Judul: Comparison Of SimulationExtrapolation, Regression Calibration And Instrumental Variable In Reducing The Bias Due To Measurement Error
Abstrak: The inability to accurately measure important variables complicates the statistical analysis of data when the purpose of inference is for the model defined in terms of the unobserved variables. In such situation, fitting the model directly to the observed data induces the bias estimates. For reducing the bias, some methods have been proposed such as Simulation-Extrapolation, Regression Calibration and Instrumen1al Variable. In this paper, the effects of measurement error on estimation)n linear and logistic regression will be demonstrated and the methods for bias rcd.uction will be compared. via simulation. The measurement error of predictors reduces the parameter estimate i.e. problem of attenuation. The corre1a1ion in predictors or the correlation in errors influences the magnitude and the direction of the bias. In general, Simulation-Extrapolation, Regression Calibration and Instrumental Variable can reduce the bias except for the structure of complex error in simple linear regression lnstrumental Variable tends more relatively stable on estimation with the increasing of measurement error compared with SimulationExtrapolation or Regression Cahbration estimation.
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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: Kajian Pengembangan Usaha Agribisnis Sayuran Di Perusahaan Pacet Segar, Cianjur, Jawa Barat
Abstrak: Sayuran adalah salah satu komoditas agribisnis yang sangat baik dikembangkan di Indonesia. Menurut Direktorat Jenderal Tanaman Pangan dan Hortikultura (1999), pada periode tahun 1997-1998 terjadi peningkatan produksi untuk 18 komoditas sayuran unggulan sebesar 17,83 persen. Berdasarkan pada produksi 11 jenis komoditas (bawang merah, bawang putih, bawang daun, kubis, petsai, sawi, kacang panjang, wortel, cabe, kentang, dan tomat), Jawa Barat menempati urutan pertama dengan rata-rata jumlah produksi tahun 1994-1998 sebesar 1.914.502 ton/tahun, diikuti Jawa Tengah dan Sumatera Utara masing- masing jumlah produksi per tahunnya sebesar 1.083.009 ton dan 950.268 ton. Dari segi konsumsi bahan pangan, sayuran merupakan sumber vitamin dan mineral yang penting bagi pemenuhan gizi masyarakat. Oleh karena itu permintaan sayuran menunjukkan kecenderungan yang meningkat seiring dengan meningkatnya pertumbuhan penduduk, peningkatan pendapatan, semakin membaiknya kesadaran masyarakat akan pentingnya sayuran dalam pemenuhan gizi, serta berkembangnya industri jasa boga bagi keperluan hotel dan restoran yang memerlukan pasokan sayur mayur berkualitas tinggi. Perusahaan Pacet Segar merupakan salah satu perusahaan agribisnis sayuran yang berada di Kabupaten Cianjur, Jawa Barat yang menekankan pada proses penambahan nilai tambah dari komoditas sayuran tersebut. Perusahaan tersebut bertindak sebagai pedagang besar (wholesaler). Sayuran yang dipasarkan oleh perusahaan Pacet Segar selama tujuh bulan terakhir ini (September 1999 sampai dengan Maret 2000) adalah 84 jenis sayuran dengan rata-rata 36,021 ton per bulan. sayuran yang memberikan nilai penjualan terbesar adalah daun bawang dengan kontribusi 10,46 persen, bunga kol (9,28%), wortel (8,02 %), Lettuce Head (7,18%) dan tomat (6,17%). Seiring dengan perkembangan produksi dan permintaan pasarnya, pada saat ini perusahaan Pacet Segar menerima pesanan dari mitra pasar baru yaitu PT. Aerowisata Catering Service sebanyak 1,492 ton per hari, yang terdiri dari beberapa komoditas sayuran dengan persyaratan-persayaratan mutu tertentu. Dalam proses pemenuhan permintaan tersebut, perusahaan Pacet Segar memandang perlu untuk melakukan kajian mengenai kelayakan pengembangan usahanya tersebut. Kajian ini dilakukan untuk membantu perusahaan Pacet Segar dalam melakukan analisis kelayakan pengembangan tersebut.
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