df2 923.743
Sig. .000
Tests null hypothesis of equal population covariance
matrices.
Stepwise Statistics
Variables EnteredRemoved
a,b,c,d
Step Min. D Squared
Exact F Entered
Statistic Between
Groups Statistic
df1 df2
Sig. 1
NWC TO TA .082
3,00 and 4,00 .515
1 53.000
.476 2
RE TO TA .496
1,00 and 2,00 1.014
2 52.000
.370 3
S TO TA .961
3,00 and 4,00 1.941
3 51.000
.135 4
EQ TO TL 1.361
3,00 and 4,00 2.021
4 50.000
.106 At each step, the variable that maximizes the Mahalanobis distance between the two closest
groups is entered. a. Maximum number of steps is 10.
b. Maximum significance of F to enter is .05. c. Minimum significance of F to remove is .10.
d. F level, tolerance, or VIN insufficient for further computation.
Variables in the Analysis
Step Tolerance
Sig. of F to Remove
Min. D Squared Between
Groups 1
NWC TO TA 1.000
.026 2
NWC TO TA .923
.006 .058
2,00 and 3,00 RE TO TA
.923 .006
.082 3,00 and 4,00
3 NWC TO TA
.912 .006
.059 2,00 and 3,00
RE TO TA .921
.012 .226
3,00 and 4,00 S TO TA
.988 .019
.496 1,00 and 2,00
4 NWC TO TA
.818 .000
.289 2,00 and 3,00
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RE TO TA .898
.011 .463
3,00 and 4,00 S TO TA
.624 .000
.635 1,00 and 2,00
EQ TO TL .605
.000 .961
3,00 and 4,00
Variables Not in the Analysis
Step Tolerance Min. Tolerance
Sig. of F to Enter
Min. D Squared
Between Groups
NWC TO TA 1.000
1.000 .026
.082 3,00 and 4,00 RE TO TA
1.000 1.000
.026 .058 2,00 and 3,00
EBIT TO TA 1.000
1.000 .343
.007 2,00 and 4,00 EQ TO TL
1.000 1.000
.045 .012 3,00 and 4,00
S TO TA 1.000
1.000 .011
.001 2,00 and 3,00 1
RE TO TA .923
.923 .006
.496 1,00 and 2,00 EBIT TO TA
.923 .923
.300 .409 3,00 and 4,00
EQ TO TL .968
.968 .023
.108 3,00 and 4,00 S TO TA
.990 .990
.010 .226 3,00 and 4,00
2 EBIT TO TA
.906 .838
.363 .763 1,00 and 2,00
EQ TO TL .958
.886 .023
.635 1,00 and 2,00 S TO TA
.988 .912
.019 .961 3,00 and 4,00
3 EBIT TO TA
.905 .827
.367 1.162 3,00 and 4,00
EQ TO TL .605
.605 .000
1.361 3,00 and 4,00 4
EBIT TO TA .825
.552 .267
1.805 3,00 and 4,00
Summary of Canonical Discriminant Functions Eigenvalues
Functio n
Eigenvalue of Variance Cumulative Canonical
Correlation
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1 1.841
a
95.6 95.6
.805 2
.056
a
2.9 98.5
.231 3
.028
a
1.5 100.0
.165 a. First 3 canonical discriminant functions were used in the analysis.
Wilks Lambda
Test of Functions
Wilks Lambda Chi-square df
Sig. 1 through 3
.324 58.586
12 .000
2 through 3 .921
4.295 6
.637 3
.973 1.437
2 .487
Standardized Canonical Discriminant Function Coefficients
Function 1
2 3
NWC TO TA
.713 .765
.333 RE TO TA
.551 -.342
.800 EQ TO TL
1.013 .000
-.581 S TO TA
1.001 -.325
-.451
Structure Matrix
Function 1
2 3
NWC TO TA
.278 .893
.261 S TO TA
.346 -.397
-.166 RE TO TA
.292 -.550
.741 EQ TO TL
.292 .063
-.422 EBIT TO
TA
a
-.082 .197
.306
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Pooled within-groups correlations between discriminating variables and standardized
canonical discriminant functions Variables ordered by absolute size of
correlation within function.
. Largest absolute correlation between each variable and any discriminant function
a. This variable not used in the analysis.
Canonical Discriminant Function Coefficients
Function 1
2 3
NWC TO TA
5.003 5.370
2.336 RE TO TA
1.044 -.649
1.515 EQ TO TL
.556 .000
-.319 S TO TA
.824 -.267
-.372 Constant
-3.006 .046
-.111 Unstandardized coefficients
Functions at Group Centroids
KEBA NGK
RUTA N
Function
1 2
3 1.00
1.357 -.063
-.058 2.00
.131 .723
.108 3.00
-.887 -.129
.203 4.00
-1.951 .015
-.254 Unstandardized canonical
discriminant functions evaluated at group means
Classification Statistics
Classification Processing Summary
Processed 57
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Excluded Missing or out-of-range
group codes At least one missing
discriminating variable Used in Output
57
Prior Probabilities for Groups
KEBA NGKR
UTAN Prior
Cases Used in Analysis Unweighted
Weighted 1.00
.250 25
25.000 2.00
.250 5
5.000 3.00
.250 17
17.000 4.00
.250 10
10.000 Total
1.000 57
57.000
Classification Function Coefficients
KEBANGKRUTAN 1.00
2.00 3.00
4.00 NWC TO TA
21.088 19.559
10.113 4.496
RE TO TA 4.836
3.299 2.932
1.037 EQ TO TL
2.500 1.764
1.168 .722
S TO TA 3.471
2.189 1.543
.797 Constant
-10.958 -6.605
-3.741 -1.999
Fishers linear discriminant functions
Casewise Statistics
Case Numb
er Highest Group
Second Highest Group
PDd | G=g
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Actua l
Group Predicted
Group p
df PG=g |
D=d Squared
Mahalano bis
Distance to
Centroid Group
PG=g | D=d
Squared Mahalanobi
s Distance to Centroid
Origi nal
1 1
1 .013
3 .996
10.780 2
.004 21.955
2 4
4 .931
3 .576
.446 3
.382 1.266
3 4
4 .958
3 .597
.309 3
.320 1.554
4 3
1 .002
3 .591
14.680 3
.324 15.879
5 4
4 .993
3 .694
.090 3
.266 2.007
6 3
2 .898
3 .399
.595 3
.365 .777
7 2
3 .917
3 .394
.508 2
.349 .753
8 3
3 .813
3 .579
.950 4
.315 2.172
9 1
1 .016
3 .865
10.266 3
.102 14.533
10 3
4 .941
3 .484
.395 3
.438 .593
11 1
1 .003
3 .988
14.045 2
.012 22.945
12 1
3 .543
3 .410
2.145 2
.241 3.212
13 1
1 .568
3 .940
2.024 2
.050 7.889
14 3
2 .922
3 .429
.487 3
.357 .858
15 1
1 .690
3 .470
1.466 2
.268 2.589
16 4
4 .970
3 .524
.244 3
.366 .959
17 3
3 .938
3 .422
.409 4
.372 .661
18 1
1 .064
3 .498
7.250 2
.495 7.260
19 2
2 1.000 3
.538 .004
3 .232
1.685 20
1 1
.072 3
.990 6.989
2 .010
16.208 21
3 3
.987 3
.474 .139
4 .364
.667 22
1 2
.933 3
.506 .432
1 .285
1.582 23
1 1
.015 3
.775 10.515
2 .133
14.033 24
3 3
.990 3
.533 .113
4 .293
1.306 25
4 4
.997 3
.568 .052
3 .356
.983 26
3 3
.978 3
.459 .200
4 .293
1.100
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27 1
2 .998
3 .583
.040 3
.191 2.268
28 3
3 .970
3 .492
.245 4
.385 .736
29 3
3 .976
3 .452
.208 4
.258 1.327
30 1
1 .344
3 .797
3.323 2
.165 6.473
31 1
2 .478
3 .384
2.485 1
.367 2.575
32 1
1 .949
3 .856
.355 2
.124 4.216
33 3
3 .911
3 .417
.536 2
.273 1.383
34 4
4 .970
3 .747
.245 3
.231 2.588
35 4
4 .987
3 .533
.137 3
.365 .896
36 1
2 .000
3 .710
18.997 1
.289 20.794
37 2
2 .320
3 .695
3.509 1
.273 5.378
38 4
4 .912
3 .793
.532 3
.192 3.364
39 1
1 .436
3 .956
2.725 2
.042 8.980
40 3
3 .988
3 .465
.129 4
.349 .707
41 1
2 .958
3 .530
.309 1
.234 1.943
42 1
1 .028
3 .877
9.089 2
.105 13.338
43 1
3 .911
3 .453
.537 2
.282 1.482
44 4
4 .998
3 .603
.042 3
.327 1.265
45 2
3 .929
3 .433
.454 2
.293 1.232
46 3
3 .984
3 .459
.161 4
.276 1.175
47 3
3 .959
3 .525
.303 4
.321 1.286
48 3
2 .971
3 .443
.242 3
.306 .982
49 1
1 .448
3 .350
2.657 2
.340 2.711
50 1
3 .658
3 .367
1.607 2
.317 1.902
51 1
1 .990
3 .744
.115 2
.213 2.621
52 3
4 .909
3 .409
.544 3
.405 .563
53 1
2 .845
3 .374
.820 1
.343 .990
54 4
4 .991
3 .552
.110 3
.366 .933
55 1
2 .954
3 .459
.332 3
.318 1.064
56 2
2 .998
3 .574
.033 3
.219 1.955
57 1
1 .349
3 .419
3.287 2
.374 3.516
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. Misclassified case
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DAFTAR PUSTAKA
Adnan, M.A danKurniasih, E. 2002.Analisis Tingkat Kesehatan Perusahaan untuk Memprediksi Potensi Kebangkrutan dengan Pendekatan Metode Altman
Kasuspada Sepuluh Perusahaan di Indonesia Altman, E. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of
Corporate Bankcrupty,” Journal of Finance Altman, E. I. 2000. Predicting financial distress of companies: Revisiting the
Zscore and Zeta® Models. Journal of Banking Finance, 1. Ghozali, Imam 2006. Aplikasi Analis Multivariate dengan Program SPSS.
Semarang: Badan Penerbit Universitas Diponegoro Hadi, S, dan Anggreani, A. 2008. Pemilihan Prediktor Delisting Terbaik
Perbandingan Antara The Zwjewski, The Altman Model, dan The Springate Model. FE UI
Harahap, Syafri Sofyan 2001. Analisis Laporan Keuangan. Jakarta: PT. Rajagrafindo Persada
Harnanto, 1984. Analisa Laporan Keuangan. BPFE Yogyakarta Idrus, Muhammad 2009. Metode Penelitian Ilmu Sosial. Yogyakarta: Erlangga
Nugroho, Mokhamad Iqbal Dwi 2012. Analisis Prediksi Financial Distress dengan Menggunakan Model Altman Z-Score Modifikasi 1995 Studi Kasus Pada
Perusahaan Manufaktur Yang Go Public di Indonesia Tahun 2008 sampai dengan
Tahun 2010. Semarang: Universitas Diponegoro
Rosy, Tita 2010. Analisis Diskriminan. S. Munawir 2002. Analisis Informasi Keuangan. Yogyakarta: Liberty Yogyakarta
ST. Ibrahim Mustafa Kamal 2010. Analisis Prediksi Kebangkrutan pada Perusahaan Perbankan Go Public yang Terdaftar di Bursa Efek Indonesia
dengan Menggunakan Metode Altman Z Score. Makassar Sugiyono 2008. Metode Penelitian Bisnis. Bandung: Alfabeta
www.bapepam.go.id www.idx.co.id
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BAB III METODE PENELITIAN
3.1 Variabel Penelitian
