Judul: M-Estimation Using the Huber Weighting Function and the Tukey Bisquare
Abstrak: Keberadaan pencilan dapat memengaruhi pendugaan parameter dalam analisis regresi yang menggunakan metode kuadrat terkecil (MKT). Metode regresi kekar dapat digunakan sebagai alternatif pendekatan dari MKT. Penelitian ini dilakukan untuk mengkaji model regresi dengan metode jumlah kuadrat terkecil dan metode analisis regresi kekar yakni Estimasi-M dengan fungsi pembobot Huber dan Tukey melalui data bangkitan. Data bangkitan diperoleh dengan membangkitkan bilangan acak berdasarkan model regresi linear tanpa pencilan dan diberikan proporsi pencilan. Hasil yang diperoleh menunjukkan bahwa pembobot Tukey dan Huber pada data yang mengandung pencilan memiliki nilai Mean Square Error (MSE) yang lebih kecil dibandingkan dengan MKT. Hal ini menunjukkan bahwa pembobot Tukey dan Huber pada kasus penelitian ini bersifat lebih kekar daripada MKT. Dengan demikian metode Huber dan Tukey dapat digunakan untuk mengatasi gugus data yang mengandung pencilan., The existence of outliers can affect parameter estimation in regression analysis using the ordinary least squares method (OLS). The robust regression method can be used as an alternative approach to the OLS. This research was conducted to examine the regression model using the Least Squares method and the robust method with the Huber and Tukey weighting functions through the generation data. Generation data is obtained by generating random numbers based on a linear regression model with no outliers and given the proportion of outliers. The results obtained indicate that the Tukey and Huber weights on the data containing outliers have a smaller Mean Square Error (MSE) value than the OLS. This shows that the Tukey and Huber weights in the case of this study are more robust than the OLS. Thus, the Huber and Tukey method can be used to overcome clusters of data that contain outliers.
Keyword: pencilan, Estimasi-M, Metode kuadrat terkecil, analisis regresi, M-Estimation, ordinary least squares, regression analysis, outlier

Judul: Kajian Perbandingan Metode Least Median of Squares dan Maximum Likelihood Type pada Regresi Kekar
Abstrak: Keberadaan pencilan dapat memengaruhi pendugaan parameter dalam analisis regresi yang menggunakan metode ordinary least squares (OLS). Metode regresi kekar dapat digunakan sebagai pendekatan alternatif dari OLS. Penelitian ini dilakukan untuk mengkaji perbandingan dari dua metode regresi kekar, yaitu least median of squares (LMS) dan maximum likelihood type (M) melalui data simulasi. Data simulasi diperoleh dengan membangkitkan bilangan acak berdasarkan model regresi linear berganda dua peubah penjelas. Kombinasi yang digunakan adalah jenis pencilan, proporsi pencilan, dan ukuran contoh. Berdasarkan nilai bias mutlak dan kuadrat tengah galat (KTG) diperoleh bahwa metode M lebih tepat diterapkan ketika data mengandung vertical outlier atau good leverage point, lalu diikuti oleh metode LMS. Sementara itu, metode LMS dapat menduga parameter regresi pada data yang mengandung bad leverage point. Analisis selanjutnya dilakukan pada data pertanian mengenai pengaruh pupuk organik dan konsumsi beras per kapita terhadap produksi padi sawah di 34 provinsi tahun 2017. Hasil yang diperoleh menunjukkan bahwa metode LMS menghasilkan nilai bias mutlak dan KTG terkecil., The presence of outliers can be affect to estimate the parameters of the linear regression using ordinary least squares (OLS). The robust regression methods can be used as an alternative to OLS. This study aims to compare the performance of two methods, namely least median of squares (LMS) and maximum likelihood type (M) using simulation study. The simulation data obtained by generating random number based on multiple linear regression with two explanatory variables. With combination of the sample size, outliers type, and the number of outliers. Based on the criteria: absoulut bias and mean square error (MSE), the M-estimator gave better result than the LMS-estimator when the data contains vertical outliers or good leverage point. Meanwhile, the LMS-estimator gave better result at data containing bad leverage points. The next step is analysed data application for agriculture on the affect of organic fertilizer and rice consumption per capita against the use of rice production in 34 provinces in 2017 using the best method based on data characteristic. The results from LMS-estimator produce the smallest absolut bias and MSE.
Keyword: LMS, M, OLS, Outlier, Robust Regression

Judul: Photovoltaic Effect In A CdS/P3HT-Chitosan Junction
Abstrak: A CdS/Polymer junction has been fabricated and it’s photovoltaic effect has been studied. The polymer is a blend of poly-3hexyltiophene (P3HT), chitosan and polyethileneglicol (PEG). Boron in situ doping of CdS using Chemical Bath Deposition (CBD) was investigated. The effect of B-doping and annealing on optical properties as well as on electrical and morphology of CdS film were studied. The structure and morphology of the film were detected by XRay Diffraction (XRD) and Scanning Electron Microscopy (SEM). The polymer film was sandwiched between the CdS semiconductor and an ITO glass to form an ITO/CdS/Polymer/ITO photovoltaic cell. Time constant (τ) and open circuit voltage (Voc) were investigated by using voltage censor. The bandgap of films were determined using absorbance edge method. The results of annealing optical bandgap of undoped CdS shifted from 2,49 eV down to 2,39 eV. Phase transition of CdS due to annealing temperature and induced lattice damage due to doping. The room temperature conductivity of polymer is 3,8 – 6,6 x 10-7 S/cm as function of frequency between 100 – 5000 Hz. The Voc of the fabricated cells ranges between 107 – 286 mV and time constant ranges between 2,5 to 43,75 s.
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