PEMBUATAN HAIR TONIC BERBAHAN DASAR LIDAH BUAYA DAN ANALISIS DENGAN FOURIER TRANSFORM INFRARED

Dewasa ini penggunaan lidah buaya sangat banyak digunakan dalam pembuatan produk kosmetik yang ramah lingkungan salah satu penggunaan lidah buaya pada pembuatan hair tonic yang berguna untuk melebatkan rambut. Lidah buaya memiliki kandungan utama seperti: minyak atsiri, gum, aloin, mineral, emodin dan vitamin. Lidah buaya yang dimanfaatkan pada penelitian ini adalah gel lidah buaya dari jenis lidah buaya barbadensis yang berarti tanaman yang kaya akan protein, vitamin A, C, dan E, kalsium, untuk melembabkan kulit, menghilangkan jerawat dan meremajakan kulit. Lidah buaya juga berfungsi sebagai antibakteri dan antiinflamasi. Kandungan kimia lidah buaya yang berupa gel dapat dipaparkan secara rinci yaitu saponin, asam sinamat, lignin, polisakarida, eteral oil, acemannan, vitamin B1, B2, B6, asam folat, tannin, enzim oksidase, amilase, monosakarida, glukomanan, enzim bradikinase, dan salisilat. Berdasarkan uraian diatas, maka dilakukan penelitian yang bertujuan untuk membuat hair tonic dari lidah buaya dengan kode F1, F2 dan F3 dengan masing – masing perbandingan ekstrak lidah buaya dan cairan lidah buaya 1:1; 1:2; 1:3, untuk mengetahui sifat fisik dari ketiga hair tonic dan untuk melihat gugus fungsi yang terdapat dalam hair tonic dengan alat instrumentasi Fourier Transform Infrared (FTIR). Hasil penelitian yang diperoleh memperlihatkan bahwa Hair tonic F1 memiliki aroma khas lavender, warna peach, bentuk cairan kental, tekstur lembut, pH 3,8 dan viskositas sebesar 0,0039 ± 0,0003 NS/m2. Gugus fungsi yang terdapat dalam hair tonic F1 yaitu -(CH2)n, C=C aromatic, C=O amida, C=O ester, C-H alkana, C-H alkuna, C-H bending, alkuna dan O-H stretching. Hair tonic F2 memiliki aroma khas melati, warna peach, bentuk caiarn kental, tekstur lembut, pH 3,7 dan viskositas sebesar 0,0034 ± 0,0002 NS/m2. Gugus fungsi yang terdapat dalam hair tonic F2 yaitu -(CH2)n, C=C aromatic, C-O-C eter, C=O aldehid, C-H alkana dan O-H stretching. Hair tonic F3 memiliki aroma khas melati-peppermint, warna peach, bentuk caiarn kental, tekstur lebih lembut, pH 3,8 dan viskositas sebesar 0,0037 ± 0,0001 NS/m2. Gugus fungsi yang terdapat dalam hair tonic F2 yaitu -(CH2)n, C=C aromatic, C-O-C eter, C=O amida, C=O aldehid, C-H alkana dan O-H stretching

Estimator Campuran Spline Smoothing Dan Deret Fourier Dalam Regresi Nonparametrik Multivariabel

Peneliti lebih banyak mengembangkan satu tipe estimator dalam regresi nonparametrik. Namun pada kenyataannya, sering ditemui data dengan pola campuran, khususnya pola data yang sebagian berubah-ubah pada sub interval tertentu dan sebagian lagi polanya mengikuti pola berulang pada suatu tren tertentu. Dalam menangani pola campuran tersebut, maka pada disertasi ini mengembangkan metode baru dalam mengestimasi kurva regresi nonparametrik. Metode ini menggabungkan fungsi Spline Smoothing dan Deret Fourier. Studi teoritis difokuskan pada model estimator dan pengembangan metode untuk memilih parameter penghalus dan parameter osilasi. Model estimator diselesaikan dengan meminimumkan Penalized Least Square (PLS). Pemodelan ini diselesaikan dengan menggunakan dua tahap estimasi yaitu tahap pertama dengan Penalized Least Square (PLS) dan tahap kedua dengan Least Square (LS). Sifat-sifat estimator campuran Spline Smoothing dan Deret Fourier dalam regresi nonparametrik multivariabel merupakan kelas linier dan bias. Pemilihan parameter penghalus dan parameter osilasi untuk model terbaik menggunakan Generalized Cross Validation (GCV). Studi simulasi dilakukan untuk menguji kinerja model estimator campuran Spline Smoothing dan Deret Fourier. Berdasarkan hasil simulasi dapat disimpulkan bahwa semakin besar ukuran sampel dan semakin kecil ukuran varians, semakin baik model yang diperoleh. Analisis data riil yaitu pengeluaran rumah tangga miskin diilustrasikan untuk model model estimator campuran Spline Smoothing dan Deret Fourier. Berdasarkan analisis data riil, estimator campuran Spline Smoothing dan Deret Fourier mampu memodelkan pengeluaran rumah tangga miskin dengan GCVminimum= -13 1,42×10 , R2=98,99%, Mean Square Error (MSE) didapat -5 8,66×10 . ================================================================================================================================== More researchers develop one type of estimator in nonparametric regression. But in fact, it is often encountered data with mixed patterns, especially data patterns that partially change at certain sub-intervals and partially follow a repeating pattern in a certain trend. In dealing with this mixed pattern, this dissertation develops a new method for estimating nonparametric regression curves. This method combines the Spline Smoothing and Fourier Series functions. Theoretical studies are focused on estimator models and the development of methods for selectingsmoothing parameters and oscillation parameters. The estimator model is solved by minimizing the Penalized Least Square (PLS). This modeling was completed using two stages of estimation, namely the first stage with the Penalized Least Square (PLS) and the second stage with the Least Square (LS). The mixed estimator properties of Spline Smoothing and Fourier Series in multivariable nonparametric regression are linear and biased classes. Selection of smoothing parameters and oscillation parameters for the best model uses Generalized Cross Validation (GCV). A simulation study was conducted to test the performance of the Spline Smoothing and Fourier Series estimator models. Based on the simulation results, it can be concluded that the larger the sample size and the smaller the variance, the better the model obtained. The real data analysis, namely the expenditure of poor households, is illustrated for the mixed spline smoothing estimator model and the Fourier series. Based on real data analysis, the estimator of the mixture Spline Smoothing and Fourier Series is able to model the expenditure of poor households with GCVminimum= -13 1,42×10 , R2=98,99%, Mean Square Error (MSE) value is obtained -5 8,66×10

ANALISIS PERCOBAAN FAKTORIAL UNTUK MELIHAT PENGARUH PENGGUNAAN ALAT PERAGA BLOK ALJABAR TERHADAP PRESTASI BELAJAR ALJABAR SISWA

The experimental design was applied in research in many different fields of science, such as in education, as used in this study. Block algebra visual aids is a visual aids in the form of the geometry model used to concretize understanding the variables and constants in the algebra which is an abstract concept. This visual aids are used as a basis for factoring algebraic forms. In connection with this, the aims of this research is to determine the effect of the application of algebra block in student academic achievement in class VII in the field of algebra in schools categorized as private, SSN (Sekolah Standar Nasional) and the previously categorized RSBI (Rintisan Sekolah Bertaraf Internasional). The method of analysis used in this study was two-factor experimental design in a randomized block design. The results showed that the academic achievement of students in the field of algebra after learning with block algebra visual aids obtained better than the academic achievement of students who received learning without using block algebra visual aids. Moreover, it also shows that the categories of schools have a significant effect on student achievement

Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression

So far, most of the researchers developed one type of estimator in nonparametric regression. But in reality, in daily life, data with mixed patterns were often encountered, especially data patterns which partly changed at certain subintervals, and some others followed a recurring pattern in a certain trend. The estimator method used for the data pattern was a mixed estimator method of smoothing spline and Fourier series. This regression model was approached by the component smoothing spline and Fourier series. From this process, the mixed estimator was completed using two estimation stages. The first stage was the estimation with penalized least squares (PLS), and the second stage was the estimation with least squares (LS). Those estimators were then implemented using simulated data. The simulated data were gained by generating two different functions, namely, polynomial and trigonometric functions with the size of the sample being 100. The whole process was then repeated 50 times. The experiment of the two functions was modeled using a mixture of the smoothing spline and Fourier series estimators with various smoothing and oscillation parameters. The generalized cross validation (GCV) minimum was selected as the best model. The simulation results showed that the mixed estimators gave a minimum (GCV) value of 11.98. From the minimum GCV results, it was obtained that the mean square error (MSE) was 0.71 and R2 was 99.48%. So, the results obtained indicated that the model was good for a mixture estimator of smoothing spline and Fourier series

The Application of Mixed Smoothing Spline and Fourier Series Model in Nonparametric Regression

In daily life, mixed data patterns are often found, namely, those that change at a certain sub-interval or that follow a repeating pattern in a certain trend. To handle this kind of data, a mixed estimator of a Smoothing Spline and a Fourier Series has been developed. This paper describes a simulation study of the estimator in nonparametric regression and its implementation in the case of poor households. The minimum Generalized Cross Validation (GCV) was used in order to select the best model. The simulation study used generation data with a Uniform distribution and a random error with a symmetrical Normal distribution. The result of the simulation study shows that the larger the sample size n, the better the mixed estimator as a model of nonparametric regression for all variances. The smaller the variance, the better the model for all combinations of samples n. Very poor households are characterized predominantly in their consumption of carbohydrates compared to that of fat and protein. The results of this study suggest that the distribution of assistance to poor households is not the same, because in certain groups there are poor households that consume higher carbohydrates, and some households may consume higher fats