Judul: Sifat optik dan sifat listrik diode schottky film Barium Titanat (BaTiO3) berstruktur emas - BaTiO3 (Metal – ferroelektrik)
Abstrak: Diode Schottky (emas - BaTiO3) berhasil dibuat dengan ditumbuhkan di atas permukaan interdigital electrode berbahan emas(Au) menggunakan metode Chemical Solution Deposition (CSD) . Film BaTiO3 dibuat dengan proses annealing pada suhu 400oC dengan variasi holding time annealing 4 jam dan 10 jam. Karakterisasi sifat optik diode Schottky dilakukan menggunakan UV-Vis Spektrofotometer untuk memperoleh nilai energi gap Film BaTiO3. Nilai energi gap BaTiO3 pada dua perlakuan holding time annealing 4 jam dan 10 jam sebesar 3.27 eV dan 3.21 eV dan nilai energi gap tersebut termasuk dalam kategori semikonduktor. Karakterisasi sifat listrik diode Schottky dilakukan menggunakan LCR Meter yang didapatkan nilai konduktivitas listrik pada Holding time annealing 10 jam lebih besar daripada 4 jam. Kemudian, didapatkan nilai impedansi yang menunjukkan bahwa pada holding time annealing 4 jam lebih besar daripada holding time 10 jam. Didapatkan pula nilai konstanta dielektrik yang menunjukkan pada waktu holding time annealing 4 jam lebih besar daripada 10 jam.
Keyword: Barium titanat, diode schotty, ferroelektrik, sifat optik, sifat listrik, emas, metal

Judul: Pembuatan Film Tipis Barium Titanat (BaTiO3) Didadah Indium Oksida (In2O3) Menggunakan Metode Chemical Solution Deposition
Abstrak: Film tipis Barium Titanat (BaTiO3) telah berhasil dibuat di atas substrat Si (100) tipe P dengan metode Chemical Solution Deposition (CSD) dengan larutan 1 M berbantuan spin coating dengan kecepatan putar sebesar 8000 rpm. Film ini kemudian di annealing pada suhu 850 0C dengan waktu tahan selama 8 jam. Film tipis barium titanat yang didadah dengan indium oksida dengan konsentrasi 0%, 2%, 4%, dan 6% dikarakterisasi dengan UV-Vis dan XRD sehingga menghasilkan energi gap berturut-turut sebesar 3.45 eV, 3.1 eV, 3.5 eV, dan 2.7 eV. Diperoleh kristal berbentuk tetragonal dengan parameter kisi a = b = 4.075003 Å dan c = 4.088857 Å ; a = b = 4.05802 Å dan c = 4.060536 Å ; a = b = 4.037801 Å dan c = 4.042604 Å ; a = b = 4.072917 Å dan c = 4.06868 Å.
Keyword: annealing, bandgap, BaTiO3, CSD, In2O3

Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis