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: Penerapan LASSO untuk Mengidentifikasi Peubah yang  Berpengaruh Terhadap Tingkat Kemiskinan di Jawa Tengah
Abstrak: Indonesia merupakan negara dengan jumlah penduduk terbanyak keempat di dunia dan termasuk kategori negara berkembang. Sebagai negara berkembang salah satu permasalahan yang ada di Indonesia adalah masalah kemiskinan. Provinsi Jawa Tengah menempati posisi kedua sebagai provinsi dengan jumlah penduduk miskin terbanyak di Indonesia pada tahun 2019. Selama lima tahun terakhir persentase kemiskinan di Jawa Tengah mengalami penurunan, akan tetapi pada awal tahun 2020 persentase kemiskinan di Jawa Tengah kembali meningkat. Analisis regresi linier dapat diterapkan pada data kemiskinan di Jawa Tengah untuk mengetahui peubah-peubah yang berpengaruh terhadap tingkat kemiskinan di Jawa Tengah. Salah satu permasalahan yang sering muncul dalam analisis regresi linier adalah adanya korelasi yang tinggi antarpeubah bebas (multikolinearitas). Multikolinearitas dapat mempengaruhi pendugaan metode kuadrat terkecil (MKT). Peubah pada data kemiskinan di Jawa Tengah mengalami masalah multikolinearitas sehingga pada penelitian ini dilakukan pendekatan dengan metode Least Absolute Shrinkage and Selection Operator (LASSO) untuk mengatasinya. Metode LASSO dapat menyusutkan koefisien MKT tepat nol sehingga dapat melakukan seleksi peubah. Model yang dihasilkan metode LASSO lebih sederhana dan secara tidak langsung bebas dari multikolinearitas. Berdasarkan hasil pada penelitian ini, peubah yang masuk ke dalam model LASSO yaitu indeks pembangunan manusia, upah minimum kabupaten, dan pengeluaran pemerintah., Indonesia is the fourth most populous country in the world and belongs to the category of developing countries. As a developing country, one of the problems in Indonesia is poverty. Central Java province was on the second position as the province with the highest number of poor people in Indonesia in 2019. During the last five years the percentage of poverty in Central Java has decreased, but in the beginning of 2020 the percentage of poverty in Central Java has increased again. Linear regression analysis can be applied on poverty data in Central Java to determine the variables that affecting the poverty level in Central Java. One of the problems common in linear regression analysis is the high correlation between independent variables (multicollinearity). In order to handle multicollinearity problem in the poverty dataset in Central Java, the Least Absolute Shrinkage and Selection Operator (LASSO) method in used in this research. The LASSO method can reduce the value of OLS coefficient into zero, this can be the considered as variable selection. Furthermore, the model produced by the LASSO method is quite simpler and indirectly free from multicollinearity. The result show that the variables included in the LASSO model are human development index, district minimum wages, and government spending.
Keyword: LASSO, linear regression, multicollinearity, poverty

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