Environmental problems are becoming an issue along with the issue of global warming whose impacts are increasingly being felt. Cases of environmental destruction in Indonesia are increasing, so several directions of government policies have started toward sustainable development goals (SDGs). The situation has challenged Islamic finance, an alternative financial system that is said to be the answer to the current toxic financial system, to prove itself to support the environment quality. This research aims to determine the impact of Islamic financial development, Gross Domestic Product (GDP), and population on the environmental quality in Indonesia using data spanning from 2016 to 2020. The results show that GDP significantly impacts the environmental quality of almost all major islands in Indonesia. Overall, in the territory of Indonesia Islamic financial development had a significantly negative effect at a significant level of 10% on environmental quality.

PT. Bintang Mitra Semestaraya Tbk is a subsidiary engaged in investment and trading. The company started as an investment company investing in real estate companies in developing basic housing projects, mid-range residential projects and companies developing commercial buildings. This study aims to determine the volume of shares in the company PT. Bintang Mitra Semestaraya Tbk during the Covid-19 pandemic. The stock data is simulated from 2020, 2021 and 2022 in daily form. This research only focuses on discussing the movement of stock values ​​during the Covid-19 pandemic and looking for some data related to Missing Value (missing or incomplete data) in the company's stock data using the imputation method. The correlation between data variables in simulations 1-8 as a whole has a significant correlation with the percentage of truth/trust in this study of 95%. Furthermore, in the regression model, the best model is seen from the parameter data, the smallest RSE is in simulation 6 and the largest RSE is in simulation 3   PT. Bintang Mitra Semestaraya Tbk adalah anak perusahaan yang bergerak di bidang investasi dan perdagangan. Perusahaan tersebut dimulai sebagai perusahaan investasi yang berinvestasi di perusahaan real estat dalam mengembangkan proyek perumahan dasar, proyek perumahan kelas menengah dan perusahaan yang mengembangkan bangunan komersial. Penelitian ini bertujuan untuk mengetahui volume saham pada perusahaan PT. Bintang Mitra Semestaraya Tbk di masa pandemi Covid-19. Data saham tersebut disimulasikan dari tahun 2020, 2021 dan 2022 dalam bentuk harian. Penelitian ini hanya berfokus membahas tentang pergerakan nilai saham selama masa pandemi Covid-19 dan mencari beberapa data terkait Missing Value (data hilang atau tidak lengkap) yang ada pada data saham perusahaan tersebut menggunakan metode imputasi. Korelasi antara variabel data pada simulasi 1-8 secara keseluruhan memiliki korelasi yang signifikan dengan persentase kebenaran/kepercayaan dalam penelitian ini sebesar 95%. Selanjutnya pada model regresi terdapat model terbaik yang dilihat dari data parameter RSE terkecil berada pada simulasi 6 dan RSE terbesar berada pada simulasi 3

This study aims to apply time series graphs on stock of Astra Agro Lestari Tbk. and Anadolu Group with last observation carried forward (LOCF) imputation. The imputation was used because the data for the two companies had missing values on several dates. Missing value contained in the company Astra Agro Lestari Tbk. in Indonesia more than Anadolu Group in Turkey because of the difference in the number of holidays. Original data and data with complete dates are combined to form new data where missing values are seen on certain dates. The function used in the R program to form the graph is xts. However, the Date variable has a character class so it needs to be changed to the Date class. The xts function will error if the class is not changed. The modification also causes the horizontal axis of the graph to be replaced by the date. Based on the chart of stock prices and transaction volume of stock of the company Astra Agro Lestari Tbk. and Anadolu Group experienced increases, decreases, and is constant on several dates. Keywords: missing value, R programming, stock prices, transaction volume. MSC2020: 62M10, 91B84, 62-04

This research aimed to form a high-order spatial weighting matrix based on various simulations. The simulation was the determination of the center of the country based on the capital and google trend data. The keywords that have been used in the Google Trends data are "gold price" and "deposit". These keywords have been translated into 6 official languages of the United Nation including Arabic, Chinese, English, French, Russian, and Spanish. Each language has been represented by 1 country. The determination of the country center that has been used based on the capital as well as keywords and time influenced the form of the high-order spatial weighting matrix. In simulations 1, 2, 4, and 5 the highest spatial order formed was 6. It was different with simulations 3, 6, and 7 the highest spatial order formed was 5. Keywords: language, simulation, gold price, deposit.

Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.