Judul: Perbandingan Metode Multivariate Imputation by Chained Equations (MICE) dan Regresi Data Panel untuk Menduga Data Hilang pada Dapodik
Abstrak: Studi longitudinal telah dilakukan di berbagai bidang penelitian. Salah satu contoh studi longitudinal di bidang pendidikan adalah penelitian dengan menggunakan Dapodik. Namun ada kalanya data yang terkumpul tidak lengkap karena beberapa kemungkinan, antara lain kelalaian atau ketidakdisiplinan pihak sekolah dalam mengisi Dapodik. Oleh karena itu, diperlukan suatu penanganan data hilang dengan memasukkan nilai tertentu dalam data yang hilang, yang juga dikenal sebagai imputasi. Dalam penelitian ini akan diterapkan metode imputasi MICE dan regresi data panel untuk memprediksi data hilang pada Dapodik di DKI Jakarta tahun 2016 sampai 2020, serta membandingkan akurasi antara kedua metode tersebut dalam membuat model imputasi. Simulasi pembangkitan data hilang dilakukan pada peubah X4, X5, dan X6. Metode MICE yang menggunakan regresi dua level mampu menduga data hilang dengan baik. Model regresi data panel mampu menduga data hilang pada peubah X4 dengan cukup baik dalam penelitian ini, namun kurang baik saat digunakan untuk menduga data hilang pada peubah X6. Kedua metode menunjukkan sebaran data hasil pendugaan yang cenderung serupa dengan sebaran data aktual untuk peubah X4 dan X5. Secara keseluruhan, metode MICE menghasilkan ketepatan yang lebih baik dalam menduga data hilang pada peubah X4, X5, dan X6., Longitudinal studies have been carried out in various fields of research. One example of a longitudinal study in the field of education is research using Dapodik. However, there are times when the collected data incomplete due to several possibilities, including the school's negligence or indiscipline in filling out the Dapodik. It is necessary to handle missing data by entering a certain value in the missing data, which is also known as imputation. In this study, MICE imputation method and panel regression will be applied to predict missing data on Dapodik in DKI Jakarta in the year 2016 until 2020, as well as compare accuracy between those two methods in making imputation models. Missing data on variables X4, X5, dan X6 are generated through simulation based on the MAR mechanism. The MICE method that uses two-level regression is able to handle missing data well. The panel data regression model is able to handle missing data on the X4 variable quite well in this study, but not suitable for handling missing data on the X6 variable. Both methods show prediction results distribution that tend to be similar to the actual data distribution for X4 and X5 variables. Overall, the MICE method produces better accuracy for handling missing data on the X4, X5, dan X6 variables.
Keyword: dapodik, imputation, missing value, MICE, panel data regression

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: Pembuatan Sel Surya Berbasis Film Ferroelektrik Barium Strontium Titanate (Ba0,5Sr0,5TiO3)
Abstrak: Growth of films Ba0,5Sr0,5TiO3 (BST) 1 M has been done with variation of annealing in hold time 8 hours, 15 hours, 22 hours, and 29 hours at constant temperature 8500C, above Si (100) substrate p-type, by using the sol-gel process method which followed with spin coating process at 3000 rpm for 30 seconds. Optics spectroscopy shows that BST film solar cells with annealing hold time 8 hours, 15 hours, and 22 hours, works effectively in wavelength ≥ 700 nm absorption range. Yet for sample with annealing hold time about 29 hours effectively absorb in ≤ 700 nm wavelength range. It obtained BST film refractive index at range 2.0 to 7.0 to wavelength range ±370-870 nm. Band gap BST film value based on annealing hold time 8 hours, 15 hours, 22 hours, and 29 hours respectively 2.60 eV; 3.12 eV: 3.24 eV, and 2.66 eV. Electrical conductivity value which obtained, indicate that the BST film is classified as semiconductor material with conductivity order 10-5-10-4 S / cm. The result of I-V photovoltaic characterization shows the BST film potentially to be a solar cells device with efficiency according to the annealing hold time 8 hours, 15 hours, 22 hours, and 29 hours in a row are 2.68x10-5%, 3.51x10-5%, 4.53x10-5% and 3.22x10-5%
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