Judul: Identification of Factor Affecting Open Unemployment Rate in West Java Using Panel Data Regression
Abstrak: Provinsi Jawa Barat merupakan provinsi dengan tingkat pengangguran terbuka tertinggi di Indonesia pada tahun 2022 sebesar 8,31%. Penelitian ini bertujuan menentukan model yang tepat untuk menduga Tingkat Pengangguran Terbuka (TPT) di Provinsi Jawa Barat menggunakan regresi data panel sehingga dapat mengidentifikasi peubah-peubah yang memengaruhinya. Data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari BPS selama lima tahun (2018–2022) dan terdiri dari 27 kabupaten/kota. Peubah respon yang diamati adalah tingkat pengangguran terbuka kabupaten/kota di Provinsi Jawa Barat. Sedangkan, faktor-faktor yang digunakan sebagai peubah penjelas adalah tingkat partisipasi angkatan kerja, rata-rata lama sekolah, pengeluaran per kapita, laju pertumbuhan penduduk, persentase penduduk miskin, angka partisipasi kasar SMA, produk domestik bruto regional per kapita atas dasar harga konstan, upah minimum kabupaten/kota, penanaman modal asing, dan penanaman modal dalam negeri. Hasil penelitian menunjukkan bahwa model regresi data panel yang sesuai untuk menggambarkan tingkat pengangguran terbuka di Jawa Barat adalah model pengaruh acak spesifik dua arah dengan nilai R2 sebesar 41,71%. Peubah yang berpengaruh signifikan pada taraf nyata 5% adalah tingkat partisipasi angkatan kerja, laju pertumbuhan penduduk, dan upah minimum kabupaten/kota., West Java is the province with the highest open unemployment rate in Indonesia by 2022, at 8,31%. This research aims to determine the appropriate model for estimating the Open Unemployment Rate (TPT) in West Java Province using panel data regression so that it can identify the variables that influence it. The data used in this study are secondary data obtained from BPS for five years (2018–2022) and comprised of 27 districts/cities. The observed response variable is the unemployment rate of the open county/city in the West Java province. The factors used as explanatory variables are the participation rate of the labor force, average school age, per capita output, population growth rate, percentage of the poor population, high school gross participation, regional gross domestic product, district/city minimum wage, foreign capital investment, and domestic capital investment. The study results showed that the panel data regression model suitable to describe the open unemployment rate in West Java was a Random Effect Model with a two-way specific effect influence with an R-squared value of 41,71%. Variables significantly affecting the 5% significance level are the labor force participation rate, population growth rate, and district/city minimum wages.
Keyword: open unemployment rate, panel data regression, random effect model two way, west java

Judul: Analisis Robust Spasial Data Panel Untuk Menentukan Faktor Yang Memengaruhi Tingkat Pengangguran Terbuka (Studi Kasus: Provinsi Banten dan Jawa Barat Tahun 2018– 2022)
Abstrak: Tingkat pengangguran di Indonesia dihitung dengan persentase jumlah pengangguran pada seluruh angkatan kerja. Tingkat pengangguran di Banten dan Jawa Barat menempati dua teratas di Indonesia per Agustus 2023. Tujuan dari penelitian ini adalah mengetahui performa robust spasial data panel dibanding dengan model spasial data panel dalam mengidentifikasi faktor-faktor yang memengaruhi tingkat pengangguran terbuka (TPT) di Provinsi Banten dan Jawa Barat tahun 2018–2022. Hasil penelitian menunjukkan terdapat ketergantungan autoregresi spasial dan galat spasial, sehingga model yang dibangun adalah spatial durbin model (SDM). Model Robust SDM dibangun karena pada proses analisis teridentifikasi adanya pencilan spasial yang menyebabkan tidak terpenuhinya asumsi kehomogenan ragam dan kebebasan sisaan pada model SDM. Model Robust SDM mampu menangani penyimpangan asumsi pada model SDM dan meningkatkan performa dengan nilai R^2 sebesar 87,21%. Nilai ini lebih tinggi jika dibandingkan dengan model SDM yang memiliki R^2 sebesar 71,19%. Model terbaik menunjukkan bahwa terdapat empat faktor utama yang memengaruhi TPT di Provinsi Banten dan Jawa Barat tahun 2018–2022, yaitu upah minimum kabupaten/kota, tingkat partisipasi angkatan kerja, rata-rata lama sekolah, dan pertumbuhan ekonomi. Selain itu, TPT juga dipengaruhi oleh faktor dengan pengaruh lag spasial, yaitu upah minimum kabupaten/kota, rata-rata lama sekolah, dan investasi PMA dari kabupaten/kota di sekitarnya., The unemployment rate in Indonesia is calculated as the percentage of the total unemployed in the labor force. The unemployment rates in Banten and West Java are the top two in Indonesia as of August 2023. The purpose of this study is to determine the performance of robust spatial panel data compared to the spatial panel data model in identifying factors that affect the open unemployment rate (TPT) in Banten and West Java Provinces in 2018-2022. The results showed that there is a dependence on spatial autoregression and spatial error, so the model built is the spatial Durbin model (SDM). The Robust SDM model was built because, in the analysis process, spatial outliers were identified which caused the assumptions of variance homogeneity and error autocorrelation in the SDM model to not be fulfilled. The Robust SDM model can handle deviations in the assumptions of the SDM model and can improve performance with an R-squared value of 87.21%. This value is higher than the SDM model which has an R-squared of 71.19%. The best model shows that there are four main factors that affect the TPT in Banten and West Java in 2018-2022, namely district/city minimum wage, labor force participation rate, average years of schooling, and economic growth. In addition, the TPT is also influenced by factors with spatial lag effects, namely district/city minimum wages, average years of schooling, and FDI investment from surrounding districts/cities.
Keyword: open unemployment rate, robust spatial durbin model, spatial durbin  model

Judul: Hybrid solar cells of ZnO nanoparticles/chlorophyll was modified metal ions (Zn2+ and Cu2+ )
Abstrak: Zinc Oxide (ZnO) is a n-type semiconductor with a wide the energy band (Band Gap) is 3.37 eV at room temperature and 3.34 eV at low temperature with a large exiton binding energy is 60 meV. ZnO has been synthesized by hydrothermal method, resulting in a decrease in particle size over the duration of the hydrothermal changes. Crystallographic structure of ZnO were characterized using XRD, has shown the characteristic pattern of a hexagonal wurtzite structure, the crystallite size increases to changes in particle size. Changes in particle size has also led to a change in the absorption peak of ZnO as has been demonstrated posted under test using the optical properties of UV-Vis spectrophotometer. Changes of this uptake has also led to changes in the energy band gap, which increases its band gap energy of the particle size. In electronic applications, especially in solar cells. Chlorophyll is considered less effective, because of their very nature are easily degraded as a result of several reactions which are acid, lighting and temperature. The results of the stability test using a halogen lamp 34 W/m2 for 5 hours, that the substitution of chlorophyll with metal ions Zn2+ and Cu2+ has been shown to form more stable than natural chlorophyll. The characterization of the optical properties, the combination of ZnO / chlorophyll in the form of hybrid films has led to a shift in the absorption region of ZnO becomes more widely from the UV to Visible. ZnO/chlorophyll has absorption range of 350-515 nm, ZnO / Zn-feofitin 332-401 nm and 332-632 nm for Cu-feofitin. The combination of ZnO/chlorophyll and CuSCN as a solid electrolite in hybrid solar cell, a modified chlorophyll with metal ions Zn2+ and Cu2+ has been shown to form a more stable I-V curve. It is characterized by its fill factor value greater than natural chlorophyll. Nevertheless, solar cells with natural chlorophyll has been able to produce higher efficiency values of chlorophyll were substituted.
Keyword: Hydrothermal, ZnO/Chlorophyll modified, Hybrid Solar Cell