Transmission of Special Autonomic Funds in the Economy through Mediation Variables

This study analyzes the effect of special autonomy funds on physical infrastructure, education, health, and poverty as well as its effect on economic growth in Aceh Province. The effect seen is the direct or indirect effect that occurs between the realization of special autonomy funds on economic growth in Aceh Province. The physical infrastructure variables represented by the length of the road, education represented by the average length of schooling, health represented by life expectancy, and poverty represented by the percentage of poor population were intervening variables. The intervening variable is a variable that is considered capable of mediating between the independent variables and the dependent variable. The analysis model used in this study is path analysis so as to be able to see the direct and indirect effects of an independent variable on the dependent variable. The results found in this study are the realization of special autonomy funds has a direct effect on economic growth. While the indirect effect is given by the variable realization of special autonomy funds on economic growth through the length of the road, life expectancy, and the percentage of poor people. The variable of average length of schooling does not have an indirect effect between the realization of special autonomy funds on the economic growth of Aceh Province.

Potential classification of Smart Village – Smart Economy with Deep Learning methods

Development gap in the city and village is still happening on Indonesia. It happened because of the massive urbanization factors. Poverty in the Indonesian villages are relatively higher than on the urbans. In order to reach the maximal city development, Ministry of Village, Development of Disadvantaged Regions and Transmigration of Indonesia created a sustainable village development program namely Village’s Sustainable Development Goals (SDGs) and optimized the village potential data. This study aimed to design the smart village – smart economy classification system by using deep learning methods on village potential data on Indonesia at 2020. The method used in this study is data mining processes namely KDD (Knowledge Discovery and Data mining). The result in this study showed the best models were obtained which consisting of 2 hidden layers and each layer is 128, 128 layers which using target class from the process of calculating the score is able to reach 94.93% of the accuracy from the training process and 96% on the testing process and succeeded to classify the potentials of smart village – smart economy.

The Effect of Energy Consumption, Energy Resources, Economic Growth, and Road Infrastructure on Co2 Emissions in Indonesia

This study aims to analyze the effect of energy resources, energy consumption, and road infrastructure on economic growth and their effect on CO2 emissions in Indonesia. This study uses time series data in Indonesia for the period 2000 to 2019 and the analytical model used is the Auto Regressive Distributed Lag (ARDL) model. The results found in this study are variables that have a significant effect on economic growth in the short term are road infrastructure in the same period, in the previous period, as well as in the previous 2 periods and resources. Meanwhile, the ones that have a significant effect in the long term are road infrastructure and energy resources. Variables that have a significant effect on CO2 emissions in the short term are road infrastructure, energy consumption in the previous period, economic growth in the previous period, energy consumption and energy resources. While the variables that influence in the long term are economic growth and energy resources.

Principal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting

Drought is a serious problem that often arises during the dry season. Hydrometeorologically, drought is caused by reduced rainfall in a certain period. Therefore, it is necessary to take the latest actions that can overcome this problem. This research aims to predict the potential for a drought to occur again in the Kupang City, Indonesia by developing a rainfall forecasting model. Incomplete daily local climate data for Kupang City is an obstacle in this analysis of rainfall forecasting. Data correction was then carried out through imputed missing values using the Kalman Filter method with Arima State-Space model. The Kalman Filter and Arima State-Space model (2,1,1) produces the best missing data imputation with a Root Mean Square Error (RMSE) of 0.930. The rainfall forecasting process is carried out using Statistical Downscaling with the Principal Component Regression (PCR) model that considers global atmospheric circulation from the Global Circular Model (GCM). The results showed that the PCR model obtained was quite good with a Mean Absolute Percent Error (MAPE) value of 2.81%. This model is used to predict the daily rainfall of Kupang City by utilizing GCM data.

Robust Optimization for Food Supply Chain Management Problems: A Critical Review and its Novelty

Optimization problems in real life often have problems with data that cannot be known precisely; constraints on the data are commonly referred as errors. This kind of data is called uncertainty. This uncertainty problem can be solved using Robust Optimization (RO). RO is growing rapidly with the participation of various kinds of research, especially the supply chain (distribution of food or goods between regions). It can be seen that RO is very active in providing support and contribution in various aspects of life by providing optimal results for an objective function and dealing with existing limitations and data uncertainty. This article discusses the background of the problem and the purpose of creating an article, provides an overview of bibliometric map analysis methods and discusses literature and studies. Critical review from OR database articles for supply chain problems are used as a reference, so at the end, it can be determined what novelty is an opportunity for further research.

The Effects of Monetary Variables on the Growth of Small and Medium Industry in Aceh Province

This study aims to analyze the effect of inflation, bank credit, and SMI investment on the growth of small and medium industries in Aceh Province. This study uses panel data consisting of 23 districts/cities in Aceh Province during the period 2014 to 2020. The analysis model used in this study is a panel data regression model. The results found in this study are variables that have a significant effect on the growth of SMIs in Aceh Province are inflation and investment in SMIs. Meanwhile, the banking credit variable has not had a statistically significant effect on the growth of SMIs. The inflation variable has a negative and significant effect on the growth of SMIs so that uncontrolled inflation will have a negative impact on the growth of SMIs. Meanwhile, SMI investment has a positive and significant impact on the growth of SMIs so that various targeted investment policies are needed so that they can support the development of SMIs in Aceh Province.

Numerical Integration Implementation Using Trapezoidal Rule Method To Calculate Aproximation Area Of West Java Province

An area can be shaped into a regular shape or an irregular shape. There is an area of irregular shape which is restricted by an unknown function, to determine that area must use a numerical integration. One of numerical integration methods is Trapezoidal Rule by replacing (????) with an integral approach function which can be evaluated, then let the (????) approximated by a linear polynomial in the certain interval, denoted as closed interval . This study is going to calculate the area of West Java Province by using this method with several different number of partitions in each quadrant such as, 9 partitions, 11 partitions, and 36 partitions in for different quadrants. This study provides the final result of the approximate area which will be compared with the actual area based in the error of result. The main finding is the approximate total area will be closer to the actual area followed by the increasing number of partitions.

Production Technology for Adding GDL (Glucono Delta Lactone) to Soy-Based Foods

The technology of adding Glucono Delta Lactone (GDL) to food made from soybeans is gaining popularity because it has many advantages. GDL is an acid that functions to coagulate proteins. GDL is a food additive that is Generally Recognized as Safe (GRAS). GDL has been applied in soy-based products such as tempeh and tofu. The use of GDL in tempeh products can reduce the acidification time of tempeh to 2-3 hours so that the production capacity of tempeh can increase significantly and reduce the amount of water used in the production process. The use of GDL in tofu is as a coagulant which makes the quality of tofu better than other agglomerates. The main objective of this paper is to provides an explanation of the application of GDL to soy-based foods. So, GDL can be used as an innovation to develop soybean-based food industries. It begins from describing about GDL, provides comparison between natural soy-based food and soy-based foods with GDL from study literature. Then moves to the application of GDL in tempeh, the application of GDL in tofu, and Back-Slopping technology.

Bifurcation Analysis and Electronic Circuit for Sprott Jerk System

In this paper, the Sprott jerk system based quadratic function is presented. The dynamics of this system is revealed through equilibrium analysis, phase portrait, bifurcation diagram and Lyapunov exponents. The Sprott system can exhibit a chaotic attractor, which has complex dynamic behavior. Finally, the circuit implementation is carried out to verify the Sprott Jerk system. The comparison between the MATLAB and MultiSIM simulation results demonstrate the effectiveness of the Sprott system.

Application of Artificial Intelligence in Modern Ecology for Detecting Plant Pests and Animal Diseases

Climate change could lead to an increase in diseases in plants and animals. Plant pathogens have caused devastating production losses, such as in tropical countries. The development of algorithms that match the accuracy of plant and animal disease detection in predicting the toxicity of substances has continued through a massive database. Data and information from 10,000 substances from more than 800,000 animal tests have been carried out to generate the algorithms. Plant and animal disease detection using artificial intelligent in the modern ecological era is important and needed. Diseases in animals are still found in several Ruminant-Slaughterhouses. The purpose of the study is to identify the leverage attributes for using of Artificial Intelligent (AI) in detecting plant pests and animal diseases. The use of Multidimensional Scaling (MDS) produces a leverage attribute for the use of AI in detecting plant pests and animal diseases. The results showed that leverage attributes found were: Prediction of the presence of proteins structures produced by pathogens with a Root Mean Square (RMS) value of 4.5123; and Plant and Animal Disease Data will be opened with an RMS value of 4.2555. The findings of this study in the real world are to produce the development of smart agricultural applications in detecting plant pests and animal diseases as an early warning system. In addition, the application is also useful for eco-tourism managers who have a natural close relationship with plants and animals, so that ecological security in the modern ecological era, can be better maintained.