73 LAMPIRAN A HASIL UJI STANDARISASI Hasil Perhitungan Penetapan Susut Pengeringan Simplisia Replikasi Hasil Susut Pengeringan 1 9,6 2 9,6 3 9,6 Rata- rata 9,6 Hasil Perhitungan Penetapan Susut Pengeringan Ekstrak Kering Replikasi Hasil Susut Pengeringan 1 7,4 2 7,4 3 7,4 Rata- rata 7,4 Hasil Perhitungan Penetapan Kadar Abu Total Simplisia No W (krus W W % Rata-Rata kosong) (Bahan) (krus+ abu) Kadar (%) (gram) (gram) (gram) abu 1 21,0625 2,0073 21,2282 8,2548 2 21,1545 2,0047 21,3224 8,3573 8,28 3 21,0875 2,0092 21,2524 8,2072 1. Kadar abu = (berat krus + ekstrak) - berat krus kosong 100% Berat ekstrak = 21,2282 – 21,0625 100 % 2,0073 = 8,2548 %
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73
LAMPIRAN A
HASIL UJI STANDARISASI
Hasil Perhitungan Penetapan Susut Pengeringan Simplisia
Replikasi Hasil Susut Pengeringan
1 9,6 2 9,6 3 9,6
Rata- rata 9,6
Hasil Perhitungan Penetapan Susut Pengeringan Ekstrak Kering
Replikasi Hasil Susut Pengeringan
1 7,4 2 7,4 3 7,4
Rata- rata 7,4
Hasil Perhitungan Penetapan Kadar Abu Total Simplisia
The Model F-value of 63660000.00 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 0.0500 indicate model terms are significant. In this case Linear Mixture Components, AB, AC, BC, ABC 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.
98
Std. Dev. 0.000 R-Squared
Mean7.21 Adj R-Squared 1.0000
C.V. % 0.000 Pred R-Squared
PRESS N/A Adeq Precision
Case(s) with leverage of 1.0000: Pred R-Squared and PRESS statistic
not defined
Component Coefficient Estimate
df Error Standard
95%
CI
Low
95%
CI
High
A-Mg Stearat B-Talk C-SSG AC AB BC ABC
7,44 7,93 8,07 -7,30 -7,46 -8,16 60,03
1 1 1 1 1 1 1
1.31 1.31 1.31 1.50 1.50 1.50 1.61
Final Equation in Terms of L_Pseudo Components:
(Kekerasan Tablet)1 = +7.44 * A +7.93 * B +8.07 * C -7.30 * A * B -7.46 * A * C -8.16 * B * C +60.03 * A * B * C
(Kekerasan Tablet)1 = +7.44000 * Mg Stearat +7.93000 * Talk +8.07000 * SSG -7.30000 * Mg Stearat * Talk -7.46000 * Mg Stearat * SSG -8.16000 * Talk * SSG +60.03000 * Mg Stearat * Talk * SSG 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.
100
LAMPIRAN J
HASIL ANOVA UJI KERAPUHAN PADA PROGRAM DESIGN
EXPERT
Response 2 Kerapuhan Tablet
ANOVA for Special Cubic Mixture Model
*** Mixture Component Coding is L_Pseudo. ***
Analysis of variance table [Partial sum of squares - Type III]
Final Equation in Terms of Actual Components: Kerapuhan Tablet = +0.56000 * Mg Stearat +0.52000 * Talk +0.51000 * SSG +0.72000 * Mg Stearat * Talk +0.98000 * Mg Stearat * SSG +0.94000 * Talk * SSG -7.65000 * Mg Stearat * Talk * SSG 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.
103
LAMPIRAN K
HASIL ANOVA UJI WAKTU HANCUR PADA PROGRAM DESIGN
EXPERT
Response 3 Waktu Hancur Tablet
ANOVA for Special Cubic Mixture Model
*** Mixture Component Coding is L_Pseudo. ***
Analysis of variance table [Partial sum of squares - Type III]
The Model F-value of 63660000.00 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 0.0500 indicate model terms are
significant. In this case Linear Mixture Components, AB, AC, BC, ABC
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.
104
Std. Dev. 0.000 R-Squared
Mean10.87 Adj R-Squared 1.0000
C.V. % 0.000 Pred R-Squared
PRESS N/A Adeq Precision
Case(s) with leverage of 1.0000: Pred R-Squared and PRESS statistic
not defined
Final Equation in Terms of L_Pseudo Components:
Waktu Hancur Tablet = +10.97 * A +11.47 * B +12.54 * C -8.40 * A * B -9.82 * A * C -11.86 * B * C +78.90 * A * B * C
Component Coefficien
t Estimate
df Standard Error
95% CI
Low 95% CI
High VIF
A-Mg Stearat B-Talk C-SSG AC AB BC ABC
10.97 78.90 12.54 -8.40 9.82 -11.86 11.47
1 1 1 1 1 1 1
1.31 1.31 1.31 1.50 1.50 1.50 1.61
105
Final Equation in Terms of Real Components: Waktu Hancur Tablet = +10.97000 * Mg Stearat +11.47000 * Talk +12.54000 * SSG -8.40000 * Mg Stearat * Talk -9.82000 * Mg Stearat * SSG -11.86000 * Talk * SSG +78.90000 * Mg Stearat * Talk * SSG
Final Equation in Terms of Actual Components: Waktu Hancur Tablet = +10.97000 * Mg Stearat +11.47000 * Talk +12.54000 * SSG -8.40000 * Mg Stearat * Talk -9.82000 * Mg Stearat * SSG -11.86000 * Talk * SSG
+78.90000 * Mg Stearat * Talk * SSG
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.