Judul: Modified local getis statistic on AMOEBA weights matrix for spatial panel model and its performance
Abstrak: Classical regression model assumes that no interaction between objects. However, in some cases these conditions are not hold so we have to find an alternative solution that can accommodate the interaction effect. Spatial model is a model that can accommodate the interaction effect by adding a spatial component in the right hand side that represented by a spatial weights matrix. Generally, spatial weighted matrix uses closeness concept among the units, without include the proximity of interest variables. Another way to construct a spatial weights matrix is to use AMOEBA procedure introduced by Aldstadt and Getis. In this procedure, each element of the matrix, beside is determined by the closeness relationship, also determined by proximity among variables using local Getis statistics. Getis and Ord have claimed that the statistic is normally distributed. However, empirically, normal curve test shows that normality of local Getis statistic is influenced by variable of interest (original variable). Due to this reason, we propose a modification of local Getis statistic, namely Gnew, which gives a robust result. A modification of local Getis statistics by transforming to ( ) gives a robust result, that is, it has normal distribution for any distribution of Xj. To assess local Getis and modified local Getis statistics, they are applied on AMOEBA procedure to create weights matrix, namely WG and WGnew. We compare them with contiguity matrix (WC). The model that used for comparison is spatial dynamic panel model with respect to Cizek. By using simulated data and root mean square error relative criteria, it is found that WGnew gives the best performance. In the next stage, we evaluate spatial matrix performance in model to real data, whereas in this case, we take poverty issue in Center Java Province. Poverty is one of the issues of concern at both the central and local government. Central Java was the province with the second largest number of poor people in Indonesia. There are many factors that influence poverty, some of them are GDP per capita, population, education, share of agriculture labor, share of industry labor, share of trading labor and share of services labor (four dominant sectors). Focus of this research is to study influence of labor of junior high school graduates, population, GDP per capita and the share of labor in four dominant sectors to the number of poor people using spatial panel model. Sources of data for modeling obtained from central BPS and Center Java BPS from 2008 to 2012. The research results show that an increase in population, labor of junior high school graduates and share of agricultural labor can increase the number of poor people. Meanwhile the increase in labor share of industry, trade and service sectors and GDP per capita can decrease the number of poor people. Based on these findings, there are several ways to reduce the number of poor people, such as to control population by family planning program (KB program), improving quality of human resources, expand some business, especially in sectors of industry, trading and services.
Keyword: poverty, PDB per capita, random permutation, local Getis statistic, modified local Getis statistic

Judul: Pemodelan Autoregresif Spasial pada Data Panel (Studi Kasus Kemiskinan di Jawa Timur)
Abstrak: Penelitian ini bertujuan mengidentifikasi peubah-peubah yang berpengaruh terhadap persentase kemiskinan di Jawa Timur pada tahun 2017-2020. Metode yang digunakan untuk menjawab tujuan tersebut adalah analisis spasial data panel. Matriks pembobot yang digunakan adalah matriks jarak invers, matriks jarak eksponensial, dan matriks jarak k-tetangga terdekat. Data yang digunakan dalam penelitian ini adalah persentase kemiskinan sebagai peubah respon, sedangkan peubah penjelasnya adalah Indeks Pembangunan Manusia (IPM), Tingkat Pengangguran Terbuka (TPT), angka harapan hidup, rata-rata lama sekolah, rumah tangga yang memiliki akses terhadap sanitasi layak, rumah tangga dengan sumber air minum yang layak, dan pengeluaran rata-rata per kapita sebulan. Hasil analisis menunjukkan bahwa terdapat ketergantungan spasial pada peubah respon dan pengaruh waktu di dalam model data panel sangat kecil sehingga dapat diabaikan. Oleh karena itu, model yang digunakan adalah model panel spatial autoregressive (SAR) dengan pengaruh tetap efek individu dan matriks pembobot jarak 2-tetangga terdekat. Hasil penelitian menunjukkan bahwa peubah-peubah yang berpengaruh signifikan terhadap persentase kemiskinan di Jawa Timur pada tahun 2017-2020 adalah tingkat pengangguran terbuka, angka harapan hidup, dan rata-rata pengeluaran per kapita sebulan., The objective of this reserch is to identify the variables that affect the percentage of poverty in East Java from 2017 to 2020. The method used to answer this objective is spatial analysis of panel data. The weighting matrix used is the inverse distance, exponential distance, and k-nearest neighbor. The data used in this research were the percentage of poverty as response variable, meanwhile the predictor variables include the human development index, open unemployment rate, life expectancy rate, average of length school, households that have access to decent sanitation, households with decent drinking water sources, and average expenditure per capita a month. The result of the analysis show that there is a spatial dependence on the response variable and the effect of time in the panel data model is so small that it can be ignored. Therefore, the model used is spatial autoregressive (SAR) panel model with a fixed effect and an 2-nearest neighbor distance weighting matrix. The result of this study show that the variables that have a significant effect on poverty in East Java from 2017 to 2020 are life expectancy rate, open unemployment rate, and average expenditure per capita a month.
Keyword: poverty, spatial panel model, spatial weighting matrix

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