BAB V. KESIMPULAN DAN SARAN
5.2. Saran
Penelitian ini berdasarkan pada sisi cost dari konservatisme misalnya, konservatisme akuntansi menyebabkan laba lebih banyak mempunyai ganguan (transitory). Padahal menurut Watts (2003a), praktek akuntansi menghasilkan efisiensi kontrak dan pajak, biaya litigasi, dan biaya regulator yang lebih rendah, yang mana hal tersebut adalah sisi yang menguntungkan menurut pandangan investor. Penelitian ini tidak mengontrol dan memasukkan manfaat-manfaat tersebut, maka untuk penelitian selanjutnya diharapkan dapat mengembangkan sebuah model yang lebih komprehensif tentang bagaimana konservatisme akuntansi mempengaruhi persistensi laba
Bagaimanapun juga tidak ada kesepakatan untuk mengukur besarnya konservatisme (Givoly et al, 2007), hal itu memungkinkan perhitungan konservatisme akuntansi dengan perhitungan akrual justru merepresentasikan fenomena ekonomi lain selain konservatisme akuntansi misalnya manajemen laba.
Penelitian selanjutnya diharapkan dapat menggunakan berbagai pengukuran sekaligus untuk mengurangi kesalahan dan menutupi kekurangan yang ada pada masing-masing pengukuran.
Penelitian ini hanya berfokus pada akuntansi konservatif dengan membedakan antara laba yang lebih konservatif dengan laba yang kurang konservatif. Penelitian selanjutnya diharapkan dapat menginvestigasi pengaruh konservatisme akuntansi terhadap persistensi laba dengan membedakan antara laba yang konservatif dan laba yang agresif.
Ball, R., Shivakumar, L., 2005. Earnings Quality in UK Private Firms:
Comparative Loss Recognition Timeliness. Journal of Accounting and Economics 39, 83-128.
Basu, S., 1997. The Conservatism Principle and The Asymmetric Timeliness of Earnings. Journal of Accounting and Economics 24, 3−37.
Beaver, W. H., Ryan, S. G., 2005. Conditional and Unconditional Conservatism:
Concepts and Modeling. Review of Accounting Studies 10, 269-309.
Chandrarin, G. 2001. Laba (Rugi) Selisih Kurs Sebagai Salah Satu Faktor yang Mempengaruhi Koefisien Respon Laba Akuntansi: Bukti Empiris dari Pasar Modal Indonesia. Disertasi. Yogyakarta: Universitas Gadjah Mada.
Dechow, P. M., 1994. Accounting Earnings and Cash Flows as Measures of Firm Performance: The Role of Accounting Accruals. Journal of Accounting and Economics 18, 3 - 42.
Dechow, P. M., Kothari, S. P., Watts, R. L., 1998. The Relation Between Earnings and Cash Flows. Journal of Accounting and Economics 25, 133-168.
Dewi, A. A. A. Ratna., 2004. Pengaruh Konservatisma Laporan Keuangan terhadap Earnings Response Coefficient. Jurnal Riset Akuntansi Indonesia, Vol 7 No. 2, Mei: 207-223.
Francis, J., LaFond, R., Olsson, P. M., Schipper, K., 2004. Cost of Equity and Earnings Attributes. The Accounting Review 79, 967-1010.
Givoly, D., C. Hayn. 2000. The Changing Time-Series Properties of Earnings, Cash Flows and Accruals: Has Financial Accounting Become More Conservative? Journal of Accounting & Economics 29, June: 287-320.
Givoly, D.. Hayn, C., 2002. Rising Conservatism: Implications for Financial Analysis. Financial Analysts Journal 58, 56-74.
Givoly, D., Hayn, C., Natarajan, A., 2007. Measuring Reporting Conservatism.
The Accounting Review 82, 65–106.
Ghozali, Imam (2002). Aplikasi Analisis Multivariate Dengan Program SPSS.
Semarang: Universitas Diponegoro.
Greene, H. William., 1997. Econometric Analysis.3rd edition. New York University.
Gujarati, Damodar, N., “Basic Econometrics”, 3rd Edition, Mc Graw-Hill.
International Editions 1995.
Kieso, D., Weygandt, J. 2001. Intermediate Accounting. Willey & Sons.
Lasdi, L., 2008. Determinan Konservatisma Akuntansi. The National Conference UKWMS 2. 2-4.
Lipe, R.C. 1990. The Relation Between Stock Return, Accounting Earnings, and Alternative Information. Accounting Reviews (Januari), 49-71.
Mayangsari, S., dan Wilopo. 2002. Konservatsima Akuntansi, Value Relevance dan Discretionary Accruals: Implikasi Empiris Model Feltham dan Ohlson (1996). Jurnal Riset Akuntansi Indonesia 5, 229-310.
Research 22, 693-717.
Paek, Wonsun., Chen, Lucy Huajing., Sami, Heibatollah., 2006. Accounting Conservatism, Earnings Persistence and Pricing Multiples on Earnings.
Journal of Contemporary Accounting and Economics Symposium.
Penman, Stephen H. Financial Statement Analysis and Security Valuation. Singapore:
Mc Graw Hill., 2001.
Penman, S. H., X. J. Zhang. 2002. Accounting Conservatism: The Quality of Earnings and Stock Returns. The Accounting Review 77, 237-264
Ruddock, C., Taylor, S. J., Taylor, S. L., 2006. Non-Audit Services and Earnings Conservatism: Is Auditor Independence Impaired? Contemporary Accounting Research 23, 701-746.
Sloan, Richard G. 1996. Do Stock Prices Fully Reflect Information in Cash Flows and Accruals About Future Earnings? The Accounting Review 71, 289- 315.
Suwardjono. 1989. Teori Akuntansi: Perekayasaan Akuntansi Keuangan. Edisi Kedua BPFE: Yogyakarta.
Watts, R.L., 2003a. Conservatism in Accounting Part I: Explanations and Implications. Accounting Horizons 17, 207–221.
Watts, R. L., 2003b. Conservatism in Accounting Part II: Evidence and Research Opportunities. Accounting Horizon 17, 287-301.
William, Mendenhall., Sincich, Terry., 1996. A Second Course in Statistics, 5 edition. Prentice Hall.
Widya. 2004. Analisis Faktor-Faktor yang Mempengaruhi Pilihan Perusahaan terhadap Akuntansi Konservatif. Simposium Nasional Akuntansi 7.
Wolk, H., M. Tearney, dan J. Dodd. 2000. Accounting Theory: A Conceptual and Institutional Approach, Third Edition, South-Western College Publishing.
Data Personal
Raditia Maharani Astika Jakarta, 17 Juli 1987
Jl. Percetakan IV Komplek PERURI No. 39, Keramat Pela, Kebayoran Baru
Jakarta Selatan
085694778803/021-98812762 [email protected]
Riwayat Pendidikan
Sekolah Dasar : 1993-1999 (SDI Dwi Matra, Jakarta)
Sekolah Menengah Pertama : 1999-2000 (SLTPN 68, Jakarta)
2000-2002 (SLTPN 48, Jakarta)
Sekolah Menengah Atas : 2002-2005 (SMUN 29, Jakarta)
Perguruan Tinggi : 2005-sekarang (Indonesia Banking School)
Sertifikasi
Service Excellence
Customer Service Excellence Basic Treasury
Trade Finacing Credit Analysis
Islamic Economic Study Club
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
No. Kode No. Kode No. Kode No. Kode No. Kode No. Kode
1. AMFG 21. TPIA 41. KBRI 61. INDR 81. KBLM 101. HMSP
2. ARNA 22. UNIC 42. SAIP 62. KARW 82. SSCO 102. RMBA
3. KIAS 23. AKKU 43. AUTO 63. MYRX 83. VOKS 103. DVLA
4. MLIA 24. AKPI 44. BRAM 64. MYRXP 84. PTSN 104. INAF
5. TOTO 25. APLI 45. GDYR 65. MYTX 85. ARTI 105. KAEF
6. ALMI 26. BRNA 46. IMAS 66. PAFI 86. ASIA 106. MERK
7. BTON 27. DYNA 47. INDS 67. PBRX 87. KBLV 107. PYFA
8. CBTN 28. IGAR 48. LPIN 68. POLY 88. MYOH 108. SCPI
9. INAI 29. LAPD 49. NIPS 69. RDTX 89. ADES 109. SQBB
10. ITMA 30. SIAP 50. PRAS 70. RICY 90. AQUA 110. SQBI
11. JKSW 31. SIMA 51. SMSM 71. SSTM 91. CEKA 111. MRAT
12. LION 32. TALFA 52. SQMI 72. TFCO 92. DLTA 112. PROD
13. LMSH 33. TALFB 53. SUGI 73. UNIT 93. MLBI 113. TCID
14. PICO 34. MAIN 54. ARGO 74. UNTX 94. MYOR 114. KDSI
15. TBMS 35. SIPD 55. CNTB 75. BATA 95. PSDN 115. KICI
16. DPNS 36. DSUC 56. CNTX 76. BIMA 96. SKBM 116. LMPI
17. EKAD 37. SUDI 57. DOID 77. SIMM 97. SKLT
18. ETWA 38. TIRT 58. ERTX 78. IKBI 98. STTP
19. INCI 39. FASW 59. ESTI 79. JECC 99. ULTJ
20. SRSN 40. INRU 60. HDTX 80. KBLI 100 BATI
No. Kode
Ketersediaan Laporan Keuangan Terdapat Dinyatakan Masuk/Tidak Nilai Buku mata uang sebagai 2003 2004 2005 2006 2007 2008 Negatif selain rupiah Kriteria Sampel
1 AMFG ya
2 ARNA ya
3 KIAS x x x x tidak
4 MLIA ada tidak
5 TOTO ya
6 ALMI ya
7 BTON ya
8 CBTN dolar US tidak
9 INAI ya
10 ITMA x x x x tidak
11 JKSW ada tidak
12 LION Ya
13 LMSH Ya
14 PICO Ya
15 TBMS Ya
16 DPNS Ya
17 EKAD Ya
18 ETWA x x tidak
19 INCI ya
20 SRSN ya
21 TPIA x x x x tidak
22 UNIC dolar US tidak
23 AKKU ya
24 AKPI x x tidak
25 APLI ya
26 BRNA ya
27 DYNA ya
28 IGAR ya
29 LAPD x x tidak
30 SIAP x x x x tidak
31 SIMA x x tidak
32 TALFA x x x x x x tidak
33 TALFB x x x x x x tidak
34 MAIN x x tidak
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
38 TIRT ya
39 FASW ya
40 INRU x x x x tidak
41 KBRI x x x x tidak
42 SAIP ada tidak
43 AUTO ya
44 BRAM ya
45 GDYR ya
46 IMAS x x tidak
47 INDS ya
48 LPIN x x tidak
49 NIPS ya
50 PRAS ya
51 SMSM ya
52 SQMI ya
53 SUGI ya
54 ARGO x x tidak
55 CNTB x x x x x x tidak
56 CNTX ya
57 DOID ya
58 ERTX x x tidak
59 ESTI ya
60 HDTX ya
61 INDR dolar US tidak
62 KARW ada tidak
63 MYRX ada tidak
64 MYRXP x x x x x x tidak
65 MYTX x x tidak
66 PAFI ada tidak
67 PBRX ya
68 POLY ada tidak
69 RDTX ya
70 RICY ya
71 SSTM x x tidak
72 TFCO dolar US tidak
73 UNIT x x tidak
74 UNTX x x x x tidak
75 BATA ya
76 BIMA ada tidak
77 SIMM ada tidak
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
79 JECC ya
80 KBLI ada tidak
81 KBLM ya
82 SSCO x x tidak
83 VOKS ada tidak
84 PTSN x x x x tidak
85 ARTI ya
86 ASIA ada tidak
87 KBLV x x x x tidak
88 MYOH x x x x tidak
89 ADES ada tidak
90 AQUA ya
91 CEKA ya
92 DLTA ya
93 MLBI ya
94 MYOR ya
95 PSDN ada tidak
96 SKBM x x x x tidak
97 SKLT ada tidak
98 STTP ya
99 ULTJ ya
100 BATI ya
101 HMSP ya
102 RMBA ya
103 DVLA ya
104 INAF ya
105 KAEF ya
106 MERK ya
107 PYFA ya
108 SCPI ada tidak
109 SQBB x x x x x x tidak
110 SQBI ya
111 MRAT ya
112 PROD x x x x tidak
113 TCID ya
114 KDSI ya
115 KICI ya
116 LMPI ya
No. Kode Nama Perusahaan 1 AMFG PT. Asahimas Flat Glass, Tbk.
2 ARNA PT. Arwana Citramulia, Tbk.
3 TOTO PT. Surya Toto Indonesia, Tbk.
4 ALMI PT. Alumindo Light Metal Industry, Tbk.
5 BTON PT. Betonjaya Manunggal, Tbk.
6 INAI PT. Indal Alumunium Industry, Tbk.
7 LION PT. Lion Metal Works, Tbk.
8 LMSH PT. Lionmesh Prima, Tbk.
9 PICO PT. Pelangi Indah Canindo, Tbk.
10 TBMS PT. Tembaga Mulia Semanan, Tbk.
11 DPNS PT. Duta Pertiwi Nusantara, Tbk.
12 EKAD PT.Ekadharma International, Tbk.
13 INCI PT. Intanwijaya International, Tbk.
14 SRSN PT. Indo Acidatama, Tbk.
15 AKKU PT. Aneka Kemasindo Utama, Tbk.
16 APLI PT. Asiaplast Industries Tbk.
17 BRNA PT. Berlina, Tbk.
18 DYNA PT. Dynaplast, Tbk.
19 IGAR PT. Kageo Igar Jaya, Tbk.
20 SIPD PT. Sierad Produce, Tbk.
21 TIRT PT. Tirta Mahakam Resources, Tbk.
22 FASW PT. Fajar Surya Wisesa, Tbk.
23 AUTO PT. Astra Otopart, Tbk.
24 BRAM PT. Indo Kordsa, Tbk.
25 GDYR PT. Goodyear Indonesia, Tbk.
26 INDS PT. Indospring, Tbk.
27 NIPS PT. Nipress, Tbk.
28 PRAS PT. Prima Alloy Steel, Tbk.
29 SMSM PT. Selamat Sempurna, Tbk.
30 SQMI PT. Albond Makmur Usaha, Tbk.
31 SUGI PT. Sugi Samapersada, Tbk.
32 CNTX PT. Centex, Tbk.
Sumber: www.idx.co.id
No. Kode Nama Perusahaan 33 DOID PT. Delta Dunia Petroindo, Tbk.
34 ESTI PT. Evershine Textile, Tbk.
35 HDTX PT. Panasia Indosyntec, Tbk.
36 PBRX PT. Pan Brothers, Tbk.
37 RDTX PT. Roda Vivatex, Tbk.
38 RICY PT. Ricky Putra Globalindo , Tbk.
39 BATA PT. Sepatu Bata, Tbk.
40 IKBI PT. Sumi Indo Kabel, Tbk.
41 JECC PT. Jembo Cable Company, Tbk.
42 KBLM PT. Kabelindo Murni , Tbk.
43 ARTI PT. Ratu Prabu Energi, Tbk.
44 AQUA PT. Aqua Golden Mississippi, Tbk.
45 CEKA PT. Cahaya Kalbar, Tbk.
46 DLTA PT. Delta Jakarta, Tbk.
47 MLBI PT. Multi Bintang Indonesia, Tbk.
48 MYOR PT. Mayora, Tbk.
49 STTP PT. Siantar Top, Tbk.
50 ULTJ PT. Ultrajaya Milk Industry & Trading Company, Tbk.
51 BATI PT. BAT Indonesia, Tbk.
52 HMSP PT. Hanjaya Mandala Sampoerna, Tbk.
53 RMBA PT. Bentoel Internasional Investama, Tbk.
54 DVLA PT. Darya Varia Laboratoria, Tbk.
55 INAF PT. Indofarma, Tbk.
56 KAEF PT. Kimia Farma, Tbk.
57 MERK PT. Merck, Tbk.
58 PYFA PT. Pyridam Farma, Tbk.
59 SQBI PT. Bristol-Myers Squibb Indonesia, Tbk.
60 MRAT PT. Mustika Ratu, Tbk.
61 TCID PT. Mandom Indonesia, Tbk.
62 KDSI PT. Kedawung Setia Industrial, Tbk.
63 KICI PT. Kedaung Indah Can, Tbk.
64 LMPI PT. Langgeng Makmur Industri, Tbk.
Sumber: www.idx.co.id
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
No Kode 2004 2005 2006 2007
1 AMFG 206,790,918,000 212,552,927,000 -17,219,568,000 155,010,000,000
2 ARNA 25,132,994,688 35,419,452,396 28,254,221,836 43,432,893,193
3 TOTO 25,878,619,226 62,884,231,950 79,705,059,548 56,376,502,262
4 ALMI 36,189,830,780 37,355,408,992 83,210,869,967 31,726,079,871
5 BTON 2,335,664,854 1,749,713,170 817,906,024 8,783,660,793
6 INAI 2,318,609,615 -20,774,060,821 2,228,385,051 334,370,529
7 LION 23,552,933,831 19,022,953,658 20,642,386,061 25,298,384,327
8 LMSH 5,505,466,185 4,107,336,724 2,667,461,566 5,942,206,112
9 PICO -4,767,138,145 1,774,173,441 1,879,515,849 8,524,937,158
10 TBMS -3,879,692,138 -17,210,856,228 24,477,226,053 -1,983,789,654
11 DPNS 6,466,261,070 4,476,878,702 -2,624,878,660 1,377,235,706
12 EKAD 4,471,541,443 5,201,547,089 5,763,685,223 4,233,068,343
13 INCI 11,828,068,122 11,590,281,515 -4,629,688,033 3,868,283,091
14 SRSN -58,251,034,000 22,778,008,000 23,384,506,000 6,796,587,000
15 AKKU 2,522,208,452 1,484,831,847 120,066,832 -38,439,188
16 APLI -7,415,527,247 -4,345,755,936 66,309,799 -4,584,651,718
17 BRNA 16,037,301,932 3,322,119,996 -5,447,287,866 -4,584,651,718
18 DYNA 47,635,241,523 20,609,851,125 -6,677,854,137 772,714,548
19 IGAR 25,883,660,134 13,777,631,117 9,964,135,535 15,426,317,547
20 SIPD -154,346,261,312 -122,479,667,812 49,646,744,439 21,196,442,562
21 TIRT 10,066,524,594 10,109,787,846 1,286,073,544 788,068,768
22 FASW 4,685,596,822 5,828,050,163 101,728,361,874 121,970,185,307
23 AUTO 223,158,000,000 279,027,000,000 282,058,000,000 454,907,000,000
24 BRAM 42,421,686,000 119,495,991,000 18,313,909,000 39,148,712,000
25 GDYR 24,990,997,000 -6,690,497,000 25,396,749,000 42,399,174,000
26 INDS -19,009,195,147 -5,836,886,305 2,171,591,250 9,887,928,336
27 NIPS -2,872,791,622 3,069,003,450 8,038,546,349 5,084,512,970
28 PRAS 11,986,262,283 4,600,058,545 -2,761,453,528 2,773,564,587
29 SMSM 57,371,201,049 65,736,914,403 66,174,829,417 80,324,965,210
30 SQMI 722,830,141 -5,101,060,670 -7,902,592,702 -34,767,890,134
31 SUGI 1,519,453,422 -8,499,051,745 343,783,158 3,689,898,512
32 CNTX 117,000,000 -3,488,000,000 14,419,000,000 -37,800,000,000
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
36 PBRX -59,390,739,410 10,301,492,141 9,747,882,077 24,637,653,757
37 RDTX 11,586,624,460 21,134,214,479 34,577,577,436 34,821,603,229
38 RICY 27,309,604,658 37,460,647,273 38,225,888,121 41,395,873,587
39 BATA 35,062,540,000 25,086,055,000 20,160,771,000 34,577,678,000
40 IKBI 7,338,894,854 23,749,261,703 44,373,633,211 77,466,616,243
41 JECC 928,986,000 -2,044,077,000 592,901,000 22,921,580,000
42 KBLM -25,318,734,538 14,126,886,066 10,507,630,038 5,314,388,899
43 ARTI 2,600,190,628 2,931,509,469 -75,925,599,165 -30,610,794,637
44 AQUA 91,639,950,311 64,349,873,753 48,853,686,588 65,912,835,099
45 CEKA -23,200,301,773 -21,594,230,577 15,291,187,419 24,676,361,894
46 DLTA 38,696,202,000 56,405,259,000 43,284,214,000 47,330,712,000
47 MLBI 86,297,000,000 87,014,000,000 73,581,000,000 84,385,000,000
48 MYOR 86,106,504,805 45,730,497,043 93,575,798,388 141,589,137,703
49 STTP 28,559,471,784 10,636,507,502 14,426,010,016 15,594,767,180
50 ULTJ 4,414,264,100 4,527,739,591 14,731,717,216 30,318,644,678
51 BATI -17,497,000,000 19,082,000,000 -62,123,000,000 -34,218,000,000
52 HMSP 1,991,852,000,000 2,383,066,000,000 3,530,490,000,000 3,624,018,000,000
53 RMBA 80,938,123,594 108,165,604,794 145,509,661,778 242,916,734,144
54 DVLA 49,810,964,000 71,576,356,000 52,508,646,000 49,917,853,000
55 INAF 7,238,989,721 9,594,742,649 15,240,675,138 11,076,807,048
56 KAEF 77,754,621,341 52,826,570,670 43,989,948,288 52,189,435,346
57 MERK 57,238,518,000 57,700,045,000 86,537,702,000 89,484,528,000
58 PYFA 1,431,690,739 1,328,422,334 1,729,406,246 1,743,483,869
59 SQBI 40,351,880,000 9,047,730,000 43,172,161,000 52,176,192,000
60 MRAT 13,150,786,421 8,510,043,884 9,096,227,057 11,130,009,996
61 TCID 82,492,058,369 92,864,924,821 100,118,341,049 111,232,287,817
62 KDSI 22,697,143,696 -7,397,998,036 1,814,687,801 14,500,297,724
63 KICI -18,158,944,421 -10,163,747,480 -14,819,139,717 15,742,232,136
64 LMPI -50,778,534,882 130,314,041,829 3,313,269,414 12,400,202,336
No Kode 2004 2005 2006 2007
1 AMFG 125,953,147,000 125,089,450,000 130,623,430,000 138,875,000,000
2 ARNA 15,957,797,919 20,613,829,333 23,901,538,646 29,667,869,623
3 TOTO 28,709,535,085 42,938,612,007 54,086,469,616 55,427,832,206
4 ALMI 13,536,861,462 34,591,772,006 35,589,825,427 35,273,141,144
5 BTON 2,134,370,155 2,301,044,631 2,295,497,321 2,358,391,921
6 INAI 12,443,757,981 13,411,238,170 13,817,237,087 10,475,182,922
7 LION 2,694,343,738 2,959,553,666 2,687,956,683 2,833,484,795
8 LMSH 1,182,590,505 1,212,781,256 1,247,312,015 1,249,294,475
9 PICO 12,040,735,028 14,755,520,236 12,474,702,214 6,757,759,169
10 TBMS 16,744,475,691 17,753,441,485 20,744,748,405 15,361,783,743
11 DPNS 1,053,190,321 1,782,032,886 1,871,359,766 2,057,065,555
12 EKAD 1,874,463,091 1,336,791,698 1,801,578,468 1,769,156,834
13 INCI 6,577,179,362 6,968,688,578 7,028,325,081 6,689,199,072
14 SRSN 3,088,603,000 20,302,675,000 18,010,923,000 15,035,330,000
15 AKKU 1,016,783,524 1,319,333,229 1,760,349,198 1,880,110,364
16 APLI 10,871,040,246 10,722,304,307 10,528,334,065 10,685,663,408
17 BRNA 20,657,178,329 24,714,737,222 32,866,446,049 23,061,446,713
18 DYNA 63,962,151,247 73,836,925,413 85,181,715,791 91,923,859,583
19 IGAR 12,929,963,750 15,981,240,576 16,192,699,301 15,304,919,402
20 SIPD 78,904,834,566 38,231,362,490 35,262,742,639 34,510,900,337
21 TIRT 23,438,936,998 26,993,836,585 19,247,825,613 20,161,701,096
22 FASW 133,387,567,908 108,601,156,885 119,050,768,227 140,752,635,725
23 AUTO 86,470,000,000 116,175,000,000 108,996,000,000 91,956,000,000
24 BRAM 84,716,813,000 93,044,754,000 79,162,017,000 81,122,466,000
25 GDYR 27,449,766,000 45,596,448,000 27,519,687,000 28,070,265,000
26 INDS 6,233,244,616 14,920,800,098 15,781,116,069 23,486,821,189
27 NIPS 8,167,403,761 8,712,334,124 7,842,320,465 8,847,717,229
28 PRAS 34,485,845,864 35,921,156,731 24,970,950,490 19,512,014,661
29 SMSM 47,984,754,834 47,080,607,781 54,254,367,052 55,095,806,398
30 SQMI 3,460,771,137 2,913,863,736 1,635,386,188 1,566,909,684
31 SUGI 1,159,784,635 866,216,697 780,135,492 692,482,903
32 CNTX 12,844,000,000 16,853,000,000 20,403,000,000 21,397,000,000
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
2 ARNA 31,317,992,128 58,622,296,906 39,029,765,342 76,940,381,769
3 TOTO 46,138,451,257 45,675,277,564 99,309,247,387 79,856,791,203
4 ALMI -52,978,245,035 197,197,278,347 -152,054,144,344 -67,657,238,554
5 BTON 2,750,593,985 1,940,197,566 -322,476,554 3,664,291,871
6 INAI -10,566,519,746 -33,878,997,576 -83,399,010,371 -1,311,666,268
7 LION 6,244,683,149 15,645,147,049 26,486,098,319 13,321,147,405
8 LMSH 7,150,374,919 -547,289,814 978,767,179 -312,236,970
9 PICO -30,204,986,836 15,453,889,499 -33,956,211,764 -16,570,656,156 10 TBMS 50,567,760,109 46,886,646,445 -292,796,988,665 -61,446,650,212
11 DPNS 7,403,413,104 5,296,733,408 7,567,977,232 5,575,831,917
12 EKAD -40,379,375 9,224,134,788 -1,738,182,832 3,922,589,788
13 INCI 17,072,058,095 -10,050,629,608 19,858,148,663 81,546,598
14 SRSN -5,864,433,000 5,378,577,000 36,207,096,000 46,189,938,000
15 AKKU -3,242,806,292 5,159,927,438 -4,327,705,276 -2,126,775,144
16 APLI -25,884,734,693 2,854,339,813 19,218,022,988 -7,861,288,338
17 BRNA 47,646,434,345 20,634,809,925 21,960,659,366 12,697,251,924
18 DYNA 129,867,355,244 82,348,144,410 92,682,493,997 96,293,740,389
19 IGAR -7,275,679,774 28,196,590,820 30,283,455,354 15,950,157,393
20 SIPD 3,151,429,540 -18,265,136,081 -2,788,725,431 -70,058,788,816
21 TIRT 29,822,645,216 -132,819,185,794 71,850,849,724 -99,973,970,266 22 FASW 119,524,439,262 100,367,318,648 88,767,298,484 721,657,147,020 23 AUTO 122,953,000,000 189,883,000,000 268,303,000,000 241,784,000,000 24 BRAM 170,052,789,000 136,743,732,000 189,866,221,000 142,628,541,000
25 GDYR 32,184,128,000 41,769,798,000 61,168,900,000 148,206,680,000
26 INDS 4,836,157,876 6,407,378,329 -64,566,964,967 47,827,023,978
27 NIPS 15,987,037,583 17,508,950,419 -543,779,388 -41,195,139,502
28 PRAS 51,029,383,228 34,062,824,423 57,252,571,683 76,974,710,907
29 SMSM 49,058,716,002 153,723,622,176 74,242,454,642 105,956,006,338
30 SQMI -26,029,460,160 2,738,099,296 180,939,732 -5,725,155,403
31 SUGI -1,000,788,094 1,236,740,267 7,944,022,011 2,939,474,888
32 CNTX -12,489,000,000 23,607,000,000 8,568,000,000 -7,112,000,000
35 HDTX 107,819,057,082 76,085,757,154 59,321,162,495 59,251,188,061
36 PBRX 3,205,806,371 12,612,918,989 15,479,120,699 21,467,990,752
37 RDTX 21,940,630,951 19,146,081,480 16,853,566,952 15,484,853,373
38 RICY 7,576,878,462 10,443,598,867 15,012,443,851 14,091,391,342
39 BATA 8,449,143,000 8,449,667,000 9,468,559,000 10,132,827,000
40 IKBI 16,514,449,211 16,547,778,467 15,633,290,340 16,078,120,098
41 JECC 10,138,893,000 12,420,793,000 12,886,324,000 13,019,556,000
42 KBLM 10,077,592,490 5,268,304,037 5,496,831,043 4,034,162,627
43 ARTI 3,653,462,027 3,732,164,156 1,406,200,346 840,015,881
44 AQUA 77,858,511,929 77,268,720,272 71,279,509,130 75,040,997,390
45 CEKA 19,475,878,354 16,563,851,822 12,917,579,650 11,901,734,886
46 DLTA 19,347,883,000 19,839,229,000 21,140,363,000 20,099,628,000
47 MLBI 39,214,000,000 46,603,000,000 55,890,000,000 68,552,000,000
48 MYOR 69,820,100,624 78,409,292,875 81,111,492,008 93,496,180,749
49 STTP 25,509,685,918 26,242,325,139 24,899,387,631 24,659,541,089
50 ULTJ 51,332,863,175 60,275,833,445 62,067,762,438 67,701,640,534
51 BATI 20,789,000,000 20,542,000,000 20,449,000,000 19,974,000,000
52 HMSP 244,600,000,000 347,453,000,000 273,552,000,000 274,162,000,000
53 RMBA 60,821,819,514 40,338,826,083 39,954,903,187 66,065,783,030
54 DVLA 13,270,663,000 12,930,530,000 13,136,792,000 13,102,583,000
55 INAF 13,101,470,248 11,784,566,900 10,527,783,390 10,176,039,028
56 KAEF 29,789,284,651 29,723,462,518 29,910,435,248 28,679,685,131
57 MERK 7,524,341,000 7,008,299,000 6,662,803,000 7,486,191,000
58 PYFA 2,665,632,179 3,655,890,666 4,201,188,440 5,095,757,120
59 SQBI 7,466,462,000 7,060,723,000 7,280,559,000 7,519,670,000
60 MRAT 5,522,446,125 5,727,752,836 5,960,666,061 5,623,797,773
61 TCID 24,126,784,825 37,725,429,325 43,249,386,451 51,923,189,236
62 KDSI 20,769,436,698 20,236,807,364 5,409,976,725 19,784,998,942
63 KICI 12,879,958,473 7,480,471,502 6,384,041,738 1,461,546,239
64 LMPI 22,262,381,645 18,669,791,531 17,485,270,300 18,146,244,414
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
No Kode 2004 2005 2006 2007
33 DOID -134,600,151,905 -147,280,495,963 16,331,478,636 42,996,917,543
34 ESTI 37,440,006,949 -19,087,242,642 -2,395,385,409 15,145,642,160
35 HDTX 52,488,750,847 30,539,714,955 12,397,961,676 22,843,730,595
36 PBRX -715,571,498 -16,648,317,829 -76,926,375,182 -94,554,708,018
37 RDTX 46,790,254,862 63,178,668,200 40,361,290,612 44,123,703,688
38 RICY 5,589,267,025 10,305,568,501 11,539,802,862 49,562,519,098
39 BATA 52,662,356,000 52,278,029,000 86,643,507,000 75,428,460,000
40 IKBI 13,400,985,299 15,097,211,638 50,034,248,262 86,784,626,833
41 JECC -17,112,708,000 24,018,416,000 -2,409,289,000 -13,578,928,000
42 KBLM 7,181,306,683 11,599,365,299 3,846,052,901 7,739,209,207
43 ARTI -18,675,096,313 3,399,974,415 -3,391,228,615 -5,837,773,863
44 AQUA 84,618,259,914 98,104,626,199 56,659,911,185 115,989,209,324
45 CEKA 28,924,682,905 276,679,649 53,338,522,907 -93,671,017,770
46 DLTA 101,149,217,000 39,588,186,000 18,108,286,000 87,272,573,000
47 MLBI 150,110,000,000 144,525,000,000 166,742,000,000 227,271,000,000 48 MYOR 103,732,421,550 157,011,359,684 24,389,308,219 178,699,351,379
49 STTP 7,222,652,279 5,095,764,447 13,927,318,682 5,275,606,873
50 ULTJ 35,588,548,288 35,660,902,311 106,877,574,910 -63,543,756,329
51 BATI 62,539,000,000 78,796,000,000 -85,162,000,000 77,827,000,000
52 HMSP 2,871,554,000,000 2,058,731,000,000 3,538,693,000,000 1,786,380,000,000 53 RMBA 29,137,534,208 121,744,343,286 -115,042,512,289 -552,085,102,941
54 DVLA 55,668,000,000 74,205,344,000 59,093,405,000 93,490,909,000
55 INAF 159,254,060,699 -54,871,009,457 73,151,865,243 83,418,335,788
56 KAEF -75,045,127,891 30,595,857,912 140,242,601,504 55,512,643,134
57 MERK 83,583,440,000 67,653,670,000 116,776,903,000 69,052,324,000
58 PYFA 4,538,448,431 1,574,217,070 -2,704,440,928 3,832,736,959
59 SQBI 17,696,841,000 43,693,943,000 98,975,851,000 35,014,670,000
60 MRAT 21,945,974,829 11,719,905,701 2,333,316,545 16,550,490,276
61 TCID 83,347,996,534 92,356,978,844 90,108,309,327 178,542,842,753
62 KDSI -607,030,138 18,272,383,900 28,783,368,497 16,407,376,449
63 KICI -13,061,265,388 1,242,074,181 -3,620,254,157 -6,526,829,115
64 LMPI -1,790,079,334 47,385,673 6,105,983,699 14,710,781,055
No Kode 2003 2004 2005 2006
1 AMFG 1,486,586,942,000 1,564,030,543,000 1,565,678,921,000 1,629,668,575,000 2 ARNA 248,099,816,150 295,971,426,534 364,794,072,950 478,777,623,456 3 TOTO 554,920,326,247 708,560,696,430 847,605,367,374 908,168,166,154 4 ALMI 1,008,172,756,263 931,926,710,601 805,744,972,702 1,249,710,084,265
5 BTON 23,460,767,870 28,780,075,529 27,720,995,361 33,674,096,945
6 INAI 316,918,757,755 406,707,855,100 476,733,635,551 534,462,374,716 7 LION 120,625,981,339 146,703,433,443 165,030,141,024 187,689,454,220
8 LMSH 34,162,617,896 42,747,950,989 42,145,203,874 43,587,839,467
9 PICO 258,349,134,073 243,302,133,991 251,143,312,493 270,733,538,669 10 TBMS 558,372,176,887 710,413,904,212 835,562,027,353 955,614,487,711 11 DPNS 138,442,282,954 150,357,847,077 143,511,871,421 146,044,633,683
12 EKAD 60,824,948,967 63,086,296,942 74,768,256,575 74,646,682,542
13 INCI 169,118,863,112 179,909,867,431 179,211,074,854 172,782,450,281 14 SRSN 138,863,648,000 89,743,066,000 338,343,696,000 330,445,358,000
15 AKKU 16,141,974,052 37,628,493,244 41,377,657,176 51,236,165,744
16 APLI 293,098,787,661 309,087,843,937 291,309,252,249 267,424,019,221 17 BRNA 266,556,398,909 406,984,397,273 398,392,367,816 408,108,447,144 18 DYNA 766,929,921,245 998,117,706,463 1,073,711,601,854 1,123,945,535,253 19 IGAR 236,243,954,052 283,462,274,188 274,803,668,480 290,144,668,879 20 SIPD 1,265,566,123,778 1,254,008,868,592 1,157,779,436,700 1,113,796,114,575 21 TIRT 529,009,345,820 808,567,425,307 856,923,525,684 570,117,317,643 22 FASW 2,627,237,772,371 2,628,414,610,349 2,881,807,820,614 3,421,981,751,436 23 AUTO 1,957,303,000,000 2,436,481,000,000 3,028,465,000,000 3,028,160,000,000 24 BRAM 1,543,441,086,000 1,710,352,181,000 1,709,355,091,000 1,530,173,239,000 25 GDYR 392,262,794,000 440,841,057,000 458,736,896,000 454,850,967,000 26 INDS 273,676,544,284 351,139,743,988 459,703,456,907 490,604,325,073 27 NIPS 171,173,233,850 189,086,635,430 190,224,877,780 220,228,504,723 28 PRAS 368,825,315,247 438,200,793,235 561,115,028,226 593,160,244,451 29 SMSM 632,609,649,320 650,930,144,026 663,138,307,944 716,685,940,960
30 SQMI 92,730,400,624 91,141,795,066 80,159,271,826 70,753,911,591
31 SUGI 65,024,731,009 65,277,742,925 49,728,950,082 50,328,320,380
32 CNTX 264,471,000,000 309,683,000,000 314,851,000,000 336,618,000,000 Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
35 HDTX 1,863,038,755,694 1,113,478,491,441 1,036,533,198,305 1,108,895,851,891 36 PBRX 126,772,375,522 112,292,465,735 390,215,826,546 553,846,048,245 37 RDTX 309,646,134,550 322,870,816,585 364,827,629,328 533,788,378,185 38 RICY 263,826,581,702 297,376,681,891 417,333,266,403 516,487,883,250 39 BATA 232,263,396,000 262,535,215,000 305,778,892,000 271,460,708,000 40 IKBI 369,799,184,620 445,145,483,805 548,244,926,925 590,295,976,330 41 JECC 277,989,762,000 302,022,257,000 322,661,922,000 362,647,601,000 42 KBLM 206,357,535,890 233,535,160,464 259,790,650,418 279,438,087,218 43 ARTI 306,242,736,329 348,116,130,249 365,549,769,549 119,944,852,445 44 AQUA 523,301,710,282 671,108,819,905 732,354,162,144 795,244,017,131 45 CEKA 295,248,814,434 290,336,868,389 328,249,320,427 280,806,653,865 46 DLTA 398,857,103,000 455,117,262,000 537,784,507,000 577,411,403,000 47 MLBI 483,004,000,000 558,388,000,000 575,385,000,000 610,437,000,000 48 MYOR 1,284,778,602,018 1,280,645,006,435 1,459,968,922,850 1,553,376,827,333 49 STTP 505,507,132,281 470,177,175,840 477,443,560,343 467,491,119,280 50 ULTJ 1,120,850,814,004 1,300,239,863,890 1,254,444,147,713 1,249,080,371,256 51 BATI 648,344,000,000 696,241,000,000 681,787,000,000 611,963,000,000 52 HMSP 10,197,768,000,000 11,563,295,000,000 11,934,600,000,000 12,659,804,000,000 53 RMBA 2,015,102,271,274 1,956,823,253,998 1,842,317,142,876 2,347,941,632,229 54 DVLA 375,386,088,000 431,173,982,000 550,628,937,000 557,337,641,000 55 INAF 629,202,669,315 523,923,104,642 518,823,729,815 686,937,377,885 56 KAEF 1,368,145,017,808 1,173,438,430,584 1,177,602,832,496 1,261,224,634,982 57 MERK 200,328,300,000 200,466,350,000 218,034,134,000 282,698,909,000
58 PYFA 68,267,469,758 70,429,780,958 76,550,878,274 83,127,282,484
59 SQBI 165,424,287,000 190,599,240,000 165,022,097,000 207,135,527,000 60 MRAT 275,179,993,855 294,415,332,598 290,646,485,673 291,768,931,718 61 TCID 387,600,926,590 472,364,307,114 545,695,228,731 672,196,585,121 62 KDSI 372,075,952,401 379,033,544,233 384,927,700,206 439,736,637,878 63 KICI 177,456,970,092 169,917,583,699 161,453,774,305 140,214,464,449 64 LMPI 501,283,975,144 509,105,219,036 505,172,478,369 508,864,677,279 Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
No Kode 2004 2005 2006 2007 1 AMFG 0.0173 0.0734 0.0389 -0.0145 2 ARNA 0.0394 -0.0087 0.0360 -0.0080 3 TOTO 0.0152 0.0849 0.0407 0.0352 4 ALMI 0.1019 -0.1344 0.3362 0.1078 5 BTON 0.0733 0.0733 0.1239 0.2221 6 INAI 0.0799 0.0652 0.2086 0.0227 7 LION 0.1658 0.0432 -0.0191 0.0789 8 LMSH -0.0135 0.1373 0.0697 0.1722 9 PICO 0.1451 0.0044 0.1924 0.1177 10 TBMS -0.0675 -0.0652 0.4045 0.0783 11 DPNS 0.0008 0.0064 -0.0580 -0.0147 12 EKAD 0.1050 -0.0426 0.1244 0.0279 13 INCI 0.0079 0.1590 -0.0974 0.0606 14 SRSN -0.3550 0.4201 0.0153 -0.0737 15 AKKU 0.4201 -0.0626 0.1500 0.0775 16 APLI 0.1001 0.0114 -0.0296 0.0522 17 BRNA -0.0411 0.0182 0.0137 0.0142 18 DYNA -0.0238 0.0121 -0.0132 -0.0032 19 IGAR 0.1951 0.0055 -0.0150 0.0509 20 SIPD -0.0621 -0.0526 0.0757 0.1129 21 TIRT 0.0070 0.2102 -0.0599 0.2121 22 FASW 0.0071 0.0053 0.0458 -0.1341 23 AUTO 0.0954 0.0843 0.0405 0.1007 24 BRAM -0.0278 0.0443 -0.0540 -0.0146 25 GDYR 0.0516 -0.0065 -0.0180 -0.1709 26 INDS -0.0644 0.0076 0.1795 -0.0295 27 NIPS -0.0625 -0.0303 0.0863 0.2503 28 PRAS -0.0124 0.0147 -0.0625 -0.0922 29 SMSM 0.0890 -0.0628 0.0696 0.0411 30 SQMI 0.3258 -0.0540 -0.0804 -0.3883 31 SUGI 0.0566 -0.1359 -0.1371 0.0287 32 CNTX 0.0962 -0.0331 0.0834 -0.0276 Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
35 HDTX 0.0208 0.1190 0.0456 0.0341 36 PBRX -0.4376 0.3523 0.2618 0.2540 37 RDTX -0.0428 -0.0709 0.0303 0.0116 38 RICY 0.1110 0.1264 0.0999 0.0115 39 BATA -0.0394 -0.0675 -0.1865 -0.1132 40 IKBI 0.0283 0.0566 0.0182 0.0115 41 JECC 0.1014 -0.0452 0.0492 0.1366 42 KBLM -0.1087 0.0334 0.0468 0.0058 43 ARTI 0.0814 0.0094 -0.1946 -0.1995 44 AQUA 0.1622 0.0648 0.0867 0.0314 45 CEKA -0.1106 -0.0183 -0.0766 0.4638 46 DLTA -0.1081 0.0805 0.0861 -0.0344 47 MLBI -0.0509 -0.0195 -0.0648 -0.1218 48 MYOR 0.0406 -0.0257 0.1029 0.0363 49 STTP 0.0927 0.0676 0.0532 0.0748 50 ULTJ 0.0180 0.0224 -0.0240 0.1293 51 BATI -0.0914 -0.0563 0.0638 -0.1505 52 HMSP -0.0623 0.0581 0.0222 0.1668 53 RMBA 0.0559 0.0137 0.1631 0.3667 54 DVLA 0.0197 0.0239 0.0119 -0.0547 55 INAF -0.2208 0.1455 -0.0913 -0.0905 56 KAEF 0.1335 0.0443 -0.0563 0.0201 57 MERK -0.0939 -0.0147 -0.1081 0.0988 58 PYFA -0.0065 0.0484 0.1128 0.0362 59 SQBI 0.1821 -0.1447 -0.2940 0.1192 60 MRAT -0.0119 0.0086 0.0438 0.0007 61 TCID 0.0600 0.0809 0.0976 -0.0229 62 KDSI 0.1185 -0.0143 -0.0560 0.0407 63 KICI 0.0439 -0.0231 -0.0298 0.1692 64 LMPI -0.0533 0.2925 0.0291 0.0311 Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
Laba per Lembar Saham (Earning Per Share)
No Kode 2004 2005 2006 2007 2008
1 AMFG 476.000 490.000 -40.000 357.000 526.000
2 ARNA 28.000 39.000 31.000 47.000 59.000
3 TOTO 522.000 1,269.000 1,609.000 1,138.000 1,278.000
4 ALMI 117.500 121.280 270.170 103.010 14.830
5 BTON 12.980 9.720 4.540 48.800 115.680
6 INAI 15.000 -131.150 79.160 2.110 6.360
7 LION 452.800 366.000 397.000 486.000 727.000
8 LMSH 573.490 428.000 278.000 619.000 962.000
9 PICO -9.000 3.120 3.310 15.000 22.850
10 TBMS -211.000 -937.000 1,333.000 -108.000 -1,680.000
11 DPNS 51.000 29.040 -8.510 4.160 -24.950
12 EKAD 21.430 9.300 10.310 7.570 8.240
13 INCI 65.000 64.000 -25.000 21.000 19.000
14 SRSN -26.480 3.940 3.880 4.270 1.130
15 AKKU 21.000 6.000 0.500 -0.200 -35.300
16 APLI -5.700 -3.340 0.050 -3.530 -3.710
17 BRNA 232.000 48.000 -79.000 150.000 212.000
18 DYNA 153.170 65.490 -21.220 2.460 0.010
19 IGAR 25.000 13.120 9.490 14.690 7.000
20 SIPD -213.250 -19.050 4.360 2.260 2.900
21 TIRT 11.000 10.000 1.000 1.000 -67.000
22 FASW 2.000 2.350 41.050 49.220 14.750
23 AUTO 293.890 362.000 366.000 590.000 734.000
24 BRAM 94.000 266.000 41.000 87.000 211.000
25 GDYR 610.000 -163.000 619.000 1,034.000 20.000
26 INDS -507.000 -158.000 58.000 264.000 849.000
27 NIPS -144.000 153.000 402.000 254.000 78.000
28 PRAS 102.000 7.800 -4.700 4.700 -25.200
29 SMSM 44.000 46.000 46.000 56.000 64.000
30 SQMI 2.400 -16.940 -26.240 -115.430 -39.910
31 SUGI 3.760 -21.010 0.850 9.120 4.410
32 CNTX 11.700 -349.000 1,442.000 -3,780.000 -9,167.000 Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
33 DOID 1.140 1.010 0.660 1.640 1.200
34 ESTI -7.340 -4.570 -25.250 -7.590 -10.930
35 HDTX 24.000 115.000 0.310 0.900 -74.190
36 PBRX 22.000 25.000 22.000 55.000 -93.000
37 RDTX 45.000 82.000 130.000 130.000 212.000
38 RICY 60.190 58.380 59.570 64.510 -14.610
39 BATA 2,697.000 1,930.000 1,551.000 2,660.000 12,120.000
40 IKBI 24.000 78.000 145.000 253.000 319.000
41 JECC 6.000 -14.000 4.000 152.000 0.520
42 KBLM -23.000 13.000 9.000 5.000 4.000
43 ARTI 13.270 14.960 -387.380 -156.180 30.610
44 AQUA 6,962.000 4,889.000 3,712.000 5,008.000 6,255.000
45 CEKA -78.000 -72.590 51.400 82.950 93.670
46 DLTA 2,417.000 3,522.000 2,703.000 2,956.000 5,230.000 47 MLBI 4,096.000 4,130.000 3,492.000 4,005.000 10,551.000
48 MYOR 111.000 60.000 122.000 185.000 256.000
49 STTP 21.830 8.120 11.010 11.900 3.680
50 ULTJ 2.000 2.000 5.000 10.000 105.000
51 BATI -265.000 289.000 -941.000 -518.000 -1,312.000
52 HMSP 454.000 544.000 804.000 827.000 889.000
53 RMBA 12.130 17.130 23.530 39.000 35.520
54 DVLA 89.000 128.000 94.000 89.000 126.000
55 INAF 2.340 3.100 4.920 3.570 1.620
56 KAEF 14.860 9.510 7.920 9.400 9.970
57 MERK 2.555 2.576 3.863 3.995 4.403
58 PYFA 2.670 2.480 3.230 3.260 4.320
59 SQBI 3,941.000 884.000 4,517.000 5,368.000 9,773.000
60 MRAT 31.000 20.000 21.000 26.000 52.000
61 TCID 529.000 586.000 562.000 591.000 590.000
62 KDSI 75.000 25.000 6.000 35.800 14.110
63 KICI -131.590 -73.650 -107.390 114.070 22.150
64 LMPI -114.000 144.000 3.000 12.000 3.000
Sumber: laporan keuangan perusahaan manufaktur (www.idx.co.id)
Descriptives
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 1\data.sav
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
EPS_masadepan 256 -9167.00 12120.00 415.4964 1661.26922
EPS_sekarang 256 -3780.00 6962.00 352.0809 1080.62474
Hi_CON_ACC 256 0 1 .50 .501
Valid N (listwise) 256
REGRESSION /MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT EPS_masadepan
/METHOD=ENTER EPS_sekarang Hi_CON_ACC /SCATTERPLOT=(*ZRESID ,*ZPRED )
/RESIDUALS DURBIN NORM(ZRESID) .
Regression
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 1\data.sav Variables Entered/Removed(b)
Model Variables Entered
Variables
Removed Method 1
Hi_CON_ACC,
EPS_sekarang(a) . Enter
a All requested variables entered.
b Dependent Variable: EPS_masadepan
Model Summary(b)
Model R R Square
Adjusted R Square
Std. Error of
the Estimate Durbin-Watson
1 .803(a) .644 .641 995.10068 1.962
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: EPS_masadepan
Residual 250527018.765 253 990225.371
Total 703752931.927 255
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: EPS_masadepan
Coefficients(a)
Model
Unstandardized Coefficients
Standardized
Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF B Std. Error
1 (Constant) 127.082 89.445 1.421 .157
EPS_sekarang 1.235 .058 .803 21.373 .000 .996 1.004
Hi_CON_ACC -292.922 124.656 -.088 -2.350 .020 .996 1.004
a Dependent Variable: EPS_masadepan
Collinearity Diagnostics(a)
Model Dimension
Eigenvalue (Constant)
Condition Index
EPS_sekarang Variance Proportions
Hi_CON_ACC (Constant) EPS_sekarang
1 1 1.892 1.000 .11 .08 .11
2 .817 1.522 .04 .91 .07
3 .291 2.550 .85 .01 .83
a Dependent Variable: EPS_masadepan
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value -4541.8135 8433.3271 415.4964 1333.17535 256
Residual -
5395.25732 8707.39844 .00000 991.19065 256
Std. Predicted Value -3.718 6.014 .000 1.000 256
Std. Residual -5.422 8.750 .000 .996 256
a Dependent Variable: EPS_masadepan
Observed Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0
Expected Cum Prob
1.0 0.8 0.6 0.4 0.2 0.0
Dependent Variable: EPS_masadepan
Regression Standardized Predicted Value 7.5 5.0
2.5 0.0
-2.5
Regression Standardized Residual
10
5
0
-5
-10
Scatterplot
Dependent Variable: EPS_masadepan
Descriptives
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 2\data.sav
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
EPS_masadepan 124 -115.430 115.680 10.71748 31.672883
EPS_sekarang 124 -115.430 115.000 14.15846 26.665143
Hi_CON_ACC 124 0 1 .52 .501
Valid N (listwise) 124
REGRESSION /MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT EPS_masadepan
/METHOD=ENTER EPS_sekarang Hi_CON_ACC /SCATTERPLOT=(*ZRESID ,*ZPRED )
/RESIDUALS DURBIN NORM(ZRESID) .
Regression
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 2\data.sav Variables Entered/Removed(b)
Model
Variables Entered
Variables
Removed Method
1 Hi_CON_AC
C,
EPS_sekaran g(a)
. Enter
a All requested variables entered.
b Dependent Variable: EPS_masadepan
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: EPS_masadepan
ANOVA(b)
Model
Sum of
Squares Df Mean Square F Sig.
1 Regression 20005.653 2 10002.827 11.707 .000(a)
Residual 103384.444 121 854.417
Total 123390.097 123
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: EPS_masadepan
Coefficients(a)
Model
Unstandardized Coefficients
Standardized
Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF B Std. Error
1 (Constant) 2.476 3.886 .637 .525
EPS_sekarang .460 .102 .388 4.533 .000 .947 1.056
Hi_CON_ACC 3.290 5.400 .052 .609 .543 .947 1.056
a Dependent Variable: EPS_masadepan
Collinearity Diagnostics(a)
Model Dimension
Eigenvalue (Constant)
Condition Index
EPS_sekarang Variance Proportions
Hi_CON_ACC (Constant) EPS_sekarang
1 1 2.126 1.000 .08 .09 .08
2 .599 1.884 .11 .91 .09
3 .276 2.776 .81 .00 .83
a Dependent Variable: EPS_masadepan
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value -50.65366 58.69790 10.71748 12.753336 124
Residual -
124.081360 98.187195 .000000 28.991791 124
Std. Predicted Value -4.812 3.762 .000 1.000 124
Std. Residual -4.245 3.359 .000 .992 124
a Dependent Variable: EPS_masadepan
Observed Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0
Expected Cum Prob
1.0 0.8 0.6 0.4 0.2 0.0
Dependent Variable: EPS_masadepan
Regression Standardized Predicted Value
4 2
0 -2
-4 -6
Regression Standardized Residual
4
2
0
-2
-4
-6
Scatterplot
Dependent Variable: EPS_masadepan
Descriptives
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 3\data.sav
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Ln_EPS_masadepan 152 -.42 9.40 4.2002 2.45579
EPS_sekarang 152 -144.00 6962.00 597.1770 1290.49472
Hi_CON_ACC 152 0 1 .55 .500
Valid N (listwise) 152
REGRESSION /MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Ln_EPS_masadepan
/METHOD=ENTER EPS_sekarang Hi_CON_ACC /SCATTERPLOT=(*ZRESID ,*ZPRED )
/RESIDUALS DURBIN NORM(ZRESID) .
Regression
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 3\data.sav
Variables Entered/Removed(b)
Model
Variables Entered
Variables
Removed Method
1 Hi_CON_AC
C,
EPS_sekaran g(a)
. Enter
a All requested variables entered.
b Dependent Variable: Ln_EPS_masadepan
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: Ln_EPS_masadepan
ANOVA(b)
Model
Sum of
Squares Df Mean Square F Sig.
1 Regression 442.445 2 221.222 70.398 .000(a)
Residual 468.223 149 3.142
Total 910.668 151
a Predictors: (Constant), Hi_CON_ACC, EPS_sekarang b Dependent Variable: Ln_EPS_masadepan
Coefficients(a)
Model
Unstandardized Coefficients
Standardized
Coefficients T Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF B Std. Error
1 (Constant) 3.433 .223 15.381 .000
EPS_sekarang .001 .000 .697 11.866 .000 1.000 1.000
Hi_CON_ACC -.045 .289 -.009 -.155 .877 1.000 1.000
a Dependent Variable: Ln_EPS_masadepan
Collinearity Diagnostics(a)
Model Dimension
Eigenvalue (Constant)
Condition Index
EPS_sekarang Variance Proportions
Hi_CON_ACC (Constant) EPS_sekarang
1 1 2.010 1.000 .08 .09 .09
2 .739 1.649 .03 .86 .11
3 .251 2.827 .89 .05 .81
a Dependent Variable: Ln_EPS_masadepan
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.2416 12.6227 4.2002 1.71175 152
Residual -4.12795 3.81042 .00000 1.76091 152
Std. Predicted Value -.560 4.920 .000 1.000 152
Std. Residual -2.329 2.150 .000 .993 152
a Dependent Variable: Ln_EPS_masadepan
Observed Cum Prob
1.0 0.8 0.6 0.4 0.2 0.0
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
Dependent Variable: Ln_EPS_masadepan
Regression Standardized Predicted Value
5 4
3 2
1 0
-1
Regression Standardized Residual
3 2 1 0 -1 -2 -3
Scatterplot
Dependent Variable: Ln_EPS_masadepan
Descriptives
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 4\data.sav
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Ln_EPS_masadepan 132 -.42 9.40 4.4518 2.45641
Ln_EPS_sekarang 132 -.42 8.85 4.3218 2.39591
Hi_CON_ACC 132 0 1 .55 .499
Valid N (listwise) 132
REGRESSION /MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Ln_EPS_masadepan
/METHOD=ENTER Ln_EPS_sekarang Hi_CON_ACC /SCATTERPLOT=(*ZRESID ,*ZPRED )
/RESIDUALS DURBIN NORM(ZRESID) .
Regression
[DataSet1] D:\SKRIPSI\langkah-langkah pengolahan SPSS\langkah 4\data.sav Variables Entered/Removed(b)
Model
Variables Entered
Variables
Removed Method
1 Hi_CON_AC
C,
Ln_EPS_sek arang(a)
. Enter
a All requested variables entered.
b Dependent Variable: Ln_EPS_masadepan
a Predictors: (Constant), Hi_CON_ACC, Ln_EPS_sekarang b Dependent Variable: Ln_EPS_masadepan
ANOVA(b)
Model
Sum of
Squares Df Mean Square F Sig.
1 Regression 727.154 2 363.577 741.026 .000(a)
Residual 63.293 129 .491
Total 790.446 131
a Predictors: (Constant), Hi_CON_ACC, Ln_EPS_sekarang b Dependent Variable: Ln_EPS_masadepan
Coefficients(a)
Model
Unstandardized Coefficients
Standardized
Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF B Std. Error
1 (Constant) .350 .141 2.490 .014
Ln_EPS_sekarang .985 .026 .960 38.497 .000 .997 1.003
Hi_CON_ACC -.278 .123 -.057 -2.266 .025 .997 1.003
a Dependent Variable: Ln_EPS_masadepan
Collinearity Diagnostics(a)
Model Dimension
Eigenvalue (Constant)
Condition Index
Ln_EPS_sekarang Variance Proportions
Hi_CON_ACC (Constant) Ln_EPS_sekarang
1 1 2.528 1.000 .03 .03 .05
2 .357 2.662 .03 .17 .84
3 .116 4.677 .94 .79 .11
a Dependent Variable: Ln_EPS_masadepan
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value -.0590 8.7843 4.4518 2.35601 132
Residual -1.93896 2.52337 .00000 .69509 132
Std. Predicted Value -1.915 1.839 .000 1.000 132
Std. Residual -2.768 3.602 .000 .992 132
a Dependent Variable: Ln_EPS_masadepan
Normal Parameters Mean 4.4518 4.3218 .55 Std.
Deviation 2.45641 2.39591 .499
Most Extreme Differences Absolute .081 .063 .368
Positive .081 .063 .313
Negative -.054 -.062 -.368
Kolmogorov-Smirnov Z .927 .723 4.226
Asymp. Sig. (2-tailed) .356 .673 .000
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) Ln_EPS_sekarang Hi_CON_ACC
-.060 .015 .033
.162 .029 .141
.045 .021
-.371 .516 .237
.712 .606 .813 a. Dependent Variable: absut