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 Impact of Missing Data on Reliability and Accuracy in the Results of the Structural Equation Modeling Case Analysis.
Abstrak: Missing data is a problem that often become an obstacle for researcher in data analysis. Missing data can cause bias in a model which can lead to inaccuracies of estimation parameter and wrong conclusion (Chen et al. 2016). General countermeasure used to resolve the adverse of missing data is by removing certain respondent with missing data from dataset. However, wasting data on fillen entry will reduce research efficiency. Another method used to resolve missing data is by imputing data using various approaches, including serial mean, linear interpolation, linear trend. Using Structural Equation Modeling (SEM) analysis, the researcher can observe internal relationship of a large number of variable which can represent the level of data reability in estimating the real data. Missing data estimation method in the SEM produces different estimated parameter coefficient values even when using the same simulation characteristic data. The estimation results show that the linear trend method is optimal in resolving the adverse of missing data in missing precentages of 5%, 10% and 15%. Serial mean method optimal in missing precentages of 5% and 10%. Linear interpolation method optimal in missing precentages 5%. Listwise deletion method is not optimal enough to resolve the adverse of missing data in missing precentages of 5%, 10%, 15% and 20%. The result of goodness of fit model using chi-square test, p-value, RMSEA and the reability level using construct reliability (CR) and variance extracted (VE) shows that increasing the percentage of missing data will cause a decrease in the efficiency of estimation. But more than that, the compatibility of the missing data pattern with the estimated value of each method used will affect the level of efficiency and optimization of the estimation. The result of accuracy and precision level using MAPE shows that in resolving the adverse of missing data the serial mean, linear interpolation, and linear trend methods with a missing precentage 5% are feasible in estimating the parameters of the control SEM model. Meanwhile the listwise deletion method for each missing precentage is not accurate to estimate the parameters of the control SEM model., Missing data is a problem that often become an obstacle for researcher in data analysis. Missing data can cause bias in a model which can lead to inaccuracies of estimation parameter and wrong conclusion (Chen et al. 2016). General countermeasure used to resolve the adverse of missing data is by removing certain respondent with missing data from dataset. However, wasting data on fillen entry will reduce research efficiency. Another method used to resolve missing data is by imputing data using various approaches, including serial mean, linear interpolation, linear trend. Using Structural Equation Modeling (SEM) analysis, the researcher can observe internal relationship of a large number of variable which can represent the level of data reability in estimating the real data. Missing data estimation method in the SEM produces different estimated parameter coefficient values even when using the same simulation characteristic data. The estimation results show that the linear trend method is optimal in resolving the adverse of missing data in missing precentages of 5%, 10% and 15%. Serial
Keyword: missing data, serial mean, linear interpolation, linear trend, SEM

Judul: Ornithonyssus Bursa (Berlese, 1888) Famili Macronyssidae yang Menyerang Unggas Dan Penaggulangannnya
Abstrak: Ornithonyssus bursa adalah tungau penghisap darah pada unggas di daerah tropis dan subtropis. Tungau ini relatif kecil, dengan panjang tubuh antara 700-1000 mikron dan lebarnya 250-490 mikron. Tubuhnya bulat berwarna coklat kekuningan sebelum menghisap darh dan berwarna merah setelah menghisap darah dari induk semangnya.
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