Prediction of Local Government Revenue using Data Mining Method

Local Government Revenue or commonly abbreviated as PAD is part of regional income which is a source of regional financing used to finance the running of government in a regional government. Each local government must plan Local Government Revenue for the coming year so that a forecasting method is needed to determine the Local Government Revenue value for the coming year. This study discusses several methods for predicting Local Government Revenue by using data on the realization of Local Government Revenue in the previous years. This study proposes three methods for forecasting local Government revenue. The three methods used in this research are Multiple Linear Regression, Artificial Neural Network, and Deep Learning. In this study, the data used is Local Revenue data from 2010 to 2020. The research was conducted using RapidMiner software and the CRISP-DM framework. The tests carried out showed an RMSE value of 97 billion when using the Multiple Linear Regression method and R2 of 0,942, the ANN method shows an RMSE value of 135 billion and R2 of 0.911, and the Deep Learning method shows the RMSE value of 104 billion and R2 of 0.846. This study shows that for the prediction of Local Government Revenue, the Multiple Linear Regression method is better than the ANN or Deep Learning method. Keywords— Local Government Revenue, Multiple Linear Regression, Artificial Neural Network, Deep Learning, Coefficient of Determination

The Determinant Factors of Earnings Persistence

The purpose of this research is to provide empirical evidence about the effect of operating cash flow, sales volatility, cash flow volatility, operating cycle, and book tax difference on earnings persistence. The company used in this research is manufacturing company listed in Indonesia Stock Exchange (IDX) from 2016 until 2020. Samples of this research were selected based on the purposive sampling method and resulted in 43 companies, therefore the data used for this research amounting to 215 data. The data obtained from these samples were analyzed using multiple regression method. The result of this research show that operating cycle have influence on earnings persistence. While operating cash flow, sales volatility, cash flow volatility, and book tax difference have no influence on earnings persistence.

Religiosity and Halal Certification: Its Effect on Interest in Buying Traditional Food

This study aims to determine (1)—the influence of religiosity on the interest in buying traditional food of sate bandeng. (2). The effect of halal certification on the interest in buying traditional food of sate bandeng. And (3). The impact of religiosity and halal certification on interest in buying traditional food of sate bandeng. This research is the millennial consumers of traditional food of Sate Bandeng Kang Cepi Kaujon, Serang City, Banten Province. The research method used is quantitative. Methods of data collection using a questionnaire. Data were processed using SPSS version 23 software. Data analysis used the multiple linear regression method. The results of this study indicate that (1). Religiosity affects an interest in buying traditional food of Sate Bandeng. (2). Halal certification affects an interest in buying traditional food of sate bandeng (3). Religiosity and halal certification have a positive and significant impact on interest in buying traditional food of Sate Bandeng. Consumers’ interest in buying traditional food of Sate Bandeng is influenced by religiosity and halal certification as much as 48.8 percent. In comparison, the remaining 51.2 percent is influenced by other variables not examined in this study.

Expectations of individuals regarding the Ijara Forward contract: the case of healthcare financing

The health sector in Morocco is marked by many achievements, but also by large deficits, especially in terms of healthcare expenditures borne by individuals. With the introduction of Islamic banks (called participative banks) in Morocco, the study aims to determine the extent to which Ijara Forward, as an Islamic financial contract, is adapted to the expectations of Moroccans to finance their health expenditures.The study sample consisted of 200 individuals. The univariate and bivariate analyses are used to identify possible relationships between the study variables. In addition, this paper proposes a model that will predict the demand for Ijara Forward based on the logistic regression method. The results reveal that the financial characteristics of the Ijara Forward contract are in line with the financial expectations of Moroccan individuals. Furthermore, the cost of health services is the main factor that makes healthcare inaccessible. This factor influences the demand of Ijara Forward. In addition, this paper reveals that religious beliefs stimulate Ijara Forward’s demand and encourages people to pay a higher price for Ijara Forward.

Testing the Green Bond Premium in COVID-19 Pandemia

The purpose of this chapter is testing the existence of the green bond premium in the secondary market by using a most update data set involving the market developments in the pandemia times. The variables such as rating, sector, amount of the issue, maturity, and external review are balanced by using a matching procedure of a green bond with conventional bond issued by the same issuer. The ask-bid spread differential is regressed by using a panel regression method under fixed and random effects. The results of the analysis revealed that there exists negative premium of 39 basis points, and the green bond premium is more profound for USD denominated twins than for Euro ones as there exist a negative premium of 59 basis points for USD-denominated green bonds whereas it is -26 basis points for Euro-denominated bonds.