LAMPIRAN A HASIL PEMERIKSAAN STANDARISASI PARAMETRIK EKSTRAK ROSELLA A. PEMERIKSAAN ORGANOLEPTIS EKSTRAK ROSELLA. Khas rosella

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1 112 LAMPIRAN A HASIL PEMERIKSAAN STANDARISASI PARAMETRIK EKSTRAK ROSELLA A. PEMERIKSAAN ORGANOLEPTIS EKSTRAK ROSELLA Pemeriksaan Bentuk Warna Bau Rasa Ekstrak Rosella Serbuk Merah Khas rosella Asam B. HASIL PENENTUAN ph EKSTRAK ROSELLA C. HASIL PENENTUAN KADAR SARI LARUT AIR EKSTRAK ROSELLA Replikasi Replikasi Ekstrak Rosella I 3,21 II 3,22 III 3,21 X ± SD 3,21±0,005 Berat cawan(g) Berat ekstrak (g) Berat konstan % kadar 1 81,1772 5, , , ,2100 5, , , ,3394 5, , ,96 X ± SD 97,43±1,53 Contoh perhitungan standarisasi kadar sari larut air : Berat cawan sesudah dipanaskan = 81,1563 Berat cawan kosong = 80,

2 113 Berat ekstrak = 5,0039/5 = 1,0078 % kadar = berat pemanasan terakhir berat cawan kosong x 100% Berat ekstrak/5 % kadar = (81, ,1966) x100% 1,0078 = 95,89% D. HASIL PENENTUAN KADAR SARI LARUT ETANOL EKSTRAK ROSELLA Replikasi Berat cawan(g) Berat ekstrak (g) Berat konstan % kadar 1 57,8016 5, , , ,3881 5, , , ,9216 5, , ,72 X ± SD 29,57±0,27 Contoh perhitungan standarisasi kadar sari larut etanol : Berat cawan sesudah dipanaskan = 57,7713 Berat cawan kosong = 57,4786 Berat ekstrak = 5,0026/5 = 1,00052 % kadar = berat pemanasan terakhir berat cawan kosong x 100% Berat ekstrak/5 % kadar = (57, ,4786) x100% 1,00052 = 29,25% 113

3 114 LAMPIRAN B HASIL PEMERIKSAAN STANDARISASI NON PARAMETRIK EKSTRAK ROSELLA A. HASIL PENENTUAN KADAR ABU TOTAL EKSTRAK ROSELLA Rep Berat krus (g) Berat ekstrak (g) Berat krus+abu konstan (g) % kadar I 36,3608 2, ,4647 4,14 II 35,1912 2, ,2952 4,14 III 23,3239 2, ,4260 4,08 X ± SD 4,12±0,01 Contoh Perhitungan standarisasi kadar abu total : % kadar = berat abu konstan berat kurs x 100% Berat ekstrak % kadar = 36, ,3608 x 100% 2,5042 = 4,14 B. HASIL PENETAPAN KADAR ABU TIDAK LARUT ASAM EKSTRAK ROSELLA Replikasi Berat krus (g) Berat krus+abu konstan (g) Berat abu+hcl konstan (g) % kadar I 23, , ,2886 9,32 II 24, , , ,61 III 23, , , ,15 X ± SD 9,69±0,79 114

4 115 Contoh Perhitungan standarisasi kadar abu tidak larut asam: % kadar = berat abu setelah penambahan HCl x 100% Berat abu total berat kurs % kadar = 22, ,4802 x 100% 36, ,4802 = 9,32% C. HASIL PENETAPAN KADAR ABU LARUT AIR EKSTRAK ROSELLA Replikasi Berat krus (g) Berat krus+abu konstan (g) Berat abu+ aquadest (g) % kadar I 22, , ,4740 8,59 II 23, , ,8239 9,13 III 23, , ,2015 8,39 X ± SD 8,70±0,38 Contoh Perhitungan standarisasi kadar abu larut air : % kadar = berat abu setelah penambahan Aquadest x 100% Berat abu total berat kurs % kadar = 23, ,2703 x 100% 36, ,2705 = 8,59% D. HASIL PENENTUAN KADAR AIR Rep Berat cawan (g) Berat cawan+eks trak Berat cawan+ekstrak konstan (g) % kadar 1 75, , ,7560 0, , , ,6010 0, , , ,5668 0,19 x ± SD 0,11±0,07 115

5 116 Contoh Perhitungan standarisasi kadar air : % kadar = berat ekstrak berat ekstrak konstan x 100% Berat ekstrak % kadar = 10,0042 9,9974 x 100% 10,0042 = 0,06% E. HASIL PENENTUAN SUSUT PENGERINGAN Replikasi Ekstrak kering rosella I 4,00 II 4,10 III 4,17 x ± SD 4,09±0,08 116

6 117 LAMPIRAN C HASIL ANOVA SATU ARAH UJI MUTU FISIK PENYALUT A. VISKOSITAS viskositas penyalut Sum of Squares ANOVA df Square F Sig. Between Groups Within Groups Total Multiple Comparisons Tukey HSD (I) formula A B c D Difference Std. 95% Confidence Interval Upper (J) formula (I-J) Error Sig. Lower Bound Bound B c D * A c * D * A B * D * A * B * c * *. The mean difference is significant at the 0.05 level. 117

7 118 Karena F hitung = 249,635 > F tabel 0,05(3,8) = 4,07; maka H 0 ditolak dan ada perbedaan yang bermakna antar formula viskositas penyalut Tukey HSD Subset for alpha = 0.05 formula N B A c D Sig B. BERAT JENIS berat jenis Sum of Squares ANOVA df Square F Sig. Between Groups E3.000 Within Groups Total

8 119 berat jenis Tukey HSD (I) formula (J) formula Multiple Comparisons Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound formula A formula B * formula C formula D * formula B formula A * formula C * formula D * formula C formula A formula B * formula D * formula D formula A * formula B * formula C * *. The mean difference is significant at the 0.05 level. Karena F hitung = 0,0012 < F tabel 0,05(3,8) = 4,07; maka H 0 diterima dan tidak ada perbedaan yang bermakna antar formula 119

9 120 berat jenis Tukey HSD formula N formula D E0 Subset for alpha = formula C E0 formula A E0 formula B E0 Sig s for groups in homogeneous subsets are displayed. C. UJI ph LARUTAN PENYALUT ph NOVA Sum of Squares df Square F Sig. Between Groups Within Groups Total

10 121 ph Tukey HSD (I) formula (J) formula Multiple Comparisons Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound formula A formula B * formula C formula D * formula B formula A * formula C * formula D formula C formula A formula B * formula D * formula D formula A * formula B formula C * *. The mean difference is significant at the 0.05 level. Karena F hitung = 23,242 >F tabel 0,05(3,8) = 4,07; maka H 0 ditolak dan ada perbedaan yang bermakna antar formula 121

11 122 formula Tukey HSD N ph formula D formula B Subset for alpha = formula A formula C Sig s for groups in homogeneous subsets are displayed. 122

12 123 LAMPIRAN D HASIL UJI STATISTIK ANTAR BETS FORMULA TABLET SALUT ENTERIK EKSTRAK KELOPAK ROSELLA A. KESERAGAMAN BOBOT A Paired Samples Test Paired Differences Pair 1 A - A Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. 123

13 124 B Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 B - B Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. C Paired Samples Test Paired Differences 95% Confidence Interval of the Std. Sig. Difference Std. Error (2- Deviation Lower Upper t df tailed) Pair 1 C - C Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. 124

14 125 D Pair 1 D - D Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. B. WAKTU HANCUR A paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed ) Pair 1 A - A Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. 125

15 126 B Paired Samples Test Pair 1 B - B Paired Differences t df Std. Std. Error Deviation 95% Confidence Interval of the Difference Lower Upper Sig. (2- tailed) Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. C Paired Samples Test Paired Differences Pair 1 Formul a C - Formul a C Std. Std. Error Deviation 95% Confidence Interval of the Difference Lower Upper Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. t df Sig. (2- tailed)

16 127 D Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 D - D Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. C. KEKERASAN A Paired Samples Test Std. Deviation Paired Differences Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- taile d) Pair 1 A - A E Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. 127

17 128 B Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 B - B Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets C Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 C - C Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets 128

18 129 D Paired Samples Test Paired Differences Std. Std. Error Deviation 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 D - D Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets D. TAMPILAN VISUAL A Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 A - A Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets. 129

19 130 B Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 B - B Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar beta Hipotesa pengujian : C Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 C - C T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets 130

20 131 D Paired Samples Test Paired Differences Std. Deviation Std. Error 95% Confidence Interval of the Difference Lower Upper t df Sig. (2- tailed) Pair 1 D - D Hipotesa pengujian : T hitung < T tabel (0,05) sehingga tidak ada perbedaan yang bermakna antar bets 131

21 132 LAMPIRAN E HASIL ANOVA SATU ARAH UJI TABLET SALUT ENTERIK EKSTRAK KELOPAK BUNGA ROSELLA A. KESERAGAMAN BOBOT keseragaman bobot ANOVA Sum of Squares df Square F Sig. Between Groups Within Groups Total Tukey HSD (I) formula (J) formula Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound A B C Fotmula D B A C Fotmula D C A B Fotmula D Fotmula D A B C Karena F hitung = 2,718 < F tabel 0,05(3,4) = 6,59; maka H 0 diterima dan tidak ada perbedaan yang bermakna antar formula. 132

22 133 B. TAMPILAN VISUAL Uji tampilan visual tablet salut enterik ANOVA Sum of Squares df Square F Sig. Between Groups Within Groups Total Tukey HSD (I) formula (J) formula Differenc e (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound formula a formula b formula c formula d formula b formula a formula c * formula d formula c formula a formula b * formula d formula d formula a formula b formula c *. The mean difference is significant at the 0.05 level. Karena F hitung = 9,904 >F tabel 0,05(3,4) = 6,59; maka H 0 ditolak dan ada perbedaan yang bermakna antar formula. 133

23 134 Tukey HSD Subset for alpha = 0.05 formula N 1 2 formula b formula d formula a formula c Sig s for groups in homogeneous subsets are displayed. C. KEKERASAN ANOVA Sum of Squares df Square F Sig. Between Groups Within Groups Total

24 135 Tukey HSD (I) formula (J) formula Differenc e (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound formula a formula b * formula c * formula d * formula b formula a * formula c * formula d * formula c formula a * formula b * formula d * formula d formula a * formula b * formula c * *. The mean difference is significant at the 0.05 level. Karena F hitung = 88,045 > F tabel 0,05(3,7) = 4,35; maka H 0 ditolak dan ada perbedaan yang bermakna antar formula Tukey HSD Subset for alpha = 0.05 formula N formula c formula d formula a formula b Sig s for groups in homogeneous subsets are displayed. 135

25 136 D. WAKTU HANCUR X1 ANOVA Sum of Squares df Square F Sig. Between Groups Within Groups Total Tukey HSD (I) formula (J) formula Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound formula a formula b formula c formula d formula b formula a formula c * formula d formula c formula a formula b * formula d formula d formula a formula b formula c Karena F hitung = 5,927 > F tabel 0,05(3,7) = 4,35; maka H 0 ditolak dan ada perbedaan yang bermakna antar formula 136

26 137 Tukey HSD formula N formula b Subset for alpha = formula d formula a formula c Sig s for groups in homogeneous subsets are displayed. 137

27 LAMPIRAN F HASIL ANOVA SATU ARAH UJI WAKTU HANCUR PADA PROGRAM DESIGN EXPERT Response 1 waktu hancur ANOVA for selected factorial model Analysis of variance table [Partial sum of squares - Type III] Sum of F p-value Source Squares df Square Value Prob > F Model < significant A-HPMCP < B-Gliserol AB Pure Error Cor Total The Model F-value of implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A, B, AB are significant model terms. Values greater than 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. 139

28 Std. Dev R-Squared Adj R-Squared C.V. % 3.98 Pred R-Squared PRESS 2.87 Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI FactorEstimate df Error Low High VIF Intercept A-HPMCP B-Gliserol AB Final Equation in Terms of Coded Factors: waktu hancur = * A 140

29 -1.34 * B * A * B Final Equation in Terms of Actual Factors: waktu hancur = * HPMCP * Gliserol * HPMCP * Gliserol 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. If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. 141

30 LAMPIRAN G HASIL ANOVA SATU ARAH UJI KEKERASAN PADA PROGRAM DESIGN EXPERT Response 2 kekerasan ANOVA for selected factorial model Analysis of variance table [Partial sum of squares - Type III] Sum of F p-value Source Squares df Square Value Prob > F Model significant A-HPMCP B-Gliserol AB4.512E E Pure Error E-003 Cor Total The Model F-value of implies the model is significant. There is only a 0.04% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A, B are significant model terms. Values greater than 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. 142

31 Std. Dev R-Squared Adj R-Squared C.V. % 0.99 Pred R-Squared PRESS Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI FactorEstimate df Error Low High VIF Intercept A-HPMCP B-Gliserol AB Final Equation in Terms of Coded Factors: kekerasan = * A 143

32 -0.38 * B * A * B Final Equation in Terms of Actual Factors: kekerasan = * HPMCP * Gliserol * HPMCP * Gliserol 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. If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. Anova Respon Keseragaman Bobot Program Design Expert 144

33 LAMPIRAN H HASIL ANOVA SATU ARAH UJI KESERAGAMAN BOBOT PADA PROGRAM DESIGN EXPERT Response 3 keseragaman bobot ANOVA for selected factorial model Analysis of variance table [Partial sum of squares - Type III] Sum of F p-value Source Squares df Square Value Prob > F Model E not significant A-HPMCP B-Gliserol 1.125E E AB9.113E E Pure Error E-003 Cor Total The "Model F-value" of 2.72 implies the model is not significant relative to the noise. There is a % chance that a "Model F-value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case there are no significant model terms. Values greater than 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. 145

34 Std. Dev R-Squared Adj R-Squared C.V. % Pred R-Squared PRESS Adeq Precision 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 3.97 indicates an inadequate signal and we should not use this model to navigate the design space. Coefficient Standard 95% CI 95% CI FactorEstimate df Error Low High VIF Intercept A-HPMCP E B-Gliserol E AB Final Equation in Terms of Coded Factors: keseragaman bobot = * A 146

35 -3.750E-003 * B * A * B Final Equation in Terms of Actual Factors: keseragaman bobot = * HPMCP E-003 * Gliserol * HPMCP * Gliserol 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. If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. 147

36 LAMPIRAN I HASIL ANOVA SATU ARAH UJI TAMPILAN VISUAL PADA PROGRAM DESIGN EXPERT Response 4 visual ANOVA for selected factorial model Analysis of variance table [Partial sum of squares - Type III] Sum of F p-value Source Squares df Square Value Prob > F Model significant A-HPMCP B-Gliserol AB Pure Error Cor Total The Model F-value of 9.90 implies the model is significant. There is only a 2.53% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A are significant model terms. Values greater than 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. 148

37 Std. Dev R-Squared Adj R-Squared C.V. % 0.38 Pred R-Squared PRESS 2.22 Adeq Precision The "Pred R-Squared" of is not as close to the "Adj R-Squared" of as one might normally expect. This may indicate a large block effect or a possible problem with your model and/or data. Things to consider are model reduction, response transformation, outliers, etc. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI FactorEstimate df Error Low High VIF Intercept A-HPMCP B-Gliserol AB Final Equation in Terms of Coded Factors: visual = * A 149

38 +0.25 * B * A * B Final Equation in Terms of Actual Factors: visual = * HPMCP * Gliserol * HPMCP * Gliserol 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. If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon. 150

39 LAMPIRAN J RANGKUMAN HASIL PREDIKSI BERDASARKAN PROGRAM DESIGN EXPERT Number HPMCP gliserol waktu hancur kekerasan keseragaman bobot tampilan visual

40 LAMPIRAN K CONTOH PERHITUNGAN BAHAN PENYALUT - Jumlah tablet inti = 900 tablet - W total tablet inti = 265,7 gram - W HPMCP (dengan pertambahan bobot 4%) = 4/100 x 265,7 = 10,628 gram Vol pelarut campuran dengan konsentrasi HPMCP 9,75% = 100/9,75 x 10,628 = 109,0051 ml - Jumlah plastisaiser = 0,52/200 x 10,628 = 0,0553 gram - BJ plastisaiser = 1,26 g/ml - Volume plastisai - Jumlah talk (4%) = 4/100 x 10,628 = 0,4251 gram - % pelarut campuran = 109,0051 (10,628 +0, ,4251) = 97,8967 ml Maka untuk 900 tablet dibutuhkan HPMCP sebanyak 10,628 gram, Gliserol dan pelarut campuran 97,8967 ml. 152

41 LAMPIRAN L CONTOH PERHITUNGAN KONVERSI DOSIS EKSTRAK KELOPAK BUNGA ROSELLA Perbandingan ekstrak jadi natura : pengisi (7 : 1) Dosis acuan 250 mg ekstrak terstandarisasi (Hudson, 2011). Berat tablet 300 mg 153 Perhitungan konversi : 8/7 x 150 = 171,42 mg Jadi berat ekstrak dari PT. Natura Laboratoria yang harus ditimbang untuk jumlah per tablet yaitu = 171,42 mg 153

42 LAMPIRAN M SERTIFIKAT ANALISIS EKSTRAK KERING ROSELLA dari PT. NATURA

43 LAMPIRAN N SERTIFIKAT ANALISIS KALSIUM FOSFAT DIBASIK ANHIDRAT

44 LAMPIRAN O TABEL UJI F

45 LAMPIRAN P TABEL UJI T