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DAFTAR LAMPIRAN
Lampiran 1 Sampel Penelitian
Lampiran 2 Data Variabel Dependen Opini Audit Going Concern
Lampiran 3 Data Variabel Independen Kualitas Audit
Lampiran 4 Data Variabel Independen Perubahan Penjualan
Lampiran 5 Data Variabel Independen Opini Audit Tahun Sebelumnya
Lampiran 6 Data Variabel Independen Audit Tenure
Lampiran 7 Data Variabel Independen Ukuran Perusahaan
Lampiran 8 Laba Perusahaan Tahun 2012-2015
Lampiran 9 Hasil Pengujian SPSS Versi 20
Lampiran 10 Formulir Konsultasi Skripsi
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 1
SAMPEL PERUSAHAAN
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
SAMPEL PENELITIAN
No. Kode Perusahaan
1 BIMA PT. Primarindo Asia Infrastructure Tbk
2 HDTX PT. Panasia Indo Resources Tbk
3 IKAI PT. Intikeramik Alamasri Industri Tbk
4 JKSW PT. Jakarta Kyoei Steel Works Tbk
5 KBRI
PT. Kertas Basuki Rachmat Indonesia
Tbk
6 MLIA PT. Mulia Industrindo Tbk
7 MYTX PT. Apac Citra Centertex Tbk
8 PSDN PT. Prashida Aneka Niaga Tbk
9 RMBA
PT. Bantoel Internasional Investama
Tbk
10 SCPI PT. Schering-Plough Indonesia Tbk
11 SSTM PT. Sunson Textile Manufacturer Tbk
12 TIRT PT. Tirta Mahakan Resources
13 YPAS PT. Yanaprima Hastapersada Tbk
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 2
DATA VARIABEL DEPENDEN
OPINI AUDIT GOING CONCERN
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL DEPENDEN OPINI AUDIT GOING CONCERN
Tahun No. Perusahaan Opini Audit Opini Audit Going Concern
2012 1 BIMA WTP dengan Paragraf Penjelas 1
2 HDTX WTP 0
3 IKAI WTP dengan Paragraf Penjelas 1
4 JKSW WTP dengan Paragraf Penjelas 1
5 KBRI WTP dengan Paragraf Penjelas 1
6 MLIA WTP 0
7 MYTX WTP dengan Paragraf Penjelas 1
8 PSDN WTP 0
9 RMBA WTP 0
10 SCPI WTP 0
11 SSTM WTP dengan Paragraf Penjelas 1
12 TIRT WTP 0
13 YPAS WTP 0
2013 14 BIMA WTP dengan Paragraf Penjelas 1
15 HDTX WTP 0
16 IKAI WTP dengan Paragraf Penjelas 1
17 JKSW WTP dengan Paragraf Penjelas 1
18 KBRI WTP dengan Paragraf Penjelas 1
19 MLIA WTP 0
20 MYTX WTP dengan Paragraf Penjelas 1
21 PSDN WTP 0
22 RMBA WTP 0
23 SCPI WTP 0
24 SSTM WTP dengan Paragraf Penjelas 1
25 TIRT WTP 0
26 YPAS WTP 0
2014 27 BIMA WTP dengan Paragraf Penjelas 1
28 HDTX WTP dengan Paragraf Penjelas 1
29 IKAI WTP dengan Paragraf Penjelas 1
30 JKSW WTP dengan Paragraf Penjelas 1
31 KBRI WTP 0
32 MLIA WTP 0
33 MYTX WTP dengan Paragraf Penjelas 1
34 PSDN WTP 0
35 RMBA WTP 0
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
36 SCPI WTP 0
37 SSTM WTP dengan Paragraf Penjelas 1
38 TIRT WTP 0
39 YPAS WTP 0
2015 40 BIMA WTP 0
41 HDTX WTP dengan Paragraf Penjelas 1
42 IKAI WTP dengan Paragraf Penjelas 1
43 JKSW WTP dengan Paragraf Penjelas 1
44 KBRI WTP dengan Paragraf Penjelas 1
45 MLIA WTP 0
46 MYTX WTP dengan Paragraf Penjelas 1
47 PSDN WTP 0
48 RMBA WTP 0
49 SCPI WTP 0
50 SSTM WTP dengan Paragraf Penjelas 1
51 TIRT WTP 0
52 YPAS WTP 0
Keterangan:
1 : Mendapatkan Opini Audit Going Concern
0 : Tidak mendapatkan Opini Audit Going Concern
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 3
DATA VARIABEL INDEPENDEN
KUALITAS AUDIT
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL INDEPENDEN KUALITAS AUDIT
Tahun No. Perusahaan Kantor Akuntan Publik Keterangan Kualitas Audit
2015 1 MYTX Kosasih, Nurdiyaman, Mulyadi, Tjahjo & Rekan Non Big-Four 0
2015 2 PSDN Ernst & Young Big-Four 1
2015 3 RMBA PWC Big-Four 1
Keterangan:
1 : Diaudit oleh KAP Big-Four
0 : Tidak diaudit oleh KAP Big-Four
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 4
DATA VARIABEL INDEPENDEN
PERUBAHAN PENJUALAN
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL INDEPENDEN PERUBAHAN PENJUALAN
Tahun No Perusahaan Penjualan Bersih t Penjualan Bersih t-1 Perubahan Penjualan
2015 1 MYTX
1,891,190,000,000
2,129,058,000,000
(0.1117)
2015 2 PSDN
920,352,848,084
975,081,057,089
(0.0561)
2015 3 RMBA
16,814,352,000,000
14,489,473,000,000
0.1605
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 5
DATA VARIABEL INDEPENDEN
OPINI AUDIT TAHUN SEBELUMNYA
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL INDEPENDEN OPINI AUDIT TAHUN
SEBELUMNYA
Tahun No. Perusahaan Opini Audit Tahun Sebelumnya Variabel Dummy
2015 1 MYTX WTP dengan Paragraf Penjelas 1
2015 2 PSDN WTP 0
2015 3 RMBA WTP 0
Keterangan:
1: Mendapatkan Opini audit going concern pada tahun sebelumnya
0: Tidak mendapatkan opini audit going concern pada tahun sebelumnya
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 6
DATA VARIABEL INDEPENDEN
AUDIT TENURE
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL INDEPENDEN AUDIT TENURE
Tahun No Perusahaan KAP Tenure
2015 1 MYTX CROWE HORWATH (KOKASIH, NURDIYAMAN, TJAHJO & REKAN) 2
2015 2 PSDN E&Y (PURWANTONO, SUNGKORO, SURJA) 1
2015 3 RMBA PWC (TANUDIREDJA, WIBISANA, RINTIS & REKAN) 1
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 7
DATA VARIABEL INDEPENDEN
UKURAN PERUSAHAAN
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
DATA VARIABEL INDEPENDEN UKURAN PERUSAHAAN
Tahun No. Perusahaan Total Asset Ln(Total asset)
2015 1 MYTX 1,944,326,000,000
28.2959
2015 2 PSDN 620,398,854,182
27.1536
2015 3 RMBA 12,667,314,000,000
30.1700
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 8
LABA PERUSAHAAN TAHUN 2012-2015
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LABA PERUSAHAAN TAHUN 2012-2015
No. Perusahaan 2012 2013 2014 2015
1 INTP 1,381,404,000,000 1,381,404,000,000 5,165,458,000,000 4,258,600,000,000
2 SMCB 1,381,404,000,000 1,006,363,000,000 568,820,000,000 144,983,000,000
3 SMGR 4,924,791,472,000 5,852,022,665,000 5,567,659,839,000 4,525,441,038,000
4 AMFG 346,609,000,000 338,358,000,000 442,307,000,000 323,503,000,000
5 ARNA 266,118,538,480 266,118,538,480 266,118,538,480 74,225,510,161
6 IKAI (39.675.848.691) (43.088.205.688) (26.157.472.796) (108.888.289.285)
7 KIAS 71,039,439,692 75,380,306,268 79,640,638,204 (163,719,244,899)
8 MLIA 364.992.327.000 (41.145.917.000) 91.468.418.000 (40.236.722.000)
9 TOTO 236,695,643,357 236,557,513,162 241,892,785,681 337,987,688,612
10 ALKA 6,265,745,000 4,720,464,000 2,948,093,000 (1,175,538,000)
11 ALMI 13.949.141.063 26.118.732.307 3.664.436.757 (53.613.905.767)
12 BTON 24,654,012,986 25,638,457,550 7,703,192,171 5,822,534,834
13 GDST 47,551,790,582 91,488,056,551 17,567,630,050 (56,108,991,583)
14 INAI 23,155,488,541 5,019,540,731 15,101,078,482 129,166,716,157
15 JKSW (16.452.350.718) (7.968.797.416) (9.631.890.621) (23.096.657.780)
16 JPRS 9,689,801,241 15,012,528,941 (2.301.541.498) 5,169,298,198
17 LION 85,373,721,654 64,761,350,816 44,712,658,815 49,472,226,776
18 LMSH 41,282,515,026 14,382,899,194 7,155,613,989 808,326,453
19 MYRX 97,653,076,745 (119,320,061,253) 1,044,743,731 14,480,616,071
20 PICO 11,198,712,164 15,921,927,303 16,298,574,907 16,566,533,152
21 BUDI 3,650,000,000 39,795,000,000 25,685,000,000 146,466,000,000
22 DPNS 24,449,221,203 68,001,612,724 14,528,830,097 9,859,176,172
23 EKAD 49,223,703,788 51,319,954,316 41,830,240,865 30,401,400,924
24 ETWA 38,599,793,625 7,911,201,004 (142,136,321,265)
25 INCI 4,443,840,864 10,331,808,096 10,307,502,624 17,623,914,396
26 SRSN 16,963,915,000 45,171,491,000 10,620,918,000 16,049,623,000
27 AKKU (2,027,005,099) (1,460,331,413) (5,945,039,944)
28 AKPI 78,710,199,000 186,069,510,000 34,659,623,000 27,644,714,000
29 APLI 4,203,700,813 1,881,586,263 10,567,292,555 1,196,254,769
30 BRNA 60,643,256,000 21,632,494,000 57,814,311,000 440,171,662,000
31 IGAR 44,507,701,367 35,030,416,158 53,840,942,025 52,790,235,852
32 SIAP 3,389,850,176 (5,779,119,000) 7,382,322,000
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
33 SIMA (5,233,828,406) (6,847,658,379) 1,378,596,138
34 TRST 112,201,202,609 384,764,680,986 30,256,039,162 25,314,103,403
35 YPAS 16,472,534,252 6,221,712,803 (9,578,404,252) (10,316,229,299)
36 CPIN 2,680,872,000,000 2,528,690,000,000 1,745,724,000,000 1,832,598,000,000
37 JPFA 1,077,433,000,000 661,699,000,000 330,515,000,000 925,458,000,000
38 MAIN 302.421.030.000 241.632.645.000 86.880.617.000 (65.454.226.000 )
39 SIPD 15.061.473.532 8.377.508.652 4.911.808.710 ( 355.915.415.181 )
40 TIRT (32.217.613.525) (46.278.445.426) 19.977.876.543 17.337.813.630
41 ALDO 13.834.744.635 32.879.579.893 20.997.314.595 24.085.227.893
42 FASW 5.292.462.870 (249.057.875.558) 82.303.094.786 866.413.258.124
43 KBRI 36.542.090.733 (18.220.913.379) (16.408.068.168) (155.785.816.987)
44 SPMA 39.967.353.729 23.957.993.102 48.961.046.055 (43.104.604.508)
45 ASII 22.460.000.000.000 23.708.000.000.000 22.157.000.000.000 16.454.000.000.000
46 AUTO 1.135.914.000.000 1.058.015.000.000 1.147.851.000.000 279.235.000.000
47 GJTL 1.086.114.000.000 340.488.000.000 171.279.000.000 (207.955.000.000)
48 IMAS 884.981.697.692 805.480.217.885 7.957.904.794 (8.573.318.114)
49 INDS 540.323.808.599 411.289.306.390 125.350.362.943 132.465.180.205
50 LPIN 16.599.848.712 8.554.996.356 (4.130.648.465)
51 NIPS 43.499.421.000 33.872.112.000 115.210.374.000 26.779.572.000
52 PRAS 41.448.799.424 87.154.383.485 111.249.192.142 49.582.224.493
53 SMSM 254.635.403.407 338.222.792.309 411.162.000.000 446.088.000.000
54 HDTX 3.102.049.511 (218.654.504.263) (107.123.756.000) (356.310.795.000)
55 MYTX (124.715.173.739) (61.110.602.174) (165.901.000.000) (296.054.000.000)
56 RICY 16.978.453.066 8,720,546,989 10,428,390,973 12,382,694,616
57 SSTM (14.137.186.803) (13.228.135.178) (14.048.178.774) (10.462.177.146)
58 TRIS 37.887.200.425 48.195.237.468 (1.425.068.094) 229.782.625
59 UNIT 352.726.678 831.855.726 205.531.281 731.817.838
60 BATA 69.343.398.000 44.373.679.000 69.755.185.000 128.895.612.000
61 BIMA 2.623.173.812 (16.149.760.144) 9.979.198.125 (2.639.975.210)
62 JECC 32.010.770.000 22.928.551.000 19.463.961.000 210.434.540.000
63 KBLI 125.214.298.269 73.566.557.566 80.877.279.698 116.753.268.219
64 KBLM 23.833.078.478 7.678.095.359 20,623,713,329 11,787,506,863
65 SCCO 169.741.648.691 104.962.314.423 137.032.574.346 152.543.050.307
66 ADES 83.376.000.000 55.656.000.000 30.624.000.000 36.224.000.000
67 AISA 253.664.000.000 346.728.000.000 371.370.000.000 379.032.000.000
68 ALTO 16.305.675.308 12.058.794.054 10.372.140.370 (24.163.431.625)
69 CEKA 58.344.237.476 64.871.947.610 39.026.238.204 102.342.342.230
70 DLTA 213.421.077.000 270.498.062.000 287.456.867.000 191.304.463.000
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
71 ICBP 2.287.242.000.000 2.286.639.000.000 2.543.396.000.000 3.025.095.000.000
72 INDF 4.871.745.000.000 5.161.247.000.000 4.866.097.000.000 4.867.347.000.000
73 MYOR 742.836.954.804 1.053.624.812.412 390.727.052.364 1.266.519.320.600
74 PSDN 25.623.404.271 21.322.248.834 (30.238.642.061) (43.116.341.800)
75 ROTI 149.149.548.025 158.015.270.921 192.411.981.898 263.710.727.440
76 SKBM 12.703.059.881 58.266.986.268 85.855.717.394 40.360.748.110
77 SKLT 7.962.693.771 11.440.014.188 6.468.015.448 18.202.605.538
78 STTP 74.626.183.474 115.824.193.258 125.940.441.093 183.516.218.337
79 ULTJ 353.431.619.485 325.127.420.664 284.526.155.237 524.199.537.504
80 GGRM 4.068.711.000.000 4.383.932.000.000 5.325.317.000.000 6.458.516.000.000
81 HMSP 9,805,421,000,000 10,807,957,000,000 10,014,995,000,000 10,355,007,000,000
82 RMBA (323.351.000.000) (919.928.000.000) (2.264.159.000.000) (1.629.718.000.000)
83 WIIM 77.301.783.553 132.378.983.720 116.469.426.444 125.706.275.922
84 DVLA 148.909.089.000 125.796.473.000 81.109.862.000 104.177.380.000
85 INAF 42.385.114.98 (54.222.595.302) 6.261.679.386 5.006.864.360
86 KAEF 205.763.997.378 215.642.329.977 263.890.829.083 187.943.098.802
87 KLBF 1.772.034.750.571 2.004.243.694.797 2.096.408.046.860 2.083.402.901.121
88 MERK 107.808.155.000 175.444.757.000 179.620.581.000 148.818.963.000
89 PYFA 5.308.221.363 6.195.800.338 2.984.435.919 4.125.447.891
90 SCPI (17.996.909.000) (12.167.645.000) (62.461.393.000) 144.728.883.000
91 TSPC 643.568.078.718 674.146.721.834 580.067.582.680 581.461.169.669
92 MBTO 46.349.076.902 16.755.803.870 4.209.673.280 ( 14.056.549.894)
93 MRAT 34.424.605.088 (1.022.684.132) 7.054.710.411 1.045.990.311
94 TCID 150.373.851.969 160.148.465.833 175.828.646.432 544.474.278.014
95 UNVR 4.839.145.000.000 5.352.625.000.000 6.073.068.000.000 5.864.386.000.000
96 KDSI 36.837.060.793 36.002.772.194 34.592.489.585 6.888.594.650
97 KICI 2.259.475.494 7.419.500.718 (1.146.177.329) 25.420.359.845
98 LMPI 2.340.674.019 (12.040.411.197) 1.746.709.496 3.968.046.308
99 VOKS 147.020.574.291 39.092.753.172 (89.531.148.200) 5.880.476.118
100 SQBI 135.248.606.000 149.521.096.000 164.808.009.000 148.660.621.000
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
LAMPIRAN 9
HASIL PENGUJIAN SPSS VERSI 20
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
HASIL PENGUJIAN SPSS VERSI 20
STATISTIK DESKRIPTIF
N Range Minimum Maximum Mean Std. Deviation
PP 52 6.68 -.73 5.95 .2502 .91439
TENURE 52 5.00 1.00 6.00 2.3846 1.35984
UP 52 4.85 25.32 30.17 27.5782 1.27367
Valid N (listwise) 52
TABEL FREKUENSI VARIABEL DEPENDEN
Frequency Percent Valid Percent Cumulative Percent
Valid
Non Opini Audit Going
Concern 28 53.8 53.8 53.8
Opini Audit Going Concern 24 46.2 46.2 100.0
Total 52 100.0 100.0
TABEL FREKUENSI VARIABEL KUALITAS AUDIT
Frequency Percent Valid Percent Cumulative Percent
Valid
Non Big-Four 36 69.2 69.2 69.2
Big-Four 16 30.8 30.8 100.0
Total 52 100.0 100.0
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
TABEL FREKUENSI OPINI AUDIT TAHUN SEBELUMNYA
Frequency Percent Valid Percent Cumulative Percent
Valid
Non Opini Audit Going
Concern 28 53.8 53.8 53.8
Opini Audit Going Concern 24 46.2 46.2 100.0
Total 52 100.0 100.0
TABEL -2LOG LIKELIHOOD AWAL
Iteration Historya,b,c
Iteration -2 Log likelihood Coefficients
Constant
Step 0 1 71.779 -.154
2 71.779 -.154
TABEL -2LOG LIKELIHOOD AKHIR
Iteration Historya,b,c,d
Iteration -2 Log likelihood Coefficients
Constant KA PP OATS TENURE UP
Step 1
1 27.434 -6.124 -.894 .336 3.209 .117 .160
2 21.723 -13.640 -2.153 .480 4.459 .302 .402
3 19.782 -21.308 -3.793 .555 5.285 .562 .652
4 19.150 -25.146 -5.341 .622 5.871 .797 .766
5 18.977 -25.979 -6.601 .662 6.123 .917 .783
6 18.924 -25.990 -7.657 .671 6.166 .942 .781
7 18.905 -25.980 -8.663 .672 6.168 .943 .780
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016
8 18.898 -25.980 -9.664 .672 6.168 .943 .780
9 18.896 -25.980 -10.665 .672 6.168 .943 .780
10 18.895 -25.980 -11.665 .672 6.168 .943 .780
11 18.895 -25.980 -12.665 .672 6.168 .943 .780
12 18.894 -25.980 -13.665 .672 6.168 .943 .780
13 18.894 -25.980 -14.665 .672 6.168 .943 .780
14 18.894 -25.980 -15.665 .672 6.168 .943 .780
15 18.894 -25.980 -16.665 .672 6.168 .943 .780
16 18.894 -25.980 -17.665 .672 6.168 .943 .780
17 18.894 -25.980 -18.665 .672 6.168 .943 .780
18 18.894 -25.980 -19.665 .672 6.168 .943 .780
19 18.894 -25.980 -20.665 .672 6.168 .943 .780
20 18.894 -25.980 -21.665 .672 6.168 .943 .780
TABEL UJI KOEFISIEN DETERMINASI
Model Summary
Step -2 Log likelihood Cox & Snell R
Square
Nagelkerke R
Square
1 18.895a .638 .853
TABEL UJI KELAYAKAN MODEL REGRESI
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 8.826 8 .357
Pengaruh Kualitas..., Agus Setiawan, FB UMN, 2016