Judul: Pembandingan  Metode Copula dan D-Vine Copula dalam Estimasi Tail Value at Risk pada  Komoditas Pertanian
Abstrak: Risiko adalah salah satu hal yang perlu dihitung investor untuk mengetahui perkiraan kerugian yang harus ditanggung. Salah satu alat ukur yang digunakan untuk menghitung risiko portofolio adalah Value at Risk (VaR). Namun, VaR memiliki kekurangan dalam hal subadditivity sehingga muncul konsep Tail Value at Risk (TVaR) yang memenuhi sifat koherensi ukuran risiko. Dalam penelitian ini, dilakukan penghitungan nilai VaR dan TVaR menggunakan data imbal hasil Crude Palm Oil (CPO), minyak kelapa, dan olein. Model ARMA-GARCH digunakan untuk mengatasi autokorelasi dan heteroskedastisitas pada komoditas-komoditas tersebut. VaR dan TVaR diukur dari portofolio model copula dan D-Vine copula. Fungsi copula yang terpilih yaitu copula-t dan copula Gauss. Setelah didapat sebaran bersama kemudian dilakukan pendugaan nilai VaR dan TVaR dengan Simulasi Monte Carlo yang selanjutnya digunakan backtesting untuk menguji kevalidan nilainya. Dengan memberikan bobot yang sama, pada tingkat kepercayaan 99% dengan copula diperoleh nilai estimasi TVaR sebesar 7.952167% dan dengan D-Vine copula diperoleh nilai estimasi TVaR sebesar 7.050056%., Risk is one of the things that investors need to calculate to find out the estimated losses that must be borne. One of the measuring tools used to calculate portfolio risk is Value at Risk (VaR). However, VaR has deficiency in subadditivity property, so the concept of Tail Value at Risk (TVaR) appears which satisfies the coherence property of risk measures. In this study, the VaR and TVaR values were calculated using the return data of Crude Palm Oil (CPO), coconut oil, and olein. The ARMA-GARCH model is used to overcome autocorrelation and heteroscedasticity effects in these commodities. VaR and TVaR were measured from the copula and D-Vine copula model portfolios. The selected copula functions are t-copula and Gaussian copula. After getting the joint distribution, VaR and TVaR values were estimated with Monte Carlo Simulation and then backtesting is used to test the validity of the values. By giving the same weight to each commodity, at a confidence level of 99% when using copula, the estimated TVaR value is 7.952167%, and using the D-Vine copula the TVaR estimate is 7.500056%.
Keyword: Tail Value at Risk, ARMA, GARCH, Copula, Drawable Vine Copula, Backtesting

Judul: Estimation of Portfolio's Value at Risk Using Nested Archimedean Copula
Abstrak: Risiko merupakan hal yang tidak dapat dihindari dalam berinvestasi. Salah satu metode yang dapat digunakan untuk mengukur risiko investasi adalah Value at Risk (VaR). Untuk meminumkan risiko, portofolio investasi dapat dibentuk dengan mengkombinasikan sejumlah aset. Saham dan emas seringkali memiliki sebaran yang tidak normal dan memiliki korelasi yang tidak linier, maka dari itu model ARMA-GARCH dan copula nested Archimedean digunakan untuk mengestimasi VaR karena tidak memerlukan asumsi kenormalan dan dapat mengatasi ketidaklinieran. Penelitian ini bertujuan untuk mengestimasi Value at Risk (VaR) portofolio yang terdiri dari dua saham dan komoditas emas menggunakan pendekatan metode copula nested Archimedean. Data yang digunakan adalah return mingguan saham BMRI, BBNI, dan komoditas emas dunia (XAU) dengan periode 1 Januari 2015 - 31 Desember 2019. Berdasarkan model copula nested Archimedean terbaik, estimasi VaR diperoleh dengan portofolio optimal yang terdiri dari 20% saham BMRI, 10% saham BBNI, dan 70% emas dunia sebesar 1,679% pada taraf nyata 10%., Risk is unavoidable in investing. One method that can be used to measure investment risk is Value at Risk (VaR). To minimize risk, investment portfolios can be formed by combining some assets. Stocks and gold often have non-normal distributions and non-linear correlations, therefore the ARMA-GARCH model and nested Archimedean copula are used to estimate VaR because they do not require normality assumptions and can overcome nonlinearity. This study aims to estimate the Value at Risk (VaR) of a portfolio consisting of two stocks and a gold commodity using the nested archimedean copula method approach. The data used is the weekly returns of BMRI, BBNI, and world gold commodity (XAU) for the period January, 1st 2015 – December, 31st 2019. Based on the best nested Archimedean copula model, VaR estimates are obtained with the optimal portfolio consisting of 20% BMRI shares, 10% BBNI shares, and 70% world gold commodity valued at 1.679% at the 10% significance level.
Keyword: Value at Risk, Portfolio, Nested Archimedean Copula

Judul: Photosensor producing based on ferroelectric material of Ba0,5Sr0,5TiO3 doped by Ferrium Oxide (Fe2O3) on a P-Type Silicon Substate Si (100) applied as a digital camera with a single pixel image sensor
Abstrak: Thin film Ba0,5Sr0,5TiO3 (BST) doped with Fe2O3 was grown on p-type silicon substrate (100) using chemical solution deposition (CSD). It used the solvent 2-methoxyethanol with annealing temperature of 850°C and the doping treatment Fe2O3 of 2.5, 5, 7.5, 10 % of the mass formed BST. Pure BST had the highest absorption of 600 nm of light wavelength. Additions Fe2O3 moves highest absorption in the range of 560 nm. Fe2O3 addition was strengthening thin film sensitivity to the effects of light. In general, doping Fe2O3 10% was result the best absorbance and current-voltage of BST thin film. Electrical properties of thin films BST include dielectric, photoconductivity, and dissipation energy. Based on the current-voltage (I-V) analysis, pure BST and doped BST showed photodiode properties. Dielectric values obtained in the range from 23.32 to 243. Based on electrical conductivity data and energy dissipation, BST thin films have shown photodiode characteristics and are therefore suitable as light sensors. The image sensor in a digital camera is also a light sensor. This BST thin film has been scientifically proven to be a light sensor and also it can be applied as an image sensor. This is a novelty image sensor based on thin film Ba0.5Sr0.5TiO3.
Keyword: barium strontium titanate, thin film, photodiode, image sensor, digital camera