• Tidak ada hasil yang ditemukan

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMAAN OPINI AUDIT GOING CONCERN PADA PERUSAHAAN LQ 45 YANG TERDAFTAR DI BURSA EFEK INDONESIA - Unika Repository

N/A
N/A
Protected

Academic year: 2019

Membagikan "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMAAN OPINI AUDIT GOING CONCERN PADA PERUSAHAAN LQ 45 YANG TERDAFTAR DI BURSA EFEK INDONESIA - Unika Repository"

Copied!
23
0
0

Teks penuh

(1)
(2)

DAFTAR PERUSAHAAN LQ 45 2009- 2011

No kode

Nama

Perusahaan

1 AALI

Astra Agro Lestari Tbk

2 ADRO

Adaro Energi Tbk

3 ANTM

Aneka Tambang ( Persero ) Tbk

4 ASII

Astra International Tbk

5 BBCA

Bank Central Asia Tbk

6 BBNI

Bank Negara Indonesia

7 BBRI

Bank Rakyat Indonesia ( Persero ) Tbk

8 BDMN

Bank Danamon Indonesia Tbk

9 BMRI

Bank Mandiri (Persero ) Tbk

10 BNBR

Bakrie & Brothers Tbk

11 BTEL

Bakrie Telecom Tbk

12 BUMI

Bumi Resources Tbk

13 ELSA

Elnusa

Tbk

14 ELTY

Bakrieland

Development

Tbk

15 ENRG

Energi Mega Persada Tbk

16 GGRM

Gudang Garam Tbk

(3)

20 INTP

Indocement Tunggal Prakarsa Tbk

21 ISAT

Indosat

Tbk

22 ITMG

Indo Tambangraya Megah Tbk

23 JSMR

Jasa

Marga

Tbk

24 KLBF

Kalbe Farma Tbk

25 LPKR

Lippo Karawaci Tbk

26 LSIP

PP London Sumatera Tbk

27 MEDC

Medco Energi International Tbk

28 PGAS

Perusahaan Gas Negara ( Tbk)

29 PTBA

Tambang Batubara Bukit Asam Tbk

30 SMCB

Holcim Indonesia ( Tbk )

31 SMGR

Semen Gresik ( Persero ) Tbk

32 TINS

Timah

Tbk

33 TLKM

Telekomunikasi Indonesia Tbk

34 UNSP

Bakrie Sumatera Plantations Tbk

35 UNTR

United Tractors Tbk

(4)

HASIL SPSS

DESCRIPTIVES VARIABLES=Z EATGR SIZE ALAG TENURE KI KM OUTSIDE

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Notes

Output Created 28-Jan-2013 17:52:48

Comments

Input Active Dataset DataSet0

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 108

Missing Value Handling Definition of Missing User defined missing values are treated as

missing.

Cases Used All non-missing data are used.

Syntax DESCRIPTIVES VARIABLES=Z EATGR

SIZE ALAG TENURE KI KM OUTSIDE

/STATISTICS=MEAN STDDEV MIN MAX.

Resources Processor Time 0:00:00.015

Elapsed Time 0:00:00.016

(5)

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Z 108 -.72 5.17 2.1885 1.52003

EATGR 108 -79.46 8.94 -.6677 8.79429

SIZE 108 12.57 14.74 13.4219 .54277

ALAG 108 25 121 68.73 18.457

TENURE 108 1 6 4.18 1.766

KI 108 83.51000 100.00000 98.7807221 3.37210132

KM 108 .00000 16.49000 1.2786112 3.37861814

OUTSIDE 108 .14 1.00 .3596 .13576

Valid N (listwise) 108

(6)

Frequencies

Notes

Output Created 28-Jan-2013 17:52:58

Comments

Input Active Dataset DataSet0

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 108

Missing Value Handling Definition of Missing User-defined missing values are treated as

missing.

Cases Used Statistics are based on all cases with valid

data.

Syntax FREQUENCIES VARIABLES=GC KAP

PRIOP DEFAULT OS

/ORDER=ANALYSIS.

Resources Processor Time 0:00:00.016

Elapsed Time 0:00:00.015

(7)

Statistics

GC KAP PRIOP DEFAULT OS

N Valid 108 108 108 108 108

Missing 0 0 0 0 0

Frequency Table

GC

Frequency Percent Valid Percent Cumulative Percent

Valid NGCAO 91 84.3 84.3 84.3

GCAO 17 15.7 15.7 100.0

Total 108 100.0 100.0

KAP

Frequency Percent Valid Percent Cumulative Percent

Valid Non Big 4 24 22.2 22.2 22.2

Big 4 84 77.8 77.8 100.0

(8)

PRIOP

Frequency Percent Valid Percent Cumulative Percent

Valid NGCAO 92 85.2 85.2 85.2

GCAO 16 14.8 14.8 100.0

Total 108 100.0 100.0

DEFAULT

Frequency Percent Valid Percent Cumulative Percent

Valid tdk default 93 86.1 86.1 86.1

default 15 13.9 13.9 100.0

Total 108 100.0 100.0

OS

Frequency Percent Valid Percent Cumulative Percent

Valid Non OS 68 63.0 63.0 63.0

OS 40 37.0 37.0 100.0

Total 108 100.0 100.0

CROSSTABS

/TABLES=GC BY KAP PRIOP DEFAULT OS

/FORMAT=AVALUE TABLES

(9)

Crosstab

Notes

Output Created 28-Jan-2013 17:53:07

Comments

Input Active Dataset DataSet0

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 108

Missing Value Handling Definition of Missing User-defined missing values are treated as

missing.

Cases Used Statistics for each table are based on all the

cases with valid data in the specified

range(s) for all variables in each table.

Syntax CROSSTABS

/TABLES=GC BY KAP PRIOP DEFAULT

(10)

[DataSet0]

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

GC * KAP 108 100.0% 0 .0% 108 100.0%

GC * PRIOP 108 100.0% 0 .0% 108 100.0%

GC * DEFAULT 108 100.0% 0 .0% 108 100.0%

GC * OS 108 100.0% 0 .0% 108 100.0%

GC * KAP Crosstabulation

KAP

Total

Non Big 4 Big 4

GC NGCAO Count 9 82 91

% of Total 8.3% 75.9% 84.3%

GCAO Count 15 2 17

% of Total 13.9% 1.9% 15.7%

Total Count 24 84 108

(11)

GC * PRIOP Crosstabulation

PRIOP

Total

NGCAO GCAO

GC NGCAO Count 87 4 91

% of Total 80.6% 3.7% 84.3%

GCAO Count 5 12 17

% of Total 4.6% 11.1% 15.7%

Total Count 92 16 108

% of Total 85.2% 14.8% 100.0%

GC * DEFAULT Crosstabulation

DEFAULT

Total

Tdk default default

GC NGCAO Count 89 2 91

% of Total 82.4% 1.9% 84.3%

GCAO Count 4 13 17

% of Total 3.7% 12.0% 15.7%

Total Count 93 15 108

(12)

GC * OS Crosstabulation

OS

Total

NON OS OS

GC NGCAO Count 63 28 91

% of Total 58.3% 25.9% 84.3%

GCAO Count 5 12 17

% of Total 4.6% 11.1% 15.7%

Total Count 68 40 108

% of Total 63.0% 37.0% 100.0%

LOGISTIC REGRESSION VARIABLES GC

/METHOD=ENTER KAP Z PRIOP EATGR SIZE DEFAULT ALAG TENURE OS KI KM OUTSIDE

/CLASSPLOT

/PRINT=GOODFIT CORR ITER(1)

(13)

Logistic Regression

Notes

Output Created 28-Jan-2013 17:53:31

Comments

Input Active Dataset DataSet0

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 108

Missing Value Handling Definition of Missing User-defined missing values are treated as

missing

Syntax LOGISTIC REGRESSION VARIABLES GC

/METHOD=ENTER KAP Z PRIOP EATGR

SIZE DEFAULT ALAG TENURE OS KI KM

OUTSIDE

/CLASSPLOT

/PRINT=GOODFIT CORR ITER(1)

/CRITERIA=PIN(0.05) POUT(0.10)

ITERATE(20) CUT(0.5).

Resources Processor Time 0:00:00.032

(14)

[DataSet0]

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis 108 100.0

Missing Cases 0 .0

Total 108 100.0

Unselected Cases 0 .0

Total 108 100.0

a. If weight is in effect, see classification table for the total number of cases.

Dependent Variable

Encoding

Original

Value Internal Value

0 0

(15)

Block 0: Beginning Block

a. Constant is included in the model.

b. Initial -2 Log Likelihood: 94.035

c. Estimation terminated at iteration number 4 because

parameter estimates changed by less than .001.

Classification Tablea,b

a. Constant is included in the model.

(16)

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant -1.678 .264 40.315 1 .000 .187

Variables not in the Equation

Score df Sig.

Step 0 Variables KAP 50.868 1 .000

Z 18.584 1 .000

PRIOP 49.731 1 .000

EATGR .535 1 .465

SIZE 1.256 1 .262

DEFAULT 66.069 1 .000

ALAG 7.046 1 .008

TENURE 29.271 1 .000

OS 9.739 1 .002

KI .140 1 .708

KM .205 1 .651

OUTSIDE .119 1 .730

(17)

Block 1: Method = Enter

Iteration Historya,b,c,d

Iteration

-2 Log

likelihood

Coefficients

Constant KAP Z PRIOP EATGR SIZE DEFAULT ALAG TENURE OS KI KM OUTSIDE

Step 1 1 46.408 2.872 -.688 .093 1.137 -.001 .243 2.062 -.004 -.097 .066 -.071 -.069 .322

2 31.939 11.923 -1.221 .201 1.835 -.005 .468 2.760 -.012 -.247 .134 -.190 -.185 .836

3 27.349 26.548 -1.731 .333 2.256 -.008 .645 3.211 -.025 -.487 .092 -.351 -.347 1.678

4 25.850 41.455 -2.186 .432 2.491 -.009 .687 3.457 -.036 -.765 -.171 -.495 -.493 2.560

5 25.474 57.509 -2.435 .463 2.650 -.009 .639 3.533 -.041 -.998 -.511 -.641 -.641 3.203

6 25.429 80.941 -2.505 .471 2.733 -.009 .612 3.547 -.042 -1.110 -.683 -.869 -.868 3.515

7 25.426 110.472 -2.512 .472 2.748 -.009 .610 3.549 -.042 -1.127 -.707 -1.164 -1.163 3.570

8 25.425 141.108 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -1.470 -1.469 3.571

9 25.425 171.821 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -1.777 -1.776 3.571

10 25.425 202.599 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -2.085 -2.084 3.571

11 25.425 233.443 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -2.393 -2.393 3.571

12 25.425 264.356 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -2.702 -2.702 3.571

13 25.425 295.342 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -3.012 -3.011 3.571

(18)

Omnibus Tests of Model Coefficients

a. Estimation terminated at iteration number 20 because maximum

iterations has been reached. Final solution cannot be found.

15 25.425 357.533 -2.512 .473 2.748 -.009 .610 3.549 -.042 -1.128 -.707 -3.634 -3.633 3.571

b. Constant is included in the model.

c. Initial -2 Log Likelihood: 94.035

(19)

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 3.270 8 .916

Contingency Table for Hosmer and Lemeshow Test

GC = 0 GC = 1

Total

Observed Expected Observed Expected

Step 1 1 11 10.998 0 .002 11

2 11 10.994 0 .006 11

3 11 10.991 0 .009 11

4 11 10.983 0 .017 11

5 11 10.966 0 .034 11

6 11 10.919 0 .081 11

7 11 10.763 0 .237 11

8 10 9.763 1 1.237 11

9 3 4.360 8 6.640 11

(20)

Classification Tablea

Observed

Predicted

GC

Percentage Correct

0 1

Step 1 GC NGCAO 90 1 98.9

GCAO 3 14 82.4

Overall Percentage 96.3

(21)

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a KAP -2.512 1.778 1.995 1 .158 .081

Z .473 .673 .492 1 .483 1.604

PRIOP 2.748 1.305 4.435 1 .035 15.615

EATGR -.009 .051 .035 1 .853 .991

SIZE .610 1.246 .239 1 .625 1.840

DEFAULT 3.549 1.641 4.679 1 .031 34.770

ALAG -.042 .036 1.363 1 .243 .959

TENURE -1.128 .781 2.084 1 .149 .324

OS -.707 1.890 .140 1 .708 .493

KI -5.202 8787.914 .000 1 1.000 .006

KM -5.202 8787.914 .000 1 1.000 .006

OUTSIDE 3.571 4.245 .708 1 .400 35.553

Constant 514.350 878791.409 .000 1 1.000 2.397E223

a. Variable(s) entered on step 1: KAP, Z, PRIOP, EATGR, SIZE, DEFAULT, ALAG, TENURE, OS, KI, KM,

(22)

Correlation Matrix

Constant KAP Z PRIOP EATGR SIZE DEFAULT ALAG TENURE OS KI KM OUTSIDE

Step 1 Constant 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 -1.000 -1.000 .000

KAP .000 1.000 -.439 .168 -.055 -.108 -.103 .406 .076 .025 .000 .000 -.254

Z .000 -.439 1.000 .260 -.322 .441 .382 -.030 -.126 .106 .000 .000 .233

PRIOP .000 .168 .260 1.000 -.275 .007 .194 .001 -.264 -.017 .000 .000 .092

EATGR .000 -.055 -.322 -.275 1.000 -.224 -.088 -.088 .020 -.094 .000 .000 -.147

SIZE .000 -.108 .441 .007 -.224 1.000 .402 -.157 -.043 -.003 .000 .000 .141

DEFAULT .000 -.103 .382 .194 -.088 .402 1.000 -.465 .011 .010 .000 .000 -.131

ALAG .000 .406 -.030 .001 -.088 -.157 -.465 1.000 .194 .223 .000 .000 .255

TENURE .000 .076 -.126 -.264 .020 -.043 .011 .194 1.000 .726 .000 .000 -.316

OS .000 .025 .106 -.017 -.094 -.003 .010 .223 .726 1.000 .000 .000 .063

KI -1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 1.000 .000

KM -1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 1.000 .000

OUTSIDE .000 -.254 .233 .092 -.147 .141 -.131 .255 -.316 .063 .000 .000 1.000

Step number: 1

Observed Groups and Predicted Probabilities

80 +

+

(23)

Referensi

Dokumen terkait

Hasil penelitian menunjukkan bahwa terdapat hubungan antara tingkat pengetahuan tentang tanda bahaya kehamilan dengan sikap ibu hamil terhadap tanda bahaya kehamilan pada Ibu hamil

Untuk meningkatkan kemampuan siswa kelas IV SD Inpres dalam menulis paragraf, maka peneliti lakukan penelitian yang terdiri atas dua siklus dengan menerapkan

daerah aliran Sungai Wanggu karena masuknya air tawar dari aliran Sungai Wanggu dan Perumahan Citraland sehingga terjadi percampuran antara air laut dan air tawar

Manfaat dari penelitian ini adalah dapat memberikan informasi tentang memanfaatkan tepung ampas kecap sebagai alternatif bahan pakan ayam petelur tua terhadap

Nama Lengkap Calon Siswa.. Telp yg

Kecanggihan pengiriman dan komunikasi data digital melalui internet, pembuatan website menggunakan PHP sebagai bahasa pemrograman yang aman dan pemanfaatan MySQL sebagai database

LKH-0001 Angga Sigit Prasetyo Universitas Gadjah Mada Yogyakarta S1 Kajian Dampak Erupsi Gunungapi Merapi Di Obyek Wisata Kaliurang 2012 LKH-0002 Heru Basoro Universitas

• Matriks adalah kelompok bilangan yang disusun dalam suatu jajaran berbentuk persegi atau persegi panjang yang terdiri atas baris-baris dan kolom-kolom...