Judul: Modeling the Crime Rate in Indonesia Using Geographically Weighted Panel Regression Analysis
Abstrak: Kriminalitas merupakan salah satu masalah sosial ekonomi yang sampai saat ini belum terselesaikan di Indonesia. Meski Indonesia masuk kategori negara yang aman dikunjungi, kenyataannya masih banyak masyarakat Indonesia yang mengalami tindak kriminalitas. Penyelesaian masalah sosial ekonomi ini menjadi sangat penting karena menyangkut keamanan dan kenyamanan masyarakat. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang memengaruhi tingkat kriminalitas di Indonesia dan menentukan model terbaik dari setiap provinsi dengan membandingkan antara model regresi data panel dan model Geographically Weighted Panel Regression (GWPR). Data penelitian ini terdiri atas 34 provinsi di Indonesia dari tahun 2016 sampai 2020. Analisis yang digunakan adalah analisis regresi data panel dan GWPR. Hasil analisis menunjukkan model adaptive kernel gaussian GWPR merupakan model terbaik dengan R^2 sebesar 69,89% dan AIC sebesar 167,4585. Pemodelan GWPR menghasilkan persamaan model dan peubah berpengaruh signifikan untuk setiap provinsi. Secara umum terdapat lima peubah yang berpengaruh signifikan terhadap tingkat kriminalitas, yaitu persentase penduduk miskin, tingkat pengangguran terbuka, Produk Domestik Regional Bruto atas dasar harga konstan per kapita, indeks pembangunan manusia, dan rata-rata lama sekolah., Crime is one of the socio-economic problems that Indonesia has not yet resolved. Although Indonesia is categorized as a safe country to visit, in reality, there are still many Indonesian people who experience crime. The resolution of this socio-economic problem is very important because it involves the safety and comfort of the community. This study aims to identify the factors that influence the crime rate in Indonesia and determine the best model for each province by comparing the panel data regression model and the Geographically Weighted Panel Regression (GWPR) model. This research data consists of 34 provinces in Indonesia from 2016 to 2020. The analysis used is panel data regression analysis and GWPR. The result is that the adaptive kernel gaussian GWPR is the best model with R^2 of 69,89% and AIC of 167,4585. The GWPR modeling produces model equations and significant variables for each province. In general, five variables have a significant effect on the crime rate, namely percentage of poor population, open unemployment rate, Gross Regional Domestic Product at the constant price per capita, human development index, and mean years of schooling.
Keyword: crime, GWPR, panel data regression, spatial heterogeneity

Judul: Pemodelan Spasial Data Panel pada Tingkat Kriminalitas di Wilayah DKI Jakarta, Jawa Barat, dan Banten.
Abstrak: Kriminalitas di Indonesia pada tahun 2017-2020 didominasi oleh wilayah Metro Jaya dengan angka kriminalitas tertinggi pada tahun 2018 dan 2019 dan Sumatera Utara dengan angka kriminalitas tertinggi pada tahun 2017 dan 2020. Metro Jaya merupakan wilayah kepolisisan yang terdiri dari 13 polres di tiga provinsi: DKI Jakarta, Jawa Barat, dan Banten. Objek penelitian adalah 41 Kab/Kota di tiga provinsi tersebut. Penelitian ini bertujuan untuk menentukan model spasial panel terbaik pada tiga matriks pembobot (jarak invers, jarak eksponensial, dan tetangga terdekat) dan menemukan peubah yang berpengaruh terhadap tingkat kriminalitas pada 2017-2020 di wilayah tersebut. Peubah respon yang digunakan pada penelitian ini adalah tingkat kriminalitas dan peubah penjelas yang digunakan adalah persentase penduduk miskin, tingkat pengangguran terbuka, harapan lama sekolah, kepadatan penduduk, produk domestik regional bruto, rasio jenis kelamin, dan upah minimum per kab/kota. Hasil menunjukan bahwa model terbaik adalah Model Panel Tetap Spasial Galat Durbin dengan nilai AIC sebesar 533,321 dan R^2 terkoreksi sebesar 84,5%. Persentase penduduk miskin, kepadatan penduduk, produk domestik regional bruto, dan rasio jenis kelamin adalah peubah yang berpengaruh signifikan terhadap tingkat kriminalitas., Crime in Indonesia in 2017-2020 dominated by Metro Jaya with the highest number in 2018 and 2019 and Sumatera Utara with the highest number in 2017 and 2020. Metro Jaya is a police region consisting of 13 districts level police in three provinces: DKI Jakarta, Jawa Barat, and Banten. The object of research was 41 districts in those three provinces. This research aimed to determine the best spatial panel model on three spatial weight matrices (inverse distance, exponential distance, and k-nearest neighbor) and find the variables that affect the crime rate in 2017-2020 at said locations. The dependent variable used in this research was crime rate and the predictor variables were percentage of poverty, open unemployment rate, average school period, percentage of population density, gross domestic regional product, gender rate, and minimum wage rate per districts. The result showed that the best model was Spatial Durbin Error Fixed Effect Model that had AIC 533,321 and Adjusted – R^2 sebesar 84,5%. Percentage of poverty, percentage of population density, gross domestic regional product, gender rate, and spatial lag of gender rate were the variables that significant with the crime rate.
Keyword: crime, spatial panel analysis, spatial weight matrix

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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