Judul: Analisis Data Longitudinal Menggunakan Multivariate Latent Curve Model
Abstrak: Kemiskinan merupakan masalah multidimensional yang terjadi di negara maju maupun berkembang. Jumlah penduduk miskin di Indonesia mencapai 25.67 juta. Kemiskinan tersebut berpengaruh terhadap kualitas hidup. Kemiskinan dan kualitas hidup tidak dapat diukur secara langsung serta mengalami perubahan setiap tahun sehingga dapat dikaji menggunakan analisis multivariate dan longitudinal. Analisis yang dapat digunakan untuk data multivariate dan longitudinal yaitu multivariate latent curve model (LCM). Pada penelitian ini, multivariate LCM diterapkan ke data longitudinal dengan kondisi kemiskinan dan kualitas hidup sebagai peubah laten. Pemodelan dilakukan dengan dua cara yaitu model dasar dan model target. Hasil evaluasi model menggunakan GFI dan AGFI menunjukkan model dasar lebih baik dibanding model target. Hal ini menunjukkan perubahan kualitas hidup dan kondisi kemiskinan dari tahun ke tahun konstan. Pernyataan ini didukung oleh nilai slope yang diperoleh pada model target mendekati nol. Model dasar menunjukkan Kabupaten/Kota di Pulau Jawa memiliki kualitas hidup lebih tinggi dan kemiskinan yang lebih rendah dibandingkan di luar Pulau Jawa. Kualitas hidup dan kondisi kemiskinan memiliki arah hubungan berlawanan, semakin tinggi kualitas hidup cenderung semakin rendah kondisi kemiskinan dan sebaliknya., Poverty is a multidimensional problem in both developed and developing countries. The number of poor people in Indonesia reaches 25.67 million. Poverty and quality of life have a significant relationship. Both of them are unobserved and always change every year so they can be studied using multivariate and longitudinal analysis. The method that can be used for multivariate and longitudinal data is the multivariate latent curve model (LCM). In this study, multivariate LCM was applied to longitudinal data using conditions of poverty and quality of life as latent variables. This method consists of two types namely the baseline model and freed loading model. The result of the model evaluation, GFI and AGFI values show that the baseline model is better than freed loading model. This shows changes in quality of life and conditions of poverty from year to year are constant. This statement is supported by the mean value of the random slope obtained in the target model which is close to zero. The baseline model shows a higher quality of life and lower condition of poverty in Java. Quality of life and condition of poverty has a negative correlation, high quality of life tends to decrease the condition of poverty.
Keyword: poverty, multivariate latent curve model, quality of life

Judul: Application of Structural Equation Modeling-Partial Least Squares on Poverty Factors in Central Java
Abstrak: Jumlah penduduk miskin di Jawa Tengah pada Maret 2020 sebesar 3,98 juta orang (11,41%), peringkat kedua terbesar di Pulau Jawa. Angka jumlah penduduk miskin yang relatif tinggi menjadi prioritas bagi pemerintah untuk menanggulangi kemiskinan. Salah satu cara penanggulangan kemiskinan adalah dengan mengetahui faktor kemiskinan. Penelitian ini bertujuan untuk mengidentifikasi faktor kemiskinan di Jawa Tengah menggunakan metode Structural Equation Modelling-Partial Least Squares (SEM-PLS). Data yang digunakan dalam penelitian ini adalah data kabupaten/kota di Jawa Tengah tahun 2020. Pada kasus ini, terdapat satu peubah laten eksogen kesehatan dan tiga peubah laten endogen kemiskinan, ekonomi, dan sumberdaya manusia. Permasalahan yang ditemui adalah data amatan relatif kecil yaitu 35 amatan serta sebaran data tidak memenuhi asumsi kenormalan, sehingga analisis yang tepat digunakan dalam penelitian ini adalah Structural Equation Modelling-Partial Least Squares (SEM-PLS). Hasil penelitian menunjukkan bahwa peubah laten ekonomi dan Sumber Daya Manusia memiliki pengaruh positif tetapi tidak signifikan. Peubah laten kesehatan memiliki pengaruh negatif dan signifikan terhadap peubah laten kemiskinan. Nilai Q2 untuk peubah laten kemiskinan adalah 0,333, hal ini menunjukkan bahwa sebesar 33,3% keragaman peubah laten kemiskinan dapat dijelaskan oleh peubah laten ekonomi, kesehatan, dan sumber daya manusia., The number of poverty-stricken people in Central Java in March 2020 was 3.98 million people (11.41%), the second-largest in Java. The approximately high number of poverty-stricken people is a priority for the government to reduce poverty. One of the solutions to reduce poverty is knowing the factors of poverty. The purpose of this study is to identify poverty factors in Central Java using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method. This study used data from districts/ cities in Central Java in 2020. In this case, there is one exogenous latent variable for health and three endogenous latent variables for poverty, economy, and human resources. The problem encountered that the observed data is relatively small, specifically for 35 observations and the data distribution is suspected not fulfilled the normal assumptions. In conclusion, the appropriate analysis used in this study is Structural Equation Modeling-Partial Least Squares (SEM-PLS). The results showed that the latent variables of economics and human resources had a positive but not significant effect. Latent variables for health had a negative and significant effect on the latent variables of poverty. The Q2 value for the latent variable of poverty is 0.333, this shows that 33.3% of the diversity of the latent variable of poverty can be explained by the latent variables of economy, health, and human resources.
Keyword: Poverty, Structural Equation Modeling (SEM), Partial Least Square (PLS)

Judul: Photosensor producing based on ferroelectric material of Ba0,5Sr0,5TiO3 doped by Ferrium Oxide (Fe2O3) on a P-Type Silicon Substate Si (100) applied as a digital camera with a single pixel image sensor
Abstrak: Thin film Ba0,5Sr0,5TiO3 (BST) doped with Fe2O3 was grown on p-type silicon substrate (100) using chemical solution deposition (CSD). It used the solvent 2-methoxyethanol with annealing temperature of 850°C and the doping treatment Fe2O3 of 2.5, 5, 7.5, 10 % of the mass formed BST. Pure BST had the highest absorption of 600 nm of light wavelength. Additions Fe2O3 moves highest absorption in the range of 560 nm. Fe2O3 addition was strengthening thin film sensitivity to the effects of light. In general, doping Fe2O3 10% was result the best absorbance and current-voltage of BST thin film. Electrical properties of thin films BST include dielectric, photoconductivity, and dissipation energy. Based on the current-voltage (I-V) analysis, pure BST and doped BST showed photodiode properties. Dielectric values obtained in the range from 23.32 to 243. Based on electrical conductivity data and energy dissipation, BST thin films have shown photodiode characteristics and are therefore suitable as light sensors. The image sensor in a digital camera is also a light sensor. This BST thin film has been scientifically proven to be a light sensor and also it can be applied as an image sensor. This is a novelty image sensor based on thin film Ba0.5Sr0.5TiO3.
Keyword: barium strontium titanate, thin film, photodiode, image sensor, digital camera