal factors are factors that originate from the internal management of the company. While external factors can come from external factors directly related to company operations or macro-economic factors (Darsono and Ashari, 2005: 104). 24 4. Financial Distress Prediction Model Until now, research on financial distress prediction has been widely developed both internationally and in Indonesia. Of the many existing models, researchers will describe some of the most popular models used as predictive analysis tools, namely the Altman prediction model (Zscore), and the Zmijewski prediction model (X-Score). a. Altman prediction model (Z-Score). Altman’s z-score or Altman bankruptcy prediction z-score model is a model that provides a formula for assessing when a company will go bankrupt, and is used to predict the possibility of a company that will go bankrupt in the next 2 years. By using the formula that is filled (interpretation) with the financial ratios it will be known certain numbers that exist become material to predict when the possibility of a company going bankrupt b. Zmijewski prediction model (X-Score). Other management tools such as creditors or investors to see the company's financial difficulties can use the Zmijewski method. According to Anandarajan (2004) explains that, "Zmijewski (1984) used financial ratios that measured firm performance, leverage, and liquidity to develop his model. Where X-score is more than 0. The firm is classified as bankrupt. " The Zmijewski model takes the following form (pg 79). RESEARCH METHODS The population in this study were all of the Hotel, Restaurant and Tourism Subsector companies listed on the Indonesia Stock Exchange from 2014-2018, amounting to 25. And the sample was 10 companies, because it did not include 10 companies that had incomplete financial statements with 5 companies not audited during the 2014-2018 period. The following is a list of companies that are used as sample companies. Table 1 Company Sample Data Company name Year Bayu Buana Tbk 2014-2018 Buva Bukit Uluwatu Villa Tbk 2014-2018 25 Fast Food Indonesia Tbk 2014-2018 Island Concepts Indonesia Tbk 2014-2018 Jakarta International Hotel & Development Tbk 2014-2018 Panorama Sentrawisata Tbk 2014-2018 Pembangunan Graha Lestari Indah Tbk 2014-2018 Pembangunan Jaya Ancol Tbk 2014-2018 Pudjiadi And Sons Tbk 2014-2018 Pusako Tarinka Tbk 2014-2018 Source: Researcher The data source in this study is secondary data taken from the annual financial statements in the Hotel, Restaurant and Tourism Subsector in the Indonesia Stock Exchange in the 2014-2018 period. Analysis of the data in this study uses the Altman Z-score method and the Zmijewski method to see the health status of the company, listed on the Indonesia Stock Exchange for the period 2014-2018, which is published on the IDX's official website, www.idx.co.id. 1. Model Z-score Altman This model was first created by Altman in 1968 with the MDA (Multi Discriminant Analysis) method to determine the coefficient of each variable in the ZScore model. The Z-Score formula obtained is Z = 1,2 (X1) + 1,4 (X2) +3,3 (X3) + 0,6 (X4) Information: X1 = working capital / total assets. It is a ratio for liquid prediction using all assets (total assets) of the company. X2 = retained earnings / total assets. This measurement is to see all owned capital (retained earnings) able to compete in looking at assets. X3 = EBIT / total assets. This measurement aims at how much the company generates profitability with the overall assets regardless of the debt used. X4 = stock market value / total debt. This measurement aims to measure the level of debt (leverage) by looking at that large debt threatens the sustainability of the company. 26 2. Model Zmijewski X = -4,3 - 4,5X1 + 5,7X2 – 0,004X3 X1= ROA (Return on Asset) Profit After Tax of Total Assets (Return on Assets). It is a ratio that shows a company's ability to generate net income derived from the total assets used. 𝑋1= Profit after tax/Total Asset X2 = Leverage (Debt Ratio) Total Liabilities to Total Assets. It is a ratio that measures how much the total assets of the company funded by the company's creditors. The higher the ratio shows the higher the risk faced by the company. 𝑋2= Total Liability/Total Asset X3 = Likuiditas (Current Ratio) Current Assets to Current Liabilities. Is a measurement of liquidity by comparing short-term assets with short-term liabilities? X3:Current Asset/Current Liability With a cut off if the score obtained exceeds 0, the company is predicted to potentially experience bankruptcy. Conversely, if a company has a score of less than 0, the company is predicted to have no potential to go bankrupt (Wulandary and Nur, 2014). Table 2 Cut Off Points in the Altman Z-score and Zmijewski Method. Altman Z> 2.99 Companies are free from the risk of bankruptcy.’  Z <1.81 Companies will go bankrupt  1.81 <Z <2.99 Companies in the gray position (doubtful about going bankrupt or not bankrupt). Zmijewski X <0 Companies that have no potential to go bankrupt.  X> 0 is classified as an unhealthy company and has the potential to go bankrupt. 27 RESULT AND DISCUSSION 1. Bankruptcy Risk Conditions Analysis in the Hotel, Restaurant and Tourism Sub Sectors with the Altman Z-Score Method a. Altman Method of Financial Distress Analysis The table below shows the Altman method of financial distress. Table 3: Altman Financial Distress Analysis X1 BAYU X2 X3 X4 ZASCORE SCORE ANALYZE 2014 0,2829256 0,0002449 0,27494 1,149223 1,2 1,70733 bankrupt 2015 0,2839599 0,0001120 0,16714 0,838799 1,4 1,29001 bankrupt 2016 0,3382394 0,0001104 0,17128 0,797384 3,3 1,30701 bankrupt 2017 0,3625827 0,0000950 0,18464 0,687165 0,6 1,23448 bankrupt 2018 0,4008772 0,0001612 0,21012 0,761098 1,37226 bankrupt 6,91111 992 Non distress condition ZASCORE SCORE ANALYZE BUVA X1 X2 X X3 3 X4 2014 bankrupt 0,0942419 0,0115758 0,134394 0,672651 1,4 0,71895 1,2 0,9128 2015 bankrupt (0,06247862 5) 0,0040962 0,050722 0,726610 94 0,0290692 0,0035319 0,007167 0,812874 3,3 0,85264 (0,2053349) 0,0036232 0,038852 0,651239 0,6 0,48838 0,2062424 0,0028976 0,017691 0,778593 2016 bankrupt 2017 bankrupt 2018 bankrupt FAST X1 X2 X X3 3 X4 1,00542 3,97826 ZASCORE SCORE Non distress condition ANALYZE 28 bankrupt 2014 0,2444497 0,0076918 0,322593 0,738442 1,2 1,31317 2015 0,1074465 0,0076601 0,190605 0,559501 1,4 0,86521 2016 0,2493292 0,0071510 0,289782 0,541799 3,3 1,08806 2017 0,5318449 0,0071442 0,197719 0,533119 0,6 1,26982 2018 0,5311790 0,006961 0,3080 0,63779 X1 X2 X X3 3 X4 2014 0,4018693 0,00365683 0,082019 0,570230 2015 0,4771899 0,01008665 0,116257 0,350512 2016 0,3861969 0,00671712 0,048592 0,307584 2017 1,0642755 0,02397931 0,140781 0,457568 2018 1,0469169 0,09751306 0,295240 0,643021 bankrupt bankrupt bankrupt ICON X1 X2 JSPT X X3 3 X4 bankrupt 1,4839 6,02027 Non distress condition ZAANALYZE SCORE SCORE bankrupt 1,05777 1,2 bankrupt 0,95404 1,4 bankrupt 0,74909 3,3 bankrupt 1,68660 0,6 bankrupt 2,08269 6,53021 ZASCORE SCORE 2014 0,2870361 0,0000022 0,361776 1,091962 1,2 1,74077 2015 0,3197021 0,0025167 0,223616 1,234047 1,4 1,77988 2016 0,2207941 0,0027351 0,173220 1,28365 3,3 1,68040 2017 0,2954346 0,0029223 0,006548 1,251713 0,6 1,55661 2018 0,3113462 0,0026621 0,007390 1,071920 Non distress condition ANALYZE bankrupt bankrupt bankrupt bankrupt bankrupt 1,39331 X1 X2 X3 X3 8,15100 ZASCORE SCORE 2014 0,0885696 1,2018485 1,598119 0,212688 1,2 3,10122 2015 (0,013663) 0,1430317 0,127023 0,186045 1,4 0,44243 PANR Non distress condition ANALYZE Non distress condition bankrupt 29 bankrupt 2016 0,0825942 0,0972322 0,028305 0,296781 3,3 0,50491 2017 0,1988634 0,0849294 0,075865 0,502695 0,6 0,86235 bankrupt bankrupt 2018 0,0690654 0,1907413 0,034237 0,508736 0,80278 5,71370 Non distress condition ANALYZE X1 X2 X3 X4 ZASCORE SCORE 2014 0,1244641 0,0267224 0,060290 22,76773 1,2 2015 0,1651149 0,0286826 0,024422 12,14236 1,4 2016 0,1436325 0,1472119 0,041369 3,324543 3,3 Non distress condition Non distress 12,5851 condition 3 3,65675 Non distress condition 2017 (0,028947) 0,1350271 0,1024267 0,600000 0,6 0,80850 2018 0,1015334 0,2025937 0,3021120 1,744519 X1 X2 X3 X4 22,1558 ZASCORE SCORE 2014 0,05546725 0,01327758 0,3220801 0,727025 1,2 1,11785 2015 0,03580545 0,01339925 0,3991401 0,799859 1,4 1,24820 2016 (0,0359988) 0,01221001 0,2159957 0,565267 3,3 0,75747 2017 0,00913069 0,01276472 0,2966520 0,679395 0,6 0,99794 2018 (0,0664820) 0,01167715 0,2608301 0,570444 PGLI PJAA 2,9792 2,35075 bankrupt Non distress condition Non distress condition ANALYZE bankrupt bankrupt bankrupt bankrupt bankrupt X1 0,77646 4,89794 X2 X3 X4 ZASCORE SCORE 2014 0,2771630 0,0051196 0,299313 1,003616 1,2 1,58521 2015 0,1055597 0,0055078 0,180013 1,132908 1,4 1,42398 2016 0,1076412 0,0050275 0,0180363 0,725789 3,3 0,85649 PNSE Non distress condition ANALYZE bankrupt bankrupt bankrupt bankrupt 2017 0,1098046 0,0049619 0,1891912 0,795665 2018 0,0027678 0,0058144 0,0531025 0,968228 0,6 1,09962 bankrupt 1,02991 30 X1 X2 X3 X4 ZASCORE SCORE PTSP ANALYZE bankrupt 2014 0,13606286 0,00036124 0,97862023 0,6347229 1,2 1,74976 bankrupt 2015 0,00016633 0,00036914 0,01659017 0,5239859 1,4 0,54111 bankrupt 2016 (0,02960997) 0,00036676 0,10170126 0,526773 3,3 0,59923 2017 (0,04809481) 0,00034747 0,15804999 0,5576268 0,6 0,66792 2018 0,02562314 1,01811 bankrupt bankrupt 0,00034522 0,30426580 0,6878821 4,57615 Non distress condition Table 4 Cut Off Points in the Altman Z-score Altman Z > 2,99 Companies are free from the risk of bankruptcy. Z< 1,81 Companies will go bankrupt 1,81 < Z < 2,99 Company in the grey position (doubtful about going bankrupt or not bankrupt). It can be seen in the corporate restaurant and tourism sub-sector looks healthy in the company's position, with the Altman method in a cut-off above 2.99. There are fluctuations in each year such as the PANR and PGLI companies, but although there are differences in the conditions of each company, each period does not cover the company's healthy condition. 2. Zmijewski Financial Distress Analysis This Financial Distress Analysis aims to determine the condition of the company's position on financial performance whether the potential is unhealthy, unhealthy or grey Zmijewski Financial Distress Analysis Table 5 Zmijewski Financial Distress Analysis BAYU X1 X2 X3 X-SCORE Z-SCORE 4,3 12,26006018 ANALYZ E bankrupt 2014 4,56998159 6,1652844 1,5247941 31 2015 4,50405533 6,1170143 1,5972681 -4,5 12,2183378 4,54159968 6,1293737 1,6925947 -5,7 12,3635681 2016 bankrupt bankrupt bankrupt 2017 2018 BUVA 4,54337744 6,1661407 1,7091483 4,54950422 6,1408204 1,8281879 0,004 12,4186664 bankrupt X1 X2 X3 X-SCORE 12,5185125 61,7791452 bankrupt A-SCORE ANALISA bankrupt 2014 4,535374 6,7000000 1,2514112 4,3 12,4867860 bankrupt 2015 4,5154676 6,1522803 0,6438098 4,5 11,3115577 bankrupt 2016 4,5042662 6,1246663 1,1439128 11,7728454 5,7 bankrupt 2017 4,5049409 6,1795245 0,4783740 0,004 11,1628395 bankrupt 2018 FAST 4,5580197 X1 6,1352261 X2 0,3508500 X3 X-SCORE 11,0440958 57,7781246 bankrupt A-SCORE ANALYZE bankrupt 4,5703059 6,1482821 1,8785508 4,3 12,5971389 4,5080855 6,2174638 1,2579224 4,5 11,983471 2014 bankrupt 2015 bankrupt 4,5094689 6,2254861 1,7891981 5,7 12,5241532 4,5170073 6,2295117 33,324638 0,004 44,0711571 4,5283608 6,1847318 36,105808 2016 bankrupt 2017 46,8189015 bankrupt 2018 32 ICON X1 X2 X3 X-SCORE 127,994822 bankrupt A-SCORE ANALYZE bankrupt 4,3175966 6,2127195 1,7189961 4,3 12,2493123 4,3127928 6,5766187 1,4653172 4,5 12,3547289 4,3002268 6,3610952 1,5525744 5,7 12,2138964 4,3011057 6,2673388 0,9960000 0,004 11,5644446 4,3012582 6,1826946 49,752701 2014 bankrupt 2015 bankrupt 2016 bankrupt 2017 60,2366546 bankrupt 108,619037 bankrupt A-SCORE ANALYZE 2018 X1 X2 X3 X-SCORE JSPT bankrupt 4,5886336 6,0546177 2,3765070 4,3 13,0197584 4,5574706 6,0271452 2,8114036 4,5 13,3960195 4,5435527 6,0185300 2,1691207 5,7 12,7312034 4,5016101 6,0240241 16,27572 0,004 26,8013585 4,5018486 6,0588687 17,178610 2014 bankrupt 2015 bankrupt 2016 bankrupt 2017 27,7393282 bankrupt 93,6876682 bankrupt A-SCORE ANALYZE 2018 X1 X2 X3 X-SCORE PANR bankrupt 4,8557075 13,082904 1,0129615 4,3 18,9515740 2014 bankrupt 4,5016565 6,4633144 0,9648936 4,5 11,9298646 4,5855666 6,3690590 1,2164381 5,7 12,1710638 2015 bankrupt 2016 33 bankrupt 2017 4,5000246 6,2441214 1,5384460 0,004 12,2825922 bankrupt 2018 PGLI 4,5015950 X1 6,2411563 X2 1,2233964 X3 X-SCORE 11,9661478 67,3012425 bankrupt A-SCORE ANALYZE bankrupt 4,517518 5,8781611 2,3805730 4,3 12,7762530 4,507223 5,8210809 3,7385581 4,5 14,0668624 4,509115 5,8528840 2,8543599 5,7 13,2163591 4,518247 5,9793635 0,8538117 0,004 11,3514226 4,550968 5,9559159 2,4397006 2014 bankrupt 2015 bankrupt 2016 bankrupt 2017 12,9465850 bankrupt 53,0060597 bankrupt A-SCORE ANALYZE 2018 X1 X2 X3 X-SCORE PJAA bankrupt 4,580782 6,152138 1,2366517 4,3 11,9695730 4,592457 6,128614 1,1757111 4,5 11,8967830 4,539797 6,214903 0,8861041 5,7 11,6408051 4,559802 6,168971 1,0392712 0,004 11,7680449 4,550980 6,212625 0,7996608 2014 Bankrupt 2015 Bankrupt 2016 Bankrupt 2017 11,5632674 Bankrupt 2018 58,8384736 PNSE X1 X2 X3 X-SCORE A-SCORE 4,3 2014 2015 4,552304 4,538162 6,074154 6,046238 2,8474792 1,6733206 -4,5 Not healthy ANALYZE Bankrupt 13,4739383 12,2577223 Bankrupt 34 5,7 Bankrupt 2016 4,507316 4,544887 6,152560 6,129902 1,7466536 1,6983464 2017 2018 12,4065303 12,3731361 Bankrupt 11,5987450 Bankrupt A-SCORE ANALYZE bankrupt 0,004 4,5053482 X1 6,0825972 X2 1,01079952 X3 X-SCORE PTSP 2014 4,517303 6,185938 1,4850479 4,3 12,1882906 4,554016 6,233814 0,9964573 4,5 11,7842885 4,507623 6,232493 0,9246262 5,7 11,6647438 4,536880 6,218301 0,8864584 0,004 11,6416411 bankrupt 4,557464 6,165881 1,06427081 11,7876160 bankrupt 59,0665801 bankrupt bankrupt 2015 bankrupt 2016 2017 2018 Source: Author Processed It can be seen from the results in a comprehensive sub sector of hotels, restaurants and tourism with unhealthy conditions. There are fluctuations in each year that give different results, there are healthy and unhealthy conditions. In this case the company maintains financial conditions so that every year is healthy. Tabel 6. Cut Off Point in Zmijewski Method Zmijewski  X < 0 Companies that have no potential to go bankrupt.  X > 0 Classified as an unhealthy company and has the potential to go bankrupt. CONCLUSION Based on the results of research and discussion, the authors conclude that: 1. The results of the research show that hotel and tourism hotel sub-sector companies are seen in the Altman Z-score analysis that this industry experiences healthy and unhealthy fluctuations in each year in each 35 company such as PANR, PGLI, but it remains visible after adding each year that the sector declared healthy and not potentially bankrupt in the company's position. 2. Generally, companies that are experiencing financial difficulties, working capital will fall faster and have a negative value to total assets which causes this ratio to decrease so that it can reduce the value of the Z-Score. The ratio of working capital to total assets is a ratio that can be used to predict the occurrence of financial difficulties. The results showed in the Zmijewski X-score analysis that the Hotel, Restaurant and Tourism Sector throughout the company is very possible for unhealthy companies, seen from their financial performance. 1. 2. 3. 4. SUGGESTION For business people the Z-Score analysis is useful as an early warning of bankruptcy. After knowing the potential bankruptcy of the company, the management immediately conducts evaluations and corrections appropriately so as to minimize the risk of bankruptcy, by improving performance and implementing appropriate turnaround strategies, will be much more able to control the condition. All companies in Zmijewski's analysis show that their current liabilities have risen, which will result in high current assets. Under current ratio, management needs to increase its current assets to finance operational activities and short-term liabilities. And companies with results above 1 while maintaining the current ratio in order to be able to meet short-term obligations. The management should pay attention to the debt that must be maintained, and the use of debt. REFERENSI Anandarajan, M., Simmers, C., & Igbaria, M. (2006). An exploratory investigation of the antecedents and impact of internet usage: An individual perspective. Behaviour and Information Technology, 19(1), 69-85. Ayu Suci Ramadhani & Niki Lukviarman. 2009. Perbandingan Analisis Prediksi Kebangkrutan Menggunakan Model Altman Pertama, Altman Revisi, dan Altman Modifikasi dengan Ukuran dan Umur Perusahaan Sebagai Variabel 36 Penjelas (Studi pada Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia). Jurnal Siasat Bisnis, Vol.13, No.1:15-28. Brahmana, Rayenda. K. (2007). Identifying Financial Distress Condition in Indonesia Manufacture Industry. Birmingham Business School, University of Birmingham United Kingdom. Halaman 1-19. Budhijana, Bambang dan Nelmida. 2018. “Analisis Risiko Kebangkrutan Pada Perusahaan Bank Umum Swasta Nasional yang Terdaftar di Bursa Efek Indonesia. STIE Indonesia Banking School”. Jurnal Akuntansi Keuangan dan Bisnis, 11 (1), 99-109. Darsono dan Ashari. (2005). Pedoman Praktis Memahami Laporan Keuangan. Yogyakarta: CV. Andi Offset Dwi Martini Dkk, (2012), Akuntansi Keuangan Menengah, buku 1, Salemba Empat, Jakarta. Edi dan May Tania. (2018). Ketepatan Model Altman, Springate, Zmijewski, dan Grover dalam Memprediksi Financial Distress. Jurnal Reviu Akuntansi dan Keuangan Vol. 8 No. 1, April 2018, Pp 79-92 Fahmi, Irham. (2011). Analisis Laporan Akuntansi. Bandung: ALFABETA. Hanifah, Oktita E. (2013). Pengaruh Struktur Corporate Governance Dan Financial Indicators Terhadap Kondisi Financial Distress.Skripsi Ilmiah Universitas Diponegoro Husein, M.F. dan Galuh, T.P., (2014), “Precision of The Model of Altman, Springate,Zmijewski and Grover for Predicting The Financial Distress”, Journal of Economics, Business and Accountancy Ventura, Vol. 17, No. 3, Hal 405-416. Kasmir, (2014). Analisis Laporan Keuangan, Edisi Pertama, Cetakan Ketujuh. Jakarta: PT. Rajagrafindo Persada Keputusan Menteri Parpostel no Km 94/HK103/MPPT 1987 Palepu, K. G. dan P. M. Healy. (2008). Business Analysis & Valuation-Using Financial Statements. Fourth Edition. Canada: Thomson South-Western. Platt, H., dan M. B. Platt. (2002). Predicting Financial Distress. Journal of 37 Financial Service Professionals, 56: 12-15. Pustylnick, l. (2012). "Restructuring The Financial Characteristies of Projects in Financial Distress". Global Journal of Business Research. Vol. 6, No. 2. pp.125-134. Toto, Prihadi. (2011). Analisis Laporan Keuangan Teori dan Aplikasi. Jakarta: Penerbit PPM Undang-Undang Nomor 9 Tahun 2009 Tentang Kepariwisataan Undang-Undang Nomor 37 Tahun 2004 tentang Kepailitan dan Penundaan Kewajiban Pembayaran Utang Wulandari, V., Nur, E., dan Julita. (2014). Analisis Perbandingan Model Altman, Springate, Ohlson, Fulmer, CA-Score, dan Zmijewski Dalam Memprediksi Financial Distress studi empiris pada Perusahaan Food and Beverages yang Terdaftar di Bursa Efek Indonesia Periode 2011-2012. JOM FEKON Vol. 1 No. 2 38