BAB V. KESIMPULAN DAN SARAN
Lampiran 11. Perhitungan menggunakan program R versi 3.1.2
a. Viskositas > show(viskositas) 1 a b ab 1 280 460 260 420 2 285 475 265 400 3 290 480 255 415 1) Viskositas formula 1 > shapiro.test(viskositas$"1") Shapiro-Wilk normality test data: viskositas$"1"
W = 1, p-value = 1 2) Viskositas formula a
> shapiro.test(viskositas$"a") Shapiro-Wilk normality test data: viskositas$a
W = 0.9231, p-value = 0.4633 3) Viskositas formula b
> shapiro.test(viskositas$"b") Shapiro-Wilk normality test data: viskositas$b
W = 1, p-value = 1 4) Viskositas formula ab
> shapiro.test(viskositas$"ab") Shapiro-Wilk normality test data: viskositas$ab
W = 0.9231, p-value = 0.4633
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data viskositas normal.
b. Daya Sebar > show(dayasebar) 1 a b ab 1 5.025 4.325 5.100 4.450 2 5.000 4.200 5.125 4.475 3 4.975 4.300 5.150 4.525 1) Daya sebar formula 1
> shapiro.test(dayasebar$"1") Shapiro-Wilk normality test data: dayasebar$"1"
W = 1, p-value = 1 2) Daya sebar formula a
> shapiro.test(dayasebar$"a") Shapiro-Wilk normality test data: dayasebar$a
W = 0.8929, p-value = 0.3631
3) Daya sebar formula b
> shapiro.test(dayasebar$"b") Shapiro-Wilk normality test data: dayasebar$b
W = 1, p-value = 1 4) Daya sebar formula ab
> shapiro.test(dayasebar$"ab") Shapiro-Wilk normality test data: dayasebar$ablv
W = 0.9643, p-value = 0.6369
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data daya sebar normal.
c. Stabilitas
1) Uji stabilitas formula 1 > geser
X48jam X1minggu X2minggu X3minggu X4minggu 1 280 280 250 280 290 2 285 290 270 285 305 3 290 285 280 295 300 > shapiro.test(geser$"48jam")
Shapiro-Wilk normality test data: geser$"48jam"
W = 1, p-value = 1
> shapiro.test(geser$"1minggu") Shapiro-Wilk normality test data: geser$"1minggu"
W = 0.75, p-value < 2.2e-16
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data stabilitas formula 1 normal.
2) Uji stabilitas formula a > fa
48jam 1minggu 2minggu 3minggu 4minggu 1 460 490 480 500 475 2 475 480 500 510 480 3 480 470 490 490 490 > shapiro.test(fa$"48jam")
Shapiro-Wilk normality test data: fa$"48jam"
W = 0.9231, p-value = 0.4633 > shapiro.test(fa$"4minggu")
Shapiro-Wilk normality test data: fa$"4minggu"
W = 0.9643, p-value = 0.6369
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data stabilitas formula a normal.
3) Uji stabilitas formula b > fb
48jam 1minggu 2minggu 3minggu 4minggu 1 260 270 270 275 280 2 265 275 260 270 275 3 255 280 265 265 270 > shapiro.test(fb$"48jam")
Shapiro-Wilk normality test data: fb$"48jam"
W = 1, p-value = 1
> shapiro.test(fb$"4minggu") Shapiro-Wilk normality test data: fb$"4minggu"
W = 1, p-value = 1
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data stabilitas formula b normal.
4) Uji stabilitas formula ab > fab1 48jam 1 2 3 4 1 420 420 400 410 425 2 400 410 410 430 415 3 415 430 415 420 420 > shapiro.test(fab1$"48jam")
Shapiro-Wilk normality test data: fab1$"48jam"
W = 0.9231, p-value = 0.4633 > shapiro.test(fab1$"4")
Shapiro-Wilk normality test data: fab1$"4"
W = 1, p-value = 1
Keterangan: Uji normalitas p>0,05 sehingga dapat dikatakan distribusi data stabilitas formula ab normal.
2. Uji Kesamaan Variansi > efek
CMCNa PG viskositas dayasebar formula 1 6.0 20 280 5.025 formula.1 2 6.0 20 285 5.000 formula.1 3 6.0 20 290 4.975 formula.1 4 7.5 20 460 4.325 formula.a 5 7.5 20 475 4.200 formula.a 6 7.5 20 480 4.300 formula.a 7 6.0 30 260 5.100 formula.b 8 6.0 30 265 5.125 formula.b 9 6.0 30 255 5.150 formula.b 10 7.5 30 420 4.450 formula.ab 11 7.5 30 400 4.475 formula.ab 12 7.5 30 415 4.525 formula.ab a. Viskositas >leveneTest(efek$viskositas,efek$formula,center=mean) Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F) group 3 1.5238 0.2813
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan data viskositas homogen.
b. Daya sebar
> leveneTest(efek$dayasebar,efek$formula,center=mean) Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F) group 3 2.2069 0.1649
8
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan data daya sebar homogen.
c. Stabilitas 1) Formula 1
> leveneTest(vektor$values~vektor$ind)
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F) group 4 0.875 0.5121 10
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan semua data pergeseran viskositas formula 1homogen.
2) Formula a
> leveneTest(vektor$values~vektor$ind)
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F) group 4 0.0455 0.9954 10
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan semua data pergeseran viskositas formula a homogen. 3) Formula b
> leveneTest(vektor$values~vektor$ind)
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F) group 4 0 1 10
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan semua data pergeseran viskositas formula b homogen. 4) Formula ab
> leveneTest(vektor$values~vektor$ind)
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F) group 4 0.2105 0.9267 10
Keterangan: Uji kesamaan variansi p>0,05 sehingga dapat dikatakan semua data pergeseran viskositas formula ab homogen.
3. Uji ANOVA a. Viskositas
> aov(vektor$values~vektor$ind) Call:
aov(formula = vektor$values ~ vektor$ind) Terms:
vektor$ind Residuals Sum of Squares 92189.58 533.33 Deg. of Freedom 3 8 Residual standard error: 8.164966 Estimated effects may be unbalanced
> anova=aov(vektor$values~vektor$ind) > anova
Call:
aov(formula = vektor$values ~ vektor$ind) Terms:
vektor$ind Residuals Sum of Squares 92189.58 533.33 Deg. of Freedom 3 8 Residual standard error: 8.164966 Estimated effects may be unbalanced > summary(anova)
Df Sum Sq Mean Sq F value Pr(>F) vektor$ind 3 92190 30730 460.9 2.69e-09 *** Residuals 8 533 67 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 b. Daya sebar > aov(vektor$values~vektor$ind) Call:
aov(formula = vektor$values ~ vektor$ind) Terms:
vektor$ind Residuals Sum of Squares 1.4893750 0.0141667 Deg. of Freedom 3 8 Residual standard error: 0.04208127 Estimated effects may be unbalanced > anova=aov(vektor$values~vektor$ind) > anova
Call:
aov(formula = vektor$values ~ vektor$ind) Terms:
vektor$ind Residuals Sum of Squares 1.4893750 0.0141667 Deg. of Freedom 3 8 Residual standard error: 0.04208127 Estimated effects may be unbalanced > summary(anova)
Df Sum Sq Mean Sq F value Pr(>F) vektor$ind 3 1.4894 0.4965 280.4 1.93e-08 ***
Residuals 8 0.0142 0.0018 ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
4. Uji nilai efek persamaan a. Viskositas
> aov(viskositas~CMCNa*PG,data=efek) Call:
aov(formula = viskositas ~ CMCNa * PG, data = efek)
Terms:
CMCNa PG CMCNa:PG Residuals Sum of Squares 85852.08 5418.75 918.75 533.33 Deg. of Freedom 1 1 1 8 Residual standard error: 8.164966
Estimated effects may be unbalanced
> summary.lm(aov(viskositas~CMCNa*PG,data=efek)) Call:
aov(formula = viskositas ~ CMCNa * PG, data = efek) Residuals:
Min 1Q Median 3Q Max -11.667 -5.000 1.667 5.000 8.333 Coefficients:
Estimate Std. Error t value Pr(>|t|) (Intercept) -691.6667 108.8322 -6.355 0.000219 *** CMCNa 171.1111 16.0247 10.678 5.19e-06 *** PG 11.5000 4.2687 2.694 0.027327 * CMCNa:PG -2.3333 0.6285 -3.712 0.005937 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.165 on 8 degrees of freedom
Multiple R-squared: 0.9942, Adjusted R-squared: 0.9921
b. Daya sebar
> aov(dayasebar~CMCNa*PG,data=efek) Call:
aov(formula = dayasebar ~ CMCNa * PG, data = efek) Terms: CMCNa PG CMCNa:PG Residuals Sum of Squares 1.4008333 0.0833333 0.0052083 0.0141667 Deg. of Freedom 1 1 1 8 Residual standard error: 0.04208127
Estimated effects may be unbalanced
> summary.lm(aov(dayasebar~CMCNa*PG,data=efek)) Call:
aov(formula = dayasebar ~ CMCNa * PG, data = efek) Residuals:
Min 1Q Median 3Q Max -0.075 -0.025 0.000 0.025 0.050 Coefficients:
Estimate Std. Error t value Pr(>|t|) (Intercept) 8.316667 0.560908 14.827 4.22e-07 *** CMCNa -0.594444 0.082589 -7.198 9.27e-05 *** PG -0.020833 0.022001 -0.947 0.371 CMCNa:PG 0.005556 0.003239 1.715 0.125 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.04208 on 8 degrees of freedom
Multiple R-squared: 0.9906, Adjusted R-squared: 0.987
F-statistic: 280.4 on 3 and 8 DF, p-value: 1.932e-08 5. Uji ANOVA efek
a. Viskositas
> anova=aov(viskositas~CMCNa*PG,data=efek) > anova
Call:
aov(formula = viskositas ~ CMCNa * PG, data = efek)
Terms:
CMCNa PG CMCNa:PG Residuals Sum of Squares 85852.08 5418.75 918.75 533.33
Deg. of Freedom 1 1 1 8 Residual standard error: 8.164966
Estimated effects may be unbalanced > summary(anova)
Df Sum Sq Mean Sq F value Pr(>F) CMCNa 1 85852 85852 1287.78 3.98e-10 *** PG 1 5419 5419 81.28 1.83e-05 *** CMCNa:PG 1 919 919 13.78 0.00594 ** Residuals 8 533 67 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 b. Daya sebar > anova=aov(dayasebar~CMCNa*PG,data=efek) > anova Call:
aov(formula = dayasebar ~ CMCNa * PG, data = efek) Terms: CMCNa PG CMCNa:PG Residuals Sum of Squares 1.4008333 0.0833333 0.0052083 0.0141667 Deg. of Freedom 1 1 1 8
Residual standard error: 0.04208127 Estimated effects may be unbalanced > summary(anova)
Df Sum Sq Mean Sq F value Pr(>F) CMCNa 1 1.4008 1.4008 791.059 2.76e-09 *** PG 1 0.0833 0.0833 47.059 0.00013 *** CMCNa:PG 1 0.0052 0.0052 2.941 0.12469 Residuals 8 0.0142 0.0018 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
6. Uji t-berpasangan stabilitas gel a. Formula 1
> geserf1
48jam 1minggu 2minggu 3minggu 4 minggu 1 280 290 250 280 290
2 285 290 270 285 305 3 290 285 280 295 300
> t.test(geserf1$"48jam",geserf1$"4minggu",paired=T) Paired t-test
data: geserf1$"48jam" and geserf1$"4minggu" t = -4, df = 2, p-value = 0.05719
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval: -27.675509 1.008842
sample estimates:
mean of the differences -13.33333
Keterangan: Uji t-berpasangan memiliki p>0,05 sehingga dapat dikatakan antara formula 1 setelah penyimpanan 48 jam dan 4 minggu tidak berbeda bermakna.
b. Formula a > fa
48jam 1minggu 2minggu 3minggu 4minggu 1 460 490 480 500 475 2 475 480 500 510 480 3 480 470 490 490 490 > t.test(fa$"48jam",fa$"4minggu",paired=T)
Paired t-test
data: fa$"48jam" and fa$"4minggu" t = -3.4641, df = 2, p-value = 0.07418
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval: -22.420689 2.420689
sample estimates:
mean of the differences -10
Keterangan: Uji t-berpasangan memiliki p>0,05 sehingga dapat dikatakan antara formula a setelah penyimpanan 48 jam dan 4 minggu tidak berbeda bermakna.
c. Formula b > fb
48jam 1minggu 2minggu 3minggu 4 minggu
1 260 270 270 275 265
2 265 275 260 270 275
3 255 280 265 265 270
> t.test(fb$"48jam",fb$"4minggu",paired=T) Paired t-test
data: fb$48jam and fb$4minggu t = -3.4641, df = 2, p-value = 0.07418
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval: -22.420689 2.420689
sample estimates:
mean of the differences -10
Keterangan: Uji t-berpasangan memiliki p>0,05 sehingga dapat dikatakan antara formula b setelah penyimpanan 48 jam dan 4 minggu tidak berbeda bermakna.
d. Formula ab > fab 48jam 1 2 3 4 1 420 420 400 420 425 2 400 410 410 430 415 3 415 430 415 420 420 > t.test(fab$"48jam",fab$"4",paired=T) Paired t-test
data: fab$"48jam" and fab$"4" t = -2.5, df = 2, p-value = 0.1296
alternative hypothesis: true difference in means is not equal to 0
-22.675509 6.008842 sample estimates:
mean of the differences -8.333333
Keterangan: Uji t-berpasangan memiliki p>0,05 sehingga dapat dikatakan antara formula 1 setelah penyimpanan 48 jam dan 4 minggu tidak berbeda bermakna.
7. Uji t-independen persen penghambatan edema
> inflamasi
positif gel antinflamasi 1 63.942 51.786 2 59.409 43.132 3 74.450 44.574
> t.test(inflamasi$"positif",inflamasi$"gel antinflamasi")
Welch Two Sample t-test
data: inflamasi$positif and inflamasi$"gel antinflamasi" t = 3.7398, df = 3.278, p-value = 0.02862
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval: 3.662135 35.210531
sample estimates: mean of x mean of y 65.93367 46.49733
Keterangan: Uji t-independen memiliki p<0,05 sehingga dapat dikatakan bahwa persen penghambatan edema gel ekstrak daun cocor bebek memiliki perbedaan yang bermakna dengan kontrol positif Voltadex®.
Lampiran 12. Perhitungan efek CMC Na, propilen glikol, dan interaksi
