Judul: Modelling of Suicide Rates in Seventeen Countries using Panel Data Regression
Abstrak: Bunuh diri merupakan fenomena yang menjadi perhatian global dan salah satu indikator penting dalam Sustainable Development Goals. Bunuh diri terjadi dari interaksi rumit genetik, psikologis, ekonomi, dan faktor lainnya. Faktor-faktor tersebut saling menguatkan atau melemahkan terjadinya bunuh diri. Faktor penyebab bunuh diri dapat dikelompokkan menjadi faktor distal dan proksimal. Penelitian ini bertujuan mengetahui faktor penyebab yang mempengaruhi tingkat bunuh diri di tujuh belas negara pada tahun 2009–2019 menggunakan analisis regresi data panel. Data yang digunakan dalam penelitian ini merupakan data sekunder bersumber dari situs resmi World Bank, World Health Organization (WHO), Sustainable Development Solutions Network (SDSN), dan PEW Research Center (PRC). Hasil penelitian memperlihatkan bahwa tingkat religiusitas dan tingkat fertilitas mempengaruhi penurunan tingkat bunuh diri, sedangkan konsumsi alkohol perkapita dan tingkat pengangguran mempengaruhi peningkatan tingkat bunuh diri. Model pengaruh acak individu dengan ln(tingkat bunuh diri) sebagai peubah respon adalah model terbaik untuk data panel yang diteliti., Suicide is a phenomenon that is a global concern and is one of the important indicators in the Sustainable Development Goals. Suicide occurs from a complex interaction of genetic, psychological, economic, and other factors. These factors mutually reinforce or weaken the occurrence of suicide. Factors causing suicide can be grouped into distal and proximal factors. This study aims to determine the causal factors that affect suicide rates in Seventeen countries in 2009–2019 using panel data regression analysis. This study uses secondary data from the official website of the World Bank, World Health Organization (WHO), Sustainable Development Solutions Network (SDSN), and PEW Research Center (PRC). The results showed that the level of religiosity and the fertility rate affected the decrease in the suicide rate, while alcohol consumption per capita and the unemployment rate affected the increase in the suicide rate. The individual random-effect model with the ln(suicide rate) as the response variable is the best model for the panel data studied.
Keyword: suicide rate, panel data regression, proximal causal, the random-effect model

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: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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