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

Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

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.

The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia. Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.

Climate trends and climate change scenarios in Ho Chi Minh City

This paper reviews the trends of climate and climate change scenarios in Ho Chi Minh City (HCMC). The linear regression method is used to determine the trend and variation of past climate (1980-2019) at Tan Son Hoa station. The annual average temperature tends to increase about 0.024°C/year (r2=0.54) and the rainfall tends to increase about 6.03 mm/year (r2=0.67). For temperature scenario, by 2030 the annual average temperature in the whole city will increase from 0.80- 0.81°C (RCP4.5) and 0.92-0.98°C (RCP8.5). By 2050, it will increase 1.23-1.33°C (RCP4.5) and 1.55-1.68°C (RCP8.5). By 2100, it will increase 1.75-1.88°C (RCP4.5) and 3.20-3.55°C (RCP8.5) compared to the base period. Regarding rainfall scenario, in 2030, the city-wide average rainfall will increase by 12-21% (RCP4.5) and by 12-17% (RCP8.5). By 2050, the average rainfall is likely to increase by 13-15% (RCP4.5) and 15-17% (RCP8.5). By 2100, the average rainfall is likely to increase by 18-22% (RCP4.5) and 20-21% (RCP8.5) compared to the base period.

Optimization of Raw Material Inventory Control CV. Dirga Eggtray Pinrang Using Probabilistic Model with Backorder and Lostsales Condition

This research discusses about the comparison of raw material inventory control CV. Dirga Eggtray Pinrang. It starts with forecasting inventory for the next 12 periods using variations of the time series forecasting method, where the linear regression method provides accurate forecasting results with a Mean Absolute Percentage Error (MAPE) value of 1,9371%. The probabilistic models of inventory control used are the simple probabilistic model, Continuous Review System (CRS) model, and Periodic Review System (PRS) model. The CRS model with backorder condition is a model that provides the minimum cost of Rp. 969.273.706,20 per year compared to another probabilistic model with the largest difference of Rp. 1.291.814,95 per year, with the optimum number of order kg, reorder level kg, and safety stock kg.

Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks

In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method.

PENGARUH EKSPOR MIGAS, PENANAMAN MODAL ASING, UTANG LUAR NEGERI, DAN INFLASI TERHADAP PERTUMBUHAN EKONOMI INDONESIA

This study aims to analyze the partial and simultaneous effect of oil and gas exports, foreign investment, foreign debt and inflation on Indonesia's economic growth in 1975-2019. The research method uses a quantitative approach which will be explained associatively. The data analysis used is multiple linear regression method as an econometric tool to describe the characteristics of a sample or observed location with the help of SPSS 26 for windows. The results of the study prove that partially oil and gas exports, foreign debt, and inflation affect Indonesia's economic growth. Meanwhile, foreign investment has no effect on Indonesia's economic growth. Simultaneously, the variables of oil and gas exports, foreign investment, foreign debt and inflation affect Indonesia's economic growth. This is supported by the R2 value of 0.599 which means that 59.9 percent of the variation in economic growth is influenced by oil and gas exports, foreign investment, foreign debt and inflation, while the remaining 40.1 percent is influenced by other factors not included in the model.