Judul: The Effect of Missing Data Estimation on The Results of Analysis Structural Equation Modeling (Case Study: Human Development Indeks Java Island)
Abstrak: Data hilang merupakan salah satu faktor yang dapat menurunkan kualitas data, karena dapat menyebabkan bias pada pendugaan parameter model sehingga kesimpulan yang didapatkan menjadi tidak tepat (Chen et al. 2012). Upaya untuk mengatasi data hilang dapat ditempuh melalui imputasi dengan menggunakan beberapa metode, antara lain linear interpolation, linear trend, dan serial mean. Dalam penelitian ini, Analisis Structural Equation Modeling Partial Least Square (SEM-PLS) digunakan untuk melihat dampak adanya data hilang terhadap hubungan kausalitas antar peubah yang direpresentasikan melalui tingkat validitas dan reliabilitas data dalam pendugaan koefisien model. Hasil evaluasi model menunjukkan metode linear interpolation saat persentase data hilang 5% dan 10%, metode linear trend dan serial mean saat persentase data hilang 5% menghasilkan model yang memenuhi semua kriteria dalam evaluasi model. Hasil dari akurasi dan ketepatan menggunakan MAPE menunjukkan metode linear interpolation saat persentase data hilang 5%, 10%, dan 15%, metode linear trend saat persentase data hilang 5% dan 10%, metode serial mean saat persentase data hilang 5% memberikan hasil yang baik dalam menduga nilai koefisien model., Missing data is one of factors that can reduce data quality, because it can cause biased in estimating model parameters so that conclusions obtained are incorrect (Chen et al. 2012). Efforts to overcome missing data can be achieved through imputation using several methods, including linear interpolation, linear trend, and serial mean. Structural Equation Modeling Partial Least Square (SEM-PLS) analysis was used to see impact of missing data on causal relationships between variable which can represent level of validity and reliability of data in estimating model coefficients. Results of model evaluation show linear interpolation method when the percentage of missing data is 5% and 10%, serial mean and linear trend methods when the percentage of missing data is 5% produces models that meet all the criteria in model evaluation. Results of the accuracy and precision using MAPE show that linear interpolation method when percentage of missing data is 5%, 10%, and 15%, linear trend method when percentage of missing data is 5% and 10%, serial mean method when percentage of missing data is 5% give good result in estimating value of the model coefficients.
Keyword: missing data, linear interpolation, linear trend, serial mean, sem-pls

Judul: The Effect of Missing Data Estimation on the  Accuracy of Estimating the Coefficients of the SEM Model
Abstrak: Missing data dapat diatasi dengan menghapus responden yang memiliki missing data dari dataset atau menggunakan imputasi. Namun menghapus data yang tidak lengkap dan hanya menggunakan data yang lengkap saja, dapat mengakibatkan berkurangnya jumlah data yang sudah ditetapkan. Maka jika terdapat missing data cara yang terbaik dengan menggunakan imputasi. Dalam penelitian ini missing data diatasi dengan menggunakan mean imputation. Setiap metode imputasi memiliki karakteristiknya. Tujuan dari penelitian ini adalah untuk mempelajari dampak missing data terhadap akurasi pendugaan koefisien model SEM. Hasil penelitian menunjukkan bahwa semakin meningkatnya ukuran persentase missing data, maka nilai yang dihasilkan akan semakin bias. Akurasi menunjukkan bahwa metode mean imputation dalam mengatasi dampak missing data dengan persentase missing 5% cukup layak dalam menduga koefisien parameter model SEM. Sedangkan pada persentase missing data 10% dan 15% tidak akurat dalam menduga parameter model. Secara keseluruhan metode mean imputation layak digunakan jika persentasi missing data kurang dari 5%., Missing data can be overcome by removing respondents who have missing data from the dataset or using imputation. However, deleting incomplete data and only using complete data can result in a reduction in the amount of data that has been set. So if there is missing data, the best way is to use imputation. In this study, missing data was overcome by using mean imputation. Each imputation method has its characteristics. The purpose of this study was to study the impact of missing data on the accuracy of the estimation of the coefficients of the SEM model. The results showed that the increasing the size of the percentage of missing data, the resulting value will be more biased. Accuracy shows that the mean imputation method in overcoming the impact of missing data with a missing percentage of 5% is quite feasible in estimating the parameter coefficients of the SEM model. While the percentage of missing data is 10% and 15% is not accurate in estimating the model parameters. Overall, the mean imputation method is feasible if the percentage of missing data is less than 5%.
Keyword: accuracy, mean imputation, missing data, SEM

Judul: Extracellular Expression of thermostable Geobacillus stearothermophilus T1.2RQ Lipase for Biodiesel Production
Abstrak: The extracellular expression of enzymes in an excellent secretion host such as Bacillus subtilis is a useful strategy for making enzymes of industrial significance more affordable by lowering the cost of its downstream processing. Lipases are a category of hydrolases that catalyze the hydrolysis of triglycerides to glycerol and free fatty acids. Also, lipases catalyze the hydrolysis and transesterification of other esters as well as the synthesis of esters. They can perform very specific biotransformation reactions and are widely used in food, organic synthesis, detergents, cosmetic, and pharmaceutical industries. Geobacillus stearothermophilus T1.2RQ lipase is an industrially-desired thermostable lipolytic enzyme with an excellent hydrolytic and transesterification activity. This study aimed to express T1.2RQ lipase, in B. subtilis WB800 and ensure its secretion into the extracellular medium. Three signal peptides (AmyQ, Epr, LipA) and two promoters (Pgrac01 and Pgrac100) were used in the secretory-expression of T1.2RQ lipase, based on five constructed expression vectors constructed by restriction cloning and PCR mutagenesis. Sangers sequencing was used to verify constructs to ensure they are in the correct reading frame. Recombinants vectors, pHT43 (plasmid control), pHT43-T1.2RQ, pHT43- T1.2RQ_AmyQ, pHT43-T1.2RQ_Epr, and pHT43-T1.2RQ_LipA, were transformed by electroporation into Bacillus subtilis WB800. The electroporation was successful with the optimum condition of 2500V, 200 ohms, 25µF, and 5milliseconds. Recombinant B. subtilis pHT43 (negative control) and pHT43-T1.2RQlip + signal peptides were streaked on the lipidic agar, LA+TBN. All four recombinants, including the negative control (B. subtilis WB800-pHT43), produced clear zones on the TBN agar plate after overnight incubation (fig 2), indicating the presence of a lipolytic enzyme in all of the recombinant B. subtilis cells. pHT43-T1.2RQlip + signal peptides (AmyQ, Epr, and LipA) have clear zones with significantly higher diameters than B. subtilis WB800-pHT43 (negative control). This phenomenon suggests that the recombinant thermostable lipase T1.2RQ introduced into these cells was successfully expressed, thereby increasing the lipolytic capability of its host cells T1.2RQ lipase was expressed in 50ml terrific broth medium (TB) and the expression induced with 1mM IPTG. Lipase activity assay using p-nitrophenyl esters showed that all three signal peptides directed the secretion of T1.2RQ lipase into the extracellular medium. Signal peptide, LipA, resulted in the highest extracellular activity of 5.6 U/mL, which corresponds to a 6-fold increase over the parent B. subtilis WB800 strain. Meanwhile, signal peptides, AmyQ and Epr produced 4.2 U/mL and 3.4 U/mL activity respectively. SDS-Zymogram analysis confirmed that T1.2RQ lipase was correctly processed and secreted in its original size of 44 kDa. An attempt to further increase lipase expression level by promoter optimization showed that, contrary to expectation, promoter Pgrac100 exhibited similar expression levels as the original Pgrac01. Nevertheless, the demonstration of the successful secretion of T1.2RQ lipase in this work confirms the latter as a promising approach for the industrial production of the enzyme
Keyword: Bacillus subtilis, Expression, Extracellular, Heterologous, Lipase, Thermostable