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: Analisis Regresi Data Panel pada Kasus Tingkat Pengangguran Terbuka (TPT) di Provinsi Jawa Barat
Abstrak: Pengangguran merupakan permasalahan akibat jumlah angkatan kerja yang lebih banyak dari jumlah lapangan pekerjaan yang tersedia. Tingkat pengangguran terbuka (TPT) di Provinsi Jawa Barat mengalami penurunan dari tahun 2017 sampai tahun 2019. Namun, nilai tersebut masih berada di atas nilai TPT Indonesia sehingga perlu penanganan yang tepat untuk menurunkan nilai TPT di Jawa Barat. Penelitian ini bertujuan untuk menentukan peubah peubah yang memengaruhi TPT di Provinsi Jawa Barat. Data penelitian ini terdiri atas 27 kabupaten/kota di Jawa Barat dalam kurun waktu tiga tahun, yaitu dari tahun 2017 sampai tahun 2019. Analisis regresi data panel digunakan untuk melihat adanya pengaruh spesifik unit individu maupun waktu sehingga penelitian tidak terbatas hanya mengamati satu individu atau satu kurun waktu saja. Berdasarkan hasil penelitian, model yang sesuai untuk menggambarkan TPT di Provinsi Jawa Barat adalah model pengaruh acak dengan pengaruh spesifik individu yang memiliki nilai kebaikan model sebesar 23,45%. Peubah penjelas yang berpengaruh signifikan terhadap TPT di Jawa Barat, yaitu kepadatan penduduk, upah minimum kabupaten/kota, dan harapan lama sekolah. Peubah kepadatan penduduk dan upah minimum kabupaten/kota memiliki hubungan yang positif terhadap TPT di Jawa Barat, sedangkan peubah harapan lama sekolah memiliki hubungan yang negatif dengan TPT di Jawa Barat., Unemployment is a problem due to the number of labor force that is more than the number of available jobs. The open unemployment rate (TPT) in West Java Province has decreased from 2017 to 2019. However, this value is still above the value of Indonesia's TPT so that proper handling is needed to reduce the value of TPT in West Java. This study aims to determine the variables that affect TPT in West Java Province. This research data consists of 27 districts/cities in West Java over a period of three years, namely from 2017 to 2019. Panel data regression analysis is used to see the specific effects of individual units and time so that the research is not limited to only observing one individual or one period of time. Based on the results of the study, the appropriate model to describe TPT in West Java Province is a random effect model with an individual specific effects which has a model goodness value of 23.45%. The explanatory variables that have a significant effect on TPT in West Java, are population density, district/city minimum wages, and expected years of schooling. The variable population density and the district/city minimum wage have a positive relationship to TPT in West Java, while the variable of long-term school expectations has a negative relationship to TPT in West Java.
Keyword: individual specific effects, open unemployment rate (TPT), panel data regression, random effect model

Judul: Sintesis Flavonol melalui 2’-Hidroksikalkon
Abstrak: Flavonol is a class of flavonoids commonly found in fruits and vegetables. Flavonol is very useful, especially in food sector and health. In this study, flavonol unsubstituted was successfully synthesized from an intermediate material 2’-hydroxychalcone. The 2’-hydroxychalcone was synthesized by Claisen-Schmidt condensation method between o-hydroxyacetophenone and benzaldehyde using 4 different catalysts, namely KOH 60%, solid pellet of KOH-grinding technique, BF3-Et2O, and NaH in DMF. The KOH 60% was the best catalyst, giving the highest yield (57%). Afterward, the flavonol was synthesized in 1-stage oxidation cyclization of o-hydroxyacetophenone using Algar-Flynn-Oyamada (AFO) reaction, giving 76%. The flavonol total yield from 2’-hydroxychalcone was 43%. All synthesized products were characterized by their melting points and their spectra. The success of the synthesis flavonol opens the way to synthesis of useful flavonol derivatives such as quercetin and kaempferol.
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