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Lampiran 1. Penentuan Panjang Gelombang Maksimum dan Kurva Baku
Kaptopril a. Panjang gelombang maksimum
λ (nm) Absorbansi 200 0,694 203 0,637 206 0,584 209 0,567 212 0,464 215 0,362 218 0,289 221 0,227 224 0,163 227 0,117
b. Kurva baku
No Pengambilan (ml) Kadar (mg%) Absorbansi 1. 0,050 0,500 0,222 2. 0,075 0,750 0,336 3. 0,100 1,000 0,425 4. 0,125 1,250 0,495 5. 0,150 1,500 0,601 6. 0,175 1,750 0,701
LR kadar vs absorbansi
A = 0,0442
B = 0,3726
r = 0,9982
Persamaan kurva baku :
Y= 0,3726 X + 0,0442 (X dalam mg%)
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Lampiran 2. Perhitungan Penyesuaian Formula
Contoh penyesuaian bobot tablet pada formula III (50% Na CMC : 50% Avicel
PH 102)
Jumlah tablet yang dibuat 450 buah dengan bobot tablet 250 mg
Mucilago PGA 5% yang dibutuhkan untuk membentuk massa granul yang baik =
22,5 ml
Serbuk PGA yang dibutuhkan untuk 1 formula = 5 g x mlml
1005,22
= 1,125 g
Serbuk PGA yang dibutuhkan untuk 1 tablet = 450125,1 = 0,0025 g
= 2,5 mg
Untuk menyesuaikan bobot tablet menjadi 250 mg, maka jumlah bahan pelicin
disesuaikan menjadi :
Bahan pelicin = 250 mg – (50 mg + 98 mg + 98 mg + 2,5 mg)
= 1,5 mg konsentrasi bahan pelicin 0,6 % dari massa
tablet
Bahan (mg) F III Kaptopril 50 Na CMC 98
Avicel PH 102 98 PGA 5% q.s
Mg stearat 4
60
Lampiran 3. Hasil Pemeriksaan Sifat Fisik Granul
a. Kecepatan alir granul (g/dtk)
Replikasi F I F II F III F IV F V 1 10,25 9,95 9,85 9,83 9,80 2 10 10 9,90 9,97 9,76 3 10,27 10,27 10 9,84 9,75
� 10,17 10,07 9,92 9,88 9,77 SD 0,150 0,172 0,076 0,08 0,026
b. Pengetapan
Formula I Replikasi Vo
(ml) Pengetapan (Vt) Tap
% 1 2 3 4 5 6 7 8 9 10 1 100 98 96 95 94 94 93 93 93 - - 7 2 100 97 96 94 94 93 92 92 92 - - 8 3 100 97 95 94 94 94 - - - - - 6 � 7 SD 1
Formula II Replikasi Vo
(ml) Pengetapan (Vt) Tap
% 1 2 3 4 5 6 7 8 9 10 1 100 95 94 93 93 92 92 92 92 - - 8 2 100 97 96 94 94 93 92 92 92 - - 8 3 100 97 95 94 94 94 93 92 91 91 91 9 � 8,333SD 0,577
Formula III
Replikasi Vo (ml)
Pengetapan (Vt) Tap %1 2 3 4 5 6 7 8 9 10
1 100 98 97 95 93 92 91 91 91 - - 9 2 100 96 95 94 93 91 91 91 9 3 100 97 95 94 93 92 92 92 8 � 8,67 SD 0,5777
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Formula IV Replikasi Vo
(ml) Pengetapan (Vt) Tap
% 1 2 3 4 5 6 7 8 9 10 1 100 96 95 94 93 92 92 92 - - - 8 2 100 97 96 93 93 93 93 - - - - 7 3 100 97 96 95 94 93 93 93 - - - 7 � 7,33 SD 0,577
Formula V
Replikasi Vo (ml)
Pengetapan (Vt) Tap % 1 2 3 4 5 6 7 8 9 10
1 100 96 95 94 93 92 91 90 90 90 - 10 2 100 97 96 93 92 91 90 89 89 89 - 11 3 100 97 96 95 94 93 92 91 89 89 89 11 � 10,33SD 0,709
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Lampiran 4. Hasil Pemeriksaan Sifat Fisik Tablet
1) Keseragaman bobot (mg)
No Keseragaman bobot (mg) F I F III F V F II F IV
1. 251,6 251,5 252,4 251,6 251,6 2. 252,4 250,7 251,5 252,4 253,4 3. 253,2 250,5 252,3 253,2 253,24. 252,6 251,8 250,1 252,6 252,6 5. 251,7 252,2 253,4 251,7 251,7 6. 253,9 253,1 250,9 253,9 253,9 7. 250,1 250,7 252,8 250,1 254,1 8. 253,8 251,9 252,4 253,8 253,8 9. 252,7 250,4 250,1 252,7 252,7 10. 251,4 250,8 250,3 252,4 251,4 11. 253,3 253,7 252,5 253,3 253,3 12. 254,2 252,1 253,5 254,2 254,2 13. 254,7 250,1 253,6 254,7 254,7 14. 251,9 250,4 252,1 251,9 251,9 15. 252,1 252,4 251,4 252,1 252,1 16. 250,9 253,5 250,7 251,9 250,9 17. 250,8 252,7 250,2 250,8 250,8 18. 254,3 250,6 253,3 254,3 252,3 19. 254,1 250,9 252,7 254,1 252,1 20. 253,4 251,2 250 253,4 253,4 X 252,65 251,56 251,81 252,61 252,85
SD 1,320 1,102 1,265 1,316 1,327 CV % 0,522 0,438 0,502 0,521 0,525
5% 250 ± 12,63
250 ± 12,58
250 ± 12,59
250 ± 12,63
250 ± 12,64
10% 250 ± 25,26
250 ± 25,16
250 ± 25,18
250 ± 25,26
250 ± 25,28
Perhitungan keseragaman bobot tablet kaptopril menurut Farmakope Indonesia
Edisi III :
1. Formula III
Bobot rata – rata 20 tablet = 251,56 mg
a. Untuk penyimpangan 5% = 100
5 x 251,56 mg = 12,58 mg
Jadi berat tablet kaptopril = 250 mg ± 12,58 mg (237,42 mg -262,58 mg)
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b. Untuk penyimpangan 10% = 10010 x 251,56 mg = 25,16 mg
Jadi berat tablet kaptopril = 250 mg ± 25,16 mg (224,84 mg-275,16
mg)
2. Formula V
Bobot rata – rata 20 tablet = 251,81 mg
a. Untuk penyimpangan 5% = 100
5 x 251,81 mg = 12,59 mg
Jadi berat tablet kaptopril = 250 mg ± 12,59 mg (237,41 mg -262,59 mg)
b. Untuk penyimpangan 10% = 10010 x 251,81 mg = 25,18 mg
Jadi berat tablet kaptopril = 250 mg ± 25,16 mg (224,82 mg-275,18
mg)
3. Formula I
Bobot rata – rata 20 tablet = 252,26 mg
a. Untuk penyimpangan 5% = 100
5 x 252,26 mg = 12,63 mg
Jadi berat tablet kaptopril = 250 mg ± 12,63 mg (237,37 mg -262,63 mg)
b. Untuk penyimpangan 10% = 10010 x 252,26 mg = 25,26 mg
Jadi berat tablet kaptopril = 250 mg ± 25,26 mg (224,74mg-275,26
mg)
2) Kekerasan tablet (Kg)
Replikasi Kekerasan tablet (Kg) F I F III F V F II F IV
1 8,08 9,96 8,11 8,55 9,15 2 8,56 10,05 9,51 9,08 8,46 3 8,10 8,52 10,25 8,92 10,44 4 9,19 8,41 10,96 9,34 8,98 5 9,50 8,70 10,21 8,90 8,94 X 8,69 9,13 9,81 8,95 9,19
SD 0,641 0,808 1,079 0,288 0,742
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3) Kerapuhan tablet (%)
Replikasi Kerapuhan tablet (%) F I F II F III F IV F V
1 0,832 0,208 0,244 0,373 0,228 2 0,029 0,211 0,142 0,423 0,123 3 0,105 0,737 0,026 0,504 0,044 X 0,322 0,385 0,137 0,433 0,132
SD 0,443 0,304 0,109 0,066 0,092
4) Waktu hancur tablet (menit)
Replikasi Waktu hancur tablet (menit) F I F III F V F II F IV
1 69 >70 >70 >70 >70 2 59 >70 >70 >70 >70 3 60 >70 >70 >70 >70 X 62,67 >70 >70 >70 >70
SD 5,507 - - - -
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Lampiran 5. Hasil Uji Disolusi Tablet
Contoh perhitungan uji disolusi
FORMULA III
Persamaan kurva baku Y = 0,3726 x + 0,0442 Satuan mg%
Replikasi 1 (251,2 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,101 1 0,1524 0 0,1524 2,73 15 0,324 1 0,7509 0,0001 0,751 13,45 30 0,523 1 1,285 0,0043 1,2893 23,09 60 0,363 2,5 2,139 0,0114 2,1504 38,51 120 0,512 2,5 3,1388 0,0233 3,1621 56,62 180 0,332 5 3,8621 0,0407 3,9028 69,89 240 0,376 5 4,4525 0,0622 4,5147 80,84 300 0,227 10 4,9061 0,0869 4,993 89,41 360 0,245 10 5,3892 0,1142 5,5033 98,55
1. Kadar (mg%) diperoleh dengan memasukkan serapan yang diperoleh pada
persamaan kurva baku (Y= 0,3726 x + 0,0442) yaitu serapan pada menit ke –
1 yaitu 0,101 sehingga diperoleh x = 0,1524 mg%, dikalikan dengan faktor
pengenceran x = 0,1524 mg%.
2. Pengambilan medium tiap selang waktu sebanyak 5,0 ml dan diganti dengan
volume medium yang sama, karena pada tiap pengambilan terjadi
pengurangan kadar dalam medium tersebut sehingga agar kadar dalam
medium dianggap tetap maka kadar tersebut dijadikan faktor koreksi menit ke
– 2 yang diperoleh dari = (5/900 x 0,1524) + 0 = 0,0001.
3. Kadar terkoreksi pada menit ke-2 diperoleh dari (0,0001+0,7509) = 0,751.
4. Kadar terdisolusi dalam % diperoleh dari kadar terkoreksi dibagi kadar awal
kaptopril dalam tablet x 100% = 2,73%
Kadar awal kaptopril = berat tablet percobaan dibagi berat tablet
sesungguhnya dikali zat aktif.
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Replikasi 2 (252,2 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,135 1 0,2437 0 0,2437 4,348 15 0,271 1 0,6087 0,0014 0,61 10,89 30 0,326 2,5 1,8908 0,0047 1,8955 33,82 60 0,376 2,5 2,2262 0,0152 2,2415 40 120 0,423 2,5 2,5416 0,0276 2,5692 45,84 180 0,287 5 3,2582 0,0417 3,2999 58,88 240 0,326 5 3,7815 0,0598 3,8414 68,54 300 0,209 10 4,423 0,0808 4,5038 80,36 360 0,249 10 5,4965 0,1054 5,6019 99,96
Replikasi 3 (252,5 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,117 1 0,1954 0 0,1954 3,482 15 0,233 1 0,5067 0,0011 0,5078 9,05 30 0,214 2,5 1,1393 0,0039 1,1432 20,37 60 0,315 2,5 1,817 0,0102 1,8272 32,56 120 0,519 2,5 3,1857 0,0203 3,206 57,14 180 0,275 5 3,0972 0,038 3,1352 55,87 240 0,367 5 4,3317 0,0552 4,387 78,18 300 0,218 10 4,6645 0,0793 4,7438 84,54 360 0,248 10 5,4697 0,1052 5,5749 99,35
FORMULA I
Replikasi 1 (251,6 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,323 1 0,7483 0 0,7483 13,38 15 0,677 1 1,6983 0,0042 1,7025 30,45 30 0,441 2,5 2,6624 0,0136 2,676 47,86 60 0,385 5 4,5733 0,0284 4,6017 82,3 120 0,413 5 4,949 0,0538 5,0028 89,48 180 0,217 10 4,6377 0,0813 4,719 84,4 240 0,269 10 6,0333 0,107 6,1403 109,8 300 0,326 10 7,5631 0,1406 7,7036 137,8 360 0,21 20 8,8996 0,1826 9,0822 162,4
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Replikasi 2 (251,9 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,191 1 0,394 0 0,394 7,038 15 0,239 2,5 1,307 0,0042 1,3112 23,42 30 0,3 2,5 1,7163 0,0114 1,7277 30,86 60 0,393 2,5 2,3403 0,021 2,3613 42,18 120 0,606 2,5 3,7695 0,034 3,8034 67,94 180 0,368 5 4,3451 0,0549 4,4 78,6 240 0,477 5 5,8078 0,079 5,8869 105,2 300 0,329 10 7,6436 0,1113 7,7549 138,5 360 0,386 10 9,1734 0,1538 9,3271 166,6
Replikasi 3 (252,1 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,108 1 0,1712 0 0,1712 3,056 15 0,199 2,5 1,0386 0,0042 1,0428 18,61 30 0,246 2,5 1,354 0,0099 1,3639 24,35 60 0,391 2,5 2,3269 0,0174 2,3443 41,85 120 0,568 2,5 3,5145 0,0304 3,5449 63,28 180 0,389 5 4,6269 0,0499 4,6768 83,48 240 0,457 5 5,5395 0,0756 5,6151 100,2 300 0,346 10 8,0998 0,1064 8,2062 146,5 360 0,399 10 9,5223 0,1514 9,6737 172,7
FORMULA II Replikasi 1(252,6 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,102 1 0,155 0 0,155 2,764 15 0,216 1 0,461 0,009 0,47 8,368 30 0,245 2,5 1,347 0,011 1,358 24,2 60 0,308 2,5 1,77 0,019 1,789 31,87 120 0,408 2,5 2,441 0,028 2,469 44 180 0,376 5 4,452 0,042 4,495 80,07 240 0,413 5 4,949 0,067 5,016 89,36 300 0,314 10 7,241 0,094 7,335 130,7 360 0,354 10 8,315 0,135 8,449 150,5
68
Replikasi 2 (255,15 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,216 1 0,461 0 0,461 9,222 15 0,287 1 0,652 0,009 0,66 11,64 30 0,239 2,5 1,307 0,012 1,319 23,27 60 0,354 2,5 2,079 0,019 2,098 37 120 0,427 2,5 2,568 0,031 2,599 45,85 180 0,317 5 3,661 0,045 3,706 65,36 240 0,411 5 4,922 0,066 4,988 87,97 300 0,325 10 7,536 0,093 7,629 134,6 360 0,375 10 8,878 0,135 9,013 159
Replikasi 3 (255,05 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,179 1 0,362 0 0,362 6,383 15 0,226 1 0,488 0,009 0,497 8,761 30 0,245 2,5 1,347 0,011 1,359 23,97 60 0,358 2,5 2,105 0,019 2,124 37,48 120 0,413 2,5 2,475 0,031 2,505 44,2 180 0,376 5 4,452 0,044 4,497 79,34 240 0,413 5 4,949 0,069 5,018 88,54 300 0,306 10 7,026 0,096 7,123 125,7 360 0,338 10 7,885 0,136 8,021 141,5
FORMULA IV
Replikasi 1 (251,09 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,095 1 0,1363 0 0,1363 2,443 15 0,277 1 0,6248 0,0008 0,6256 11,21 30 0,579 1 1,4353 0,0042 1,4395 25,8 60 0,235 5 2,5604 0,0122 2,5726 46,11 120 0,327 5 3,795 0,0264 3,8214 68,49 180 0,459 5 5,5663 0,0475 5,6138 100,6 240 0,243 10 5,3355 0,0784 5,4139 97,03 300 0,309 10 7,1068 0,1081 7,2149 129,3 360 0,346 10 8,0998 0,1476 8,2474 147,8
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Replikasi 2 (248,12 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,103 1 0,1578 0 0,1578 2,862 15 0,238 1 0,5201 0,0008 0,5209 9,447 30 0,544 1 1,3414 0,0036 1,345 24,39 60 0,233 5 2,5335 0,0111 2,5446 46,15 120 0,332 5 3,8621 0,0252 3,8872 70,5 180 0,478 5 5,8213 0,0466 5,8679 106,4 240 0,263 10 5,8722 0,079 5,9512 107,9 300 0,298 10 6,8116 0,1116 6,9232 125,6 360 0,32 10 7,402 0,1494 7,5515 137
Replikasi 3 (260,99 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,087 1 0,1149 0 0,1149 1,981 15 0,243 1 0,5335 0,0008 0,5343 9,212 30 0,532 1 1,3092 0,0037 1,3129 22,64 60 0,266 5 2,9764 0,011 2,9874 51,51 120 0,416 5 4,9893 0,0275 5,0168 86,5 180 0,448 5 5,4187 0,0552 5,4739 94,38 240 0,28 10 6,3285 0,0854 6,4139 110,6 300 0,302 10 6,9189 0,1205 7,0395 121,4 360 0,372 10 8,7976 0,1589 8,9566 154,4
FORMULA V
Replikasi 1 (252,3 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,17 1 0,3376 0 0,3376 6,022 15 0,247 1 0,5443 0,0042 0,5484 9,781 30 0,298 1 0,6812 0,0072 0,6883 12,28 60 0,324 1 0,7509 0,011 0,7619 13,59 120 0,286 2,5 1,6224 0,0151 1,6375 29,2 180 0,349 2,5 2,0451 0,0242 2,0692 36,9 240 0,398 2,5 2,3739 0,0355 2,4094 42,97 300 0,455 2,5 2,7563 0,0487 2,805 50,03 360 0,303 5 3,4729 0,064 3,5369 63,08
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Replikasi 2 (251,8 mg) Menit Abs fp Kadar
(mg%) Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,143 1 0,2652 0 0,2652 4,739 15 0,229 1 0,496 0,0042 0,5001 8,939 30 0,306 1 0,7026 0,0069 0,7095 12,68 60 0,345 2,5 2,0183 0,0108 2,0291 36,27 120 0,425 2,5 2,555 0,022 2,577 46,06 180 0,487 2,5 2,971 0,0362 3,0072 53,75 240 0,298 5 3,4058 0,0527 3,4585 61,81 300 0,327 5 3,795 0,0716 3,8666 69,11 360 0,386 5 4,5867 0,0927 4,6794 83,64
Replikasi 3 (252,7 mg)
Menit Abs fp Kadar (mg%)
Faktor koreksi
Kadar terkoreksi
(mg%)
Kadar terdisolusi
(%) 5 0,123 1 0,2115 0 0,2115 3,766 15 0,201 1 0,4208 0,0042 0,425 7,569 30 0,214 1 0,4557 0,0065 0,4622 8,232 60 0,333 1 0,7751 0,009 0,7841 13,96 120 0,399 1 0,9522 0,0133 0,9656 17,2 180 0,432 2,5 2,602 0,0186 2,6206 46,67 240 0,499 2,5 3,0515 0,0331 3,0846 54,94 300 0,324 5 3,7547 0,05 3,8047 67,76 360 0,368 5 4,3451 0,0709 4,416 78,65
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Lampiran 6. Hasil Anova Kecepatan Alir
Response 1 kecepatan alir (g/dtk) ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III]
Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 0,0985886 2 0,049294 58,28716 0.0169 significant
Linear Mixture 0,09801 1 0,09801 115,8902 0.0085 AB 0,0005786 1 0,000579 0,684122 0.4951
Residual 0,0016914 2 0,000846 Cor Total 0,10028 4
The Model F‐value of 58.29 implies the model is significant. There is only a 1.69% chance that a "Model F‐Value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case Linear Mixture Components are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.
Std. Dev. 0,0290812 R‐Squared 0,983133 Mean 9,962 Adj R‐Squared 0,966266 C.V. % 0,291921 Pred R‐Squared 0,905985 PRESS 0,0094278 Adeq Precision 17,57955
The "Pred R‐Squared" of 0.9060 is in reasonable agreement with the "Adj R‐Squared" of 0.9663.
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 17.580 indicates an adequate signal. This model can be used to navigate the design space.
Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A‐Na CMC 9,7768571 1 0,027369 9,659098 9,894616 1,660714286
B‐Avicel PH 102 10,172857 1 0,027369 10,0551 10,29062 1,660714286AB ‐0,1028571 1 0,124356 ‐0,63792 0,432205 2,428571429
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Final Equation in Terms of L_Pseudo Components:
kecepatan alir (g/dtk) =
9,78 * A 10,17 * B
‐0,10 * A* B
Final Equation in Terms of Real Components:
kecepatan alir (g/dtk) =
9,6520804 * Na CMC 10,46058 * Avicel PH 102 ‐0,42875 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
kecepatan alir (g/dtk) =
0,0492453 * Na CMC 0,0533703 * Avicel PH 102 ‐1,116E‐05 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 7. Hasil Anova Pengetapan
Response 2 pengetapan (%) ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III]
Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 3,4003886 2 1,700194 0,975976 0.5061 not significant
Linear Mixture 3,20356 1 3,20356 1,838964 0.3079 AB 0,1968286 1 0,196829 0,112987 0.7688
Residual 3,4840914 2 1,742046 Cor Total 6,88448 4
The "Model F‐value" of 0.98 implies the model is not significant relative to the noise. There is a 50.61 % chance that a "Model F‐value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case there are no significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.
Std. Dev. 1,3198658 R‐Squared 0,493921 Mean 8,332 Adj R‐Squared ‐0,01216 C.V. % 15,840924 Pred R‐Squared ‐6,64839 PRESS 52,655216 Adeq Precision 2,214476
A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 2.21 indicates an inadequate signal and we should not use this model to navigate the design space.
Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A‐Na CMC 9,7011429 1 1,242157 4,356571 15,04571 1,660714286
B‐Avicel PH 102 7,4371429 1 1,242157 2,092571 12,78171 1,660714286AB ‐1,8971429 1 5,643984 ‐26,1812 22,38696 2,428571429
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Final Equation in Terms of L_Pseudo Components:
Pengetapan(%) = 9,70 * A 7,44 * B ‐1,90 * A* B
Final Equation in Terms of Real Components:
pengetapan(%) = 12,383038 * Na CMC 7,7607044 * Avicel PH 102 ‐7,9080556 * NaCMC * Avicel PH 102
Final Equation in Terms of Actual Components:
pengetapan(%) = 0,0631788 * Na CMC 0,0395954 * Avicel PH 102 ‐0,0002059 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 8. Hasil Anova Keseragaman Bobot
Response 3 CV keseragaman bobot (%) ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III]
Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 0,0012636 2 0,000632 0,30658 0.7654 not significant
Linear Mixture 0,0001296 1 0,00013 0,062888 0.8254 AB 0,001134 1 0,001134 0,550272 0.5355
Residual 0,0041216 2 0,002061 Cor Total 0,0053852 4
The "Model F‐value" of 0.31 implies the model is not significant relative to the noise. There is a 76.54 % chance that a "Model F‐value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case there are no significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.
Std. Dev. 0,045396 R‐Squared 0,234643 Mean 0,5016 Adj R‐Squared ‐0,53071 C.V. % 9,0502462 Pred R‐Squared ‐3,22335 PRESS 0,0227436 Adeq Precision 1,228543
A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 1.23 indicates an inadequate signal and we should not use this model to navigate the design space.
Component
Coefficient Estimate
df
Standard Error
95% CI Low
95% CI High
VIF
A‐Na CMC 0,5124 1 0,042723 0,328576 0,696224 1,660714286B‐Avicel PH 102 0,5268 1 0,042723 0,342976 0,710624 1,660714286
AB ‐0,144 1 0,194122 ‐0,97924 0,691238 2,428571429
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Final Equation in Terms of L_Pseudo Components:
CV keseragaman bobot (%) = 0,51 * A 0,53 * B ‐0,14 * A * B
Final Equation in Terms of Real Components:
CV keseragaman bobot (%) = 0,6189625 * Na CMC 0,6483625 * Avicel PH 102 ‐0,60025 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
CV keseragaman bobot (%) = 0,003158 * Na CMC 0,003308 * Avicel PH 102
‐1,563E‐05 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 9. Hasil Anova Kekerasan Tablet
Response 4 kekerasan tablet (Kg) ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III] Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 0,6407543 2 0,320377 13,24811 0.0702 not significant Linear Mixture 0,61504 1 0,61504 25,43289 0.0371 AB 0,0257143 1 0,025714 1,063327 0.4108 Residual 0,0483657 2 0,024183 Cor Total 0,68912 4
The Model F‐value of 13.25 implies there is a 7.02% chance that a "Model F‐Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case Linear Mixture Components are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.
Std. Dev. 0,1555084 R‐Squared 0,929815 Mean 9,154 Adj R‐Squared 0,859631 C.V. % 1,6988025 Pred R‐Squared ‐0,08733 PRESS 0,7493041 Adeq Precision 8,235353 A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 8.235 indicates an adequate signal. This model can be used to navigate the design space.
Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A‐Na CMC 9,7357143 1 0,146353 9,10601 10,36542 1,660714286B‐Avicel PH 102 8,7437143 1 0,146353 8,11401 9,373419 1,660714286AB ‐0,6857143 1 0,664982 ‐3,5469 2,175472 2,428571429
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Final Equation in Terms of L_Pseudo Components:
kekerasan tablet (Kg) = 9,7357143 * A 8,7437143 * B ‐0,6857143 * A * B
Final Equation in Terms of Real Components:
kekerasan tablet (Kg) = 10,795536 * Na CMC 8,7702024 * Avicel PH 102 ‐2,8583333 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
kekerasan tablet (Kg) = 0,0550793 * Na CMC 0,0447459 * Avicel PH 102 ‐7,44E‐05 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 10. Hasil Anova Kerapuhan Tablet
Response 5 kerapuhan tablet (%) ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III] Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 0,0207367 2 0,010368 0,358778 0.7360 not significant Linear Mixture 0,0183184 1 0,018318 0,633875 0.5094 AB 0,0024183 1 0,002418 0,08368 0.7996 Residual 0,0577981 2 0,028899 Cor Total 0,0785348 4
The "Model F‐value" of 0.36 implies the model is not significant relative to the noise. There is a 73.60 % chance that a "Model F‐value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case there are no significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. Std. Dev. 0,1699972 R‐Squared 0,264045Mean 0,2818 Adj R‐Squared ‐0,47191C.V. % 60,325489 Pred R‐Squared ‐3,028PRESS 0,3163383 Adeq Precision 1,300129
A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 1.30 indicates an inadequate signal and we should not use this model to navigate the design space. Component
CoefficientEstimate df
StandardError
95% CILow
95% CI High
VIF
A‐Na CMC 0,1699143 1 0,159988 ‐0,51846 0,858289 1,660714286B‐Avicel PH 102 0,3411143 1 0,159988 ‐0,34726 1,029489 1,660714286AB 0,2102857 1 0,726939 ‐2,91748 3,338051 2,428571429
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Final Equation in Terms of L_Pseudo Components:
kerapuhan tablet (%) =
0,1699143 * A 0,3411143 * B 0,2102857 * A * B
Final Equation in Terms of Real Components:
kerapuhan tablet (%) =
‐0,0858198 * Na CMC 0,2637135 * Avicel PH 102 0,8765556 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
kerapuhan tablet (%) =
‐0,0004379 * Na CMC 0,0013455 * Avicel PH 102 2,282E‐05 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 11. Hasil Anova Linieritas
Response 6 Linieritas ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III] Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 0,000197 2 9,85E‐05 0,814509 0.5511 not significant Linear Mixture 0,0001849 1 0,000185 1,529182 0.3417 AB 1,207E‐05 1 1,21E‐05 0,099835 0.7820 Residual 0,0002418 2 0,000121 Cor Total 0,0004388 4
The "Model F‐value" of 0.81 implies the model is not significant relative to the noise. There is a 55.11 % chance that a "Model F‐value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case there are no significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. Std. Dev. 0,0109961 R‐Squared 0,448887 Mean 0,9882 Adj R‐Squared ‐0,10223 C.V. % 1,1127407 Pred R‐Squared ‐4,55348 PRESS 0,0024369 Adeq Precision 2,019361
A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 2.02 indicates an inadequate signal and we should not use this model to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A‐Na CMC 0,9986571 1 0,010349 0,95413 1,043184 1,660714286B‐Avicel PH 102 0,9814571 1 0,010349 0,93693 1,025984 1,660714286AB ‐0,0148571 1 0,047021 ‐0,21717 0,187459 2,428571429
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Final Equation in Terms of L_Pseudo Components:
linieritas = 0,9986571 * A 0,9814571 * B ‐0,0148571 * A * B
Final Equation in Terms of Real Components:
linieritas = 1,0193838 * Na CMC 0,9842672 * Avicel PH 102 ‐0,0619306 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
linieritas = 0,0052009 * Na CMC 0,0050218 * Avicel PH 102 ‐1,612E‐06 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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Lampiran 12. Hasil Anova Kecepatan Disolusi
Response 7 Kecepatan Disolusi ANOVA for Quadratic Mixture Model *** Mixture Component Coding is L_Pseudo. *** Analysis of variance table [Partial sum of squares ‐ Type III] Sum of Mean F p‐value Source Squares df Square Value Prob > F Model 4023,0243 2 2011,512 1,065669 0.4841 not significant Linear Mixture 3800,9402 1 3800,94 2,013682 0.2917 AB 222,08411 1 222,0841 0,117657 0.7643 Residual 3775,1148 2 1887,557 Cor Total 7798,1391 4
The "Model F‐value" of 1.07 implies the model is not significant relative to the noise. There is a 48.41 % chance that a "Model F‐value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case there are no significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. Std. Dev. 43,446029 R‐Squared 0,515895Mean 138,334 Adj R‐Squared 0,031791
C.V. % PRESS
31,40661632681,608
Pred R‐Squared Adeq Precision
‐3,190952,317287
A negative "Pred R‐Squared" implies that the overall mean is a better predictor of your response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 2.32 indicates an inadequate signal and we should not use this model to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A‐Na CMC 91,376286 1 40,8881 ‐84,551 267,3036 1,660714286B‐Avicel PH 102 169,36029 1 40,8881 ‐6,567 345,2876 1,660714286AB 63,725714 1 185,783 ‐735,634 863,0856 2,428571429
Final Equation in Terms of L_Pseudo Components:
kecepatan
84
pelepasan obat
=
91,376286 * A 169,36029 * B 63,725714 * A * B
Final Equation in Terms of Real Components:
kecepatan pelepasan obat = 0,2824365 * Na CMC 159,49977 * Avicel PH 102 265,63444 * Na CMC * Avicel PH 102
Final Equation in Terms of Actual Components:
kecepatan pelepasan obat =
0,001441 * Na CMC 0,8137743 * Avicel PH 102 0,0069147 * Na CMC * Avicel PH 102
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node. In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the: 1) Normal probability plot of the studentized residuals to check for normality of residuals. 2) Studentized residuals versus predicted values to check for constant error. 3) Externally Studentized Residuals to look for outliers, i.e., influential values. 4) Box‐Cox plot for power transformations.
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