Judul: Handling Spatial Heterogeneity in Life Expectancy Rates for Jawa Timur Province from 2019 to 2021 Using Geographically Weighted Panel Regression
Abstrak: Perserikatan Bangsa Bangsa menekankan bahwa manusia adalah kekayaan utama bangsa. Oleh karena itu, tujuan utama pembangunan manusia adalah menciptakan lingkungan yang memungkinkan orang menikmati umur panjang, hidup sehat, dan menjalani kehidupan yang produktif. Harapan hidup, sebagai bagian penting dari penelitian ini, adalah komponen perhitungan Indeks Pembangunan Manusia. Jawa Timur adalah salah satu provinsi dengan harapan hidup di bawah rata-rata nasional. Harapan hidup merupakan sudut pandang yang kompleks dan tidak hanya terjadi pada satu waktu, tetapi dapat dipengaruhi oleh aspek regional atau spasial, hal ini menunjukkan perlunya penelitian yang melibatkan efek spasial selama beberapa periode. Dalam hal ini, data spasial dengan struktur panel digunakan dalam penelitian ini. Data ini membuka kemungkinan adanya pengaruh spasial seperti heterogenitas spasial. Uji heterogenitas spasial dilakukan menggunakan uji Breusch-Pagan. Berdasarkan uji ini, ditemukan adanya heterogenitas spasial dalam data harapan hidup di Jawa Timur pada tahun 2019-2021, sehingga analisis lebih lanjut dilakukan menggunakan model Geographically Weighted Panel Regression (GWPR). Hasil menunjukkan bahwa model terbaik dengan nilai AIC terendah dan adjusted R^2 tertinggi adalah adaptive kernel bisquare, dengan nilai AIC sebesar 67,62 dan nilai adjusted R^2 sebesar 95,95%. Peubah harapan lama sekolah, persentase penduduk miskin, dan pengeluaran per kapita disesuaikan memiliki pengaruh signifikan terhadap indeks harapan hidup di sebagian besar wilayah di Jawa Timur., The United Nations emphasized that humans are the true wealth of the nation. Therefore the primary purpose of human development is to create an enabling environment for people to enjoy longevity, live healthily, and lead productive lives. Life expectancy, as an important part of this research, is a component of the Human Development Index calculation. Jawa Timur is one of the provinces with a life expectancy below the national average. The life expectancy is a complex viewpoint that does not only occur at one time and can be influenced by regional or spatial aspects, this indicates the need for a research involving spatial effects over several periods. In this regard, spatial data with panel structure is used in this research. Spatial data with a panel structure results in the possibility of the emergence of spatial influences such as spatial heterogeneity. Spatial heterogeneity test was performed using the Breusch-Pagan test. Based on the test, it was found that there is spatial heterogeneity in life expectancy data in Jawa Timur in 2019-2021, so further analysis was carried out using the Geographically Weighted Panel Regression (GWPR) model. The result showed the best model with the lowest AIC and the highest adjusted R^2 was the adaptive kernel bisquare, with an AIC value of 67,62 and an adjusted R^2value of 95,95%. The variables of expected years of schooling, percentage of poor population and consumption per capita had significant effect on the life expectancy index in most region in Jawa Timur.
Keyword: GWPR, life expectancy, panel data, spatial heterogeneity

Judul: Pemodelan Angka Harapan Hidup di Provinsi Papua dengan Pendekatan Model Data Panel Dinamis Spasial
Abstrak: Life expectancy rate is one of the indicators used to comprehensively measure the health status of the community in an area. Life expectancy is used as a reference in planning health programs and as an evaluation of the government's performance in improving the welfare of the population in general and improving health status in particular. One of the provinces with the lowest life expectancy achievement is Papua Province which is in the second most down position nationally from 2014 to 2019. The achievement of life expectancy in Papua province varies widely with different characteristics between regions. This achievement is very different from other provincial factors, so it is necessary to identify the contributing factors. In this research, life expectancy will be modelled using spatial regression analysis of dynamic panel data. Panel data regression is used for the relationship between two or more in the data panel. In contrast, spatial regression is used for the relationship between several things taking into account the spatial effect. In other words, this research was conducted to examine the relationship between several changes in the data panel by considering the spatial aspect. The data used in this study is secondary data from the Indonesian Central Statistics Agency for 2012-2019. The parameter estimation method used is Maximum Likelihood (ML). The variables that are thought to affect life expectancy in 29 districts/cities in Papua Province are the percentage of the sick population, the percentage of households that use a proper drinking water source, school year expectations, average years of schooling, and population density. The results showed a spatial dependence on the response and explanatory variables. The modeling of the life expectancy of the Papua province in 2012-2019 is the Dynamic Spatial Durbin Model (MDSD) with a weighting matrix for the two closest neighbors. The modeling results show that the estimation of the effect of spatial dependence and time lag, namely ρ and λ has a significant impact. The variables that affect life expectancy are expected years of schooling, population density, and the percentage of households with proper sanitation. This modeling can explain the diversity of the model by 73.57%. The marginal effect shows that the variable with the most significant influence on life expectancy is school year expectancy. Further research can add other variables that can affect life expectancy in terms of socio-economic, environmental, and others and can build models that are robust to outliers.
Keyword: Life Expectancy, Dynamic Panel Data, Spatial, Spatial Dynamic Panel Data

Judul: Sintesis 1-Kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai Prekursor Asam (S)-(+)-(6-Metoksi-2-naftil)propanoat
Abstrak: Senyawa 1-kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai prekursor asam (S)-(+)-(6-metoksi-2-naftil)propanoat disintesis dari bahan awal 2-metoksinaftalena. Tahapan sintesis diawali dengan klorinasi menggunakan 2 jenis pereaksi klorinasi, yakni isosianurat terklorinasi dan NaOCl. Klorinasi dengan isosianurat terklorinasi berhasil menyintesis 1-kloro-2-metoksinaftalena sebagai produk monoklorinasi melalui pencirian dengan kromatograf gas-spektrometer massa (GC-MS). Nisbah konversi produk paling efisien diperoleh pada laju penambahan 2.5 mmol/jam. Sebaliknya, klorinasi dengan NaOCl menghasilkan produk triklorinasi. 1-Kloro-2-metoksinaftalena selanjutnya diasilasi dengan propanoil klorida, tetapi analisis dengan GC menunjukkan bahwa produk asilasi tidak terbentuk. Sistem bebas-air diperlukan untuk keberhasilan proses asilasi tersebut.
Keyword: isosianurat terklorinasi, NaOCl, nisbah konversi