Judul: Perbandingan Metode Hot-deck, Regression dan K-Nearest Neighbor Imputation dalam Pendugaan Data Hilang pada Dapodik Tahun 2020
Abstrak: Data Pokok Pendidikan (Dapodik) adalah sistem pendataan nasional yang memuat data satuan pendidikan. Data hilang pada Dapodik menyebabkan hilangnya informasi penting, sehingga dapat diatasi dengan imputasi. Imputasi merupakan prosedur untuk memprediksi nilai yang hilang dengan metode tertentu. Penelitian ini membandingkan tiga metode imputasi yaitu Hot-deck Imputation, Regression imputation dan K-Nearest Neighbor Imputation (KNNI). Hot-deck imputation adalah metode imputasi menggunakan nilai yang memiliki karakteristik serupa dari observasi lain. Regression imputation adalah metode untuk memprediksi data hilang dengan menggunakan pendekatan regresi. KNNI adalah metode imputasi dengan mengelompokkan data berdasarkan keanggotaan terdekat. Dilakukan simulasi pembangkitan data hilang dengan persentase 2%, 3%, 4% dan 5% lalu dilakukannya imputasi dengan ketiga metode tersebut. Model terbaik ditentukan berdasarkan RMSE dan MAPE terendah. Metode imputasi terbaik berdasarkan nilai RMSE dan MAPE terendah yaitu regression imputation., Data Pokok Pendidikan (Dapodik) is a nationwide data collection system that contains data on education units. Missing value in Dapodik causes the loss of important information. To solve this problem can use imputation. Imputation is a procedure to predict the missing value with a certain method. This study aims to compare three imputation methods which are Hot-deck imputation, Regression Imputation, and K-Nearest Neighbor imputation (KNNI). Hot-deck imputation is an imputation method with values that have similar characteristics. Regression imputation is a method to predict missing values by using the regression approach. KNNI is an imputation method that groups data based on the closest neighbor. Simulation for generating missing value was carried out by dividing the percentage of 2%, 3%, 4%, and 5% then imputed with the three methods. The best model is determined based on the lowest value of RMSE and MAPE. The best imputation method based on the lowest RMSE and MAPE values is a regression imputation
Keyword: Dapodik, Hot Deck Imputation, KNNI, Missing Value, Regression Imputation

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: 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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