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: 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: Budidaya Bunga Potong Anyelir dan Gladiol di Desa Cihideung, Kec. Cisarua Kabupaten Bandung
Abstrak: Pralctek lapang dilalcdcan d i pertanion rokyat d i desa Ciliideung, kec. Cisorua, kab. Bnndung d a r i tanggal 12 Juni sanlpai 12 September 1989. Iceadactn topografi desa bergelombang deqpn lcetinggian 800-1400 m d.p.1. Suku udara rota-rat8 hnrinc nntora 1 7 ' ~ don 27'~. Jellis tanah ondos o l dnn bor.tckstur gembrrr. 1Ce;;iaLnn budidaya bungs potong a n y e l i r dan gladiol meliputi penyiapan lahan, pembibiton, penanaman, pemeliharaan, p a n g dan pa.sco panen. Penyiapan lahan dilakultan dengan membersihkan lahnn yang akan digunakan untuk penanaman dan lcemudian dicang- 1:ul s e r t a dirotalcan.
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