Judul: Analisis Spasial Data Panel Angka Buta Huruf di Provinsi Papua
Abstrak: Angka buta huruf merupakan salah satu indikator penyusun Indeks Pembangunan Manusia (IPM). IPM menjadi tolok ukur dalam keberhasilan upaya membangun kualitas hidup manusia dan dapat menentukan peringkat atau level pembangunan suatu wilayah negara. Provinsi Papua merupakan provinsi di Indonesia dengan angka buta huruf tertinggi dan pendidikan yang sangat minim, sehingga perlu diperhatikan peubah-peubah yang memengaruhi angka buta huruf tersebut. Data penelitian yang digunakan adalah data sekunder dari Badan Pusat Statistik (BPS) dari tahun 2017-2020 yang mencakup 29 kabupaten/kota di Provinsi Papua. Data tersebut meliputi angka buta huruf sebagai peubah respon dengan tujuh peubah penjelas (Produk Domestik Regional Bruto (PDRB) per kapita, banyaknya penduduk miskin, rasio tenaga pendidik terhadap jumlah anak usia sekolah, persentase fasilitas pendidikan, persentase penduduk yang memiliki telepon, angka partisipasi murni SD/sederajat, dan angka kesakitan). Penelitian ini bertujuan untuk mengetahui peubah-peubah yang memengaruhi angka buta huruf di Provinsi Papua. Metode yang digunakan dalam penelitian ini adalah analisis spasial data panel. Hasil penelitian menunjukkan bahwa model yang sesuai untuk menggambarkan angka buta huruf di Provinsi Papua adalah model pengaruh acak Spatial Error Model (SEM) dengan menggunakan matriks pembobot spasial jarak eksponensial. Model ini memiliki nilai koefisien determinasi (R^2) sebesar 77,969%. Peubah penjelas yang berpengaruh terhadap angka buta huruf di Papua pada taraf nyata 5% yaitu PDRB per kapita, rasio tenaga pendidik terhadap jumlah anak usia sekolah, dan banyaknya penduduk miskin. Peubah PDRB per kapita dan rasio tenaga pendidik terhadap jumlah anak usia sekolah memiliki hubungan yang negatif terhadap angka buta huruf di Provinsi Papua,, The illiteracy rate is one of the indicators that make up the Human Development Index. Human Development Index is a benchmark for the success of efforts to build the quality of human life and can determine the ranking or level of development of a country’s territory. Papua Province is the highest illiteracy rate province in Indonesia with a very minimal level of education, so it is necessary to pay attention to what factors affect the illiteracy rate. The research data used is secondary data from the Central Statistics Agency (BPS) from 2017 – 2020 which covers 29 districts/cities in Papua Province. The data includes the illiteracy rate as a response variable, GRDP per capita, the number of poor people, the ratio of educators to the number of school-age children, the percentage of educational facilities, the percentage of the population who owns a telephone, pure enrollment rate for primary school, and the morbidity rate. This study aims to determine the variables that affect the illiteracy rate in Papua Province. The method used in this research is the spatial analysis of panel data. The results show that the appropriate model to describe the illiteracy rate in Papua Province is Spatial Error Model (SEM) with random effect using an exponential matrix which has a model goodness value of 77,969%. The explanatory variables that have a significant effect on the illiteracy rate in Papua Province are GRDP per capita, the ratio of educators to the number of school-age children, and the number of poor people. The GRDP per capita and ratio of educators to number of school age children have a
Keyword: illiteracy rate of Papua Province, panel data analysis, panel data spatial analysis

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 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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