International Journal on Advanced Science, Engineering and Information Technology
Not a member yet
2006 research outputs found
Sort by
Analysis of the Risk of Rubber Prices Using the ARCH-GARCH and VaR Methods on Farm Income in West Aceh District
Get PDFThe fluctuating price of rubber is often detrimental to farmers because farmers generally cannot manage the timing of the sale to get a profitable selling price. High price fluctuations provide opportunities for traders to manipulate price information on farmers and cause farmers not to enjoy the higher true price. This research aims to see how risky the price is received by rubber farmers and its effect on farm income in West Aceh District. This study uses the ARCH-GARCH method and VaR calculations for price risk analysis and Simple Linear Analysis to analyze the effect of price on farm income. The results showed that the price risk received by rubber farmers was high, namely 41.699% during the one-year sales period. The regression results show that the price significantly affects the income of rubber farming in West Aceh Regency with a probability of 0.000. The value of R2 is 0.935, which means that rubber prices influence 93% of rubber farming income, and variables outside the model influence 7%. The selling price of rubber influences the income of rubber farming. If the price of rubber is low, the farmers cannot afford to pay for rubber maintenance, thus disrupting rubber productivity. Decreased productivity will reduce farm income in the West Aceh Regency, which means that the price of rubber influences 93% of rubber farming income, and 7% is influenced by variables outside the model
Classification of Air-Cured Tobacco Leaf Pests Using Pruning Convolutional Neural Networks and Transfer Learning
Get PDFConvolutional Neural Network (CNN) usually uses a large image dataset with many parameters. Small datasets require a small number of parameters. Existing standard (pre-trained) models such as Alexnet, VGG, Inception, and Resnet have been tested with high accuracy but have many parameters. For small datasets, too many parameters become less efficient and increase computation costs. The high computational costs make the model unsuitable for computers with limited resources such as embedded devices and mobile phones. This research proposes pruning on the depth of resnet50 architecture and adds a dimensionality reduction layer after the pruning point. This approach does not require a complex pruning criteria algorithm, so it is easy to implement. Resnet50 was chosen because it is a good performance with batch normalization and skip connections. We use transfer learning for Resnet50 weight. Pruning is carried out at a depth of the network by cutting at the layer of the activation function. Several pruning points were selected to produce several models with certain parameters. The more networks layer pruned, the smaller the number of parameters produced. We add a layer for channel reduction after pruned network to reduce the number of feature maps before entering the fully connected (FC) layer as a classifier. We retrained a new network using a 2000 tobacco leaf pest dataset split into 1600 training and 400 validation images with 4-classes. The result shows that the accuracy could be maintained equal to the unpruned network up to 100% accuracy and 74.38% reduction rate for the number of parameters. A higher reduction rate of the number of parameters up to 90.62% still provides high accuracy of validation data around 99.3%. These prove that our proposed method effectively maintained accuracy and reduced the number of parameters
Investigation on Billets and Tools Geometry in Cold Forging of the Straight Bevel Gear
Get PDFStraight bevel gears are an essential component of mechanical transmissions; they are widely used in the automotive, aerospace, shipbuilding industries and are made by shaping methods such as metal forming, casting, or machining. To market products, with proper function and properties, at a low cost, this gear component is usually fabricated by precise cold forging (the billet is forged in a closed-die at room temperature). In cold forging, the geometry of the billet and the forming tool plays an important role. It determines the ability to fill the die cavity, creating a finished product profile that meets the required geometric parameters. In this study, by 3D numerical simulation, some geometric shapes of workpieces and tools were investigated to find the optimal parameters. The results obtained from the simulation method were determined that a cylindrical workpiece with a tapered end and a die bottom with a convex profile will increase material flow velocity, improving cavity filling, uniform distribution stress in the forming specimen, and forging products without defects. Experimentally also verified the simulation results, which were cold forging with optimized workpiece and tool geometry, the straight bevel gear part was fully shaped, ensuring geometrical accuracy. The result of this study is a suggestion to apply to the design of a cold forging die for similar features
Protein Isolate of Jack Bean Tempeh (Canavalia ensiformis) by Spray Drying Method with Variation of Inlet Temperature
Get PDFProtein isolation from beans is commonly carried out to increase protein availability and digestibility. Protein isolates made through the spray drying process have functional properties and characteristics. The variation of spray drying inlet air temperatures affects the properties of jack bean tempeh protein isolate (JTPI). The protein of jack bean tempeh was extracted and isolated using the method of the isoelectric point approach. The isoelectric point of jack bean tempeh was determined at a pH of 4.20 using the turbidimetry method. The research aimed to identify different inlet spray drying air temperatures on the physical characteristics and functional properties of JTPI. The spray drying method of JTPI was carried out using variations in inlet temperatures were 140°C, 150°C, and 160°C, and then moisture content, protein content, water holding capacity (WHC), and microstructure by Scanning Electron Microscopy were determined. The results showed that the 150°C inlet air temperature variation gave the lowest value for JTPI moisture content (3,91±0.04%). In comparison, the 160°C inlet air temperature variation gave the highest value for JTPI protein content (49.6±0, 30%) and JTPI water holding capacity (3.89±0.03 ml/g). The microstructure of JTPI obtained was porous, with a more spherical shape found at lower inlet temperature but wrinkled at the higher inlet temperature. The inlet temperature also affects the particle size JTPI. The inlet temperature of 160°C can be carried out to produce JTPI, which requires both high protein content and water holding capacity
Overview of Applied Data Analytic Mechanisms and Approaches Using Permissioned Blockchains
Get PDFBlockchain technology deployment has surged in diverse domains to secure and maintain valuable data. Wherever valuable data exists, the motivation of applying analytics emerges. However, this case is slightly different since it deals with a distributed system environment with security constraints such as privacy and confidentiality. This study aims to provide an overview of approaches that applied analytics over permissioned blockchains. Moreover, extract key features from these studies to report and discuss common features and best practices. This contributes to determining the requirements to apply analytics and outlines the remaining challenges. The research method was conducted in four phases. The initial phase states the goals and objectives. Subsequently, the analysis phase examines a group of research papers to extract key features from various studies. These features were divided into three categories: general aspects, data management, and an analytics perspective. Afterward, the outcomes are classified according to the findings and observations to point out common aspects and best practices. Finally, the evaluation of the research determines the requirements to apply data analytics over permissioned blockchains. Based on the findings and observations of these research papers. Most of the studies focused on off-chain analytics with the assistance of a third party. Also, most of the analytics types were descriptive and diagnostic, whereas fewer studies proposed predictive analytics. This explains the lack of existing approaches that use artificial intelligence and real-time analysis. The most used blockchain platform for analytics was Hyperledger fabric for multiple reasons mentioned in detail in this research
GIS-Based Binary Logistic Regression for Landslide Susceptibility Mapping in the Central Part of Pacitan Regency, East Java, Indonesia
Get PDFLandslides are among the most hazardous phenomena in the Pacitan Regency, especially in the Sub-Districts of Pacitan, Kebonagung, Tulakan, and Arjosari, where the landslide mainly occurs. Strategic planning through GIS analysis can be applied to minimize potential losses and strengthen resilience to natural disasters. This study combined the binary logistic regression method and GIS to map the landslide susceptibility in the Sub-Districts of Pacitan, Kebonagung, Tulakan, and Arjosari, Pacitan Regency, East Java, Indonesia. An inventory map of 293 landslides was randomly divided into 80%-20% basis for model training and testing. Fourteen landslide conditioning factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), land use, proximity to roads, proximity to rivers, proximity to faults, soil types, lithology, normalized difference vegetation index (NDVI) and rainfall was used. Analysis shows that fourteen landslide conditioning factors are contributed to 22.7%. The analysis shows that 36.59% or 17,734.95 Ha of the study area has high-very high susceptibility. The area of high-very high susceptibility is mainly located in the western part of the study area. It is related to high slope value and volcanic and sediment-volcanic rock from the formation of Arjosari and Mandalika. The validation using AUROC showed an excellent fit of 0.806. Validation of susceptibility map using testing data showed 0.711 accuracy value and 0.694 precision value, which meant that the susceptibility model was quite sensible. This information could be helpful to support the local government for hazard mitigation efforts
Defining Teamwork Productivity Factors in Agile Software Development
Get PDFTeamwork productivity plays a substantial role in attaining successful projects in agile software development. For improving agile software development, it is necessary to look at many factors that influence agile teamwork productivity. Thus, there is a need to identify these influential ones among factors. Identifying these influential factors affecting agile teamwork productivity can enable the teams to pertain to where they need to enforce the elbow grease to improve productivity. Teams in software organizations will improve their productivity by considering these teamwork factors in agile software development. In this respect, the classification of the teamwork factors that might cause an influence on the productivity of the agile software development teams becomes the indication of divergence for the choice and characterization of enhancement approaches. We carried on a systematic literature review to execute such analysis in which we included 53 primary studies. The systematic literature review aimed to identify and classify the factors influencing teamwork productivity in agile software development. As a result of the systematic literature review, we identified 77 influential factors and classified these factors into technical, non-technical, organizational, environmental, project management, and user requirements level factors that affect teamwork productivity in agile software development. Based on this data, software organizations can mend the teamwork productivity of their teams by appraising the impact factors that best fit their context
The Evolution of Cyberattack Motives
Get PDFA cyberattack can be defined as an action aiming to cause damages and losses to computer networks, information systems, and even personal devices and data. Many professionals and organizations have put a lot of effort and resources into preventing cyberattacks based on how they occur, their targets, and what damages they can cause. However, one of the aspects that are often overlooked and one of the reasons that cyberattacks are successfully carried out is the fact that the nature of attackers' motivations is not fully understood. Therefore, this research examines the main reasons for cyberattacks to be carried out by adversaries and the motives behind cyberattacks. Specifically, we studied over 7,700 cyber records and events between 2006 and 2018, including data breaches, privacy violations, and cyber incidents, to learn how attack motives have evolved over the years. The analyses of the data were mainly carried out using descriptive analysis. Our study found that the early cyberattacks were mainly financially motivated. However, in the later years, the cyberattack motives included espionage, ideology, and skill and knowledge testing. This implies that the motives behind cyberattacks became more varied in terms of types, proportions, and correlations between them. It is hoped and expected that the results of the analyses will be helpful to various stakeholders in such a way that they will better understand the reasons and motivations for cyberattacks
Estimation of Hourly Solar Radiations on Horizontal Surface from Daily Average Solar Radiations Using Artificial Neural Network
Get PDFHourly solar radiation information is necessary to increase the effectiveness of photovoltaic (PV) energy systems. However, compared with daily average solar radiation, the measurement of hourly solar radiation is much less available. In this study, a multilayer perceptron artificial neural network based on daily average solar radiation is proposed to estimate the amount of hourly solar radiation. The proposed method also relies on the location of the PV system, the hour when the estimate is needed, and the month when the estimate is needed. Two separate networks were built to estimate hourly direct solar radiation and hourly diffuse solar radiation, the summation of which is the estimated value of global hourly solar radiation. The networks were trained using three years of data from 2016 to 2018 from five locations around Japan. The data were taken from the website of the Japan Meteorological Agency (JMA). The number of hidden neurons for each network was determined by comparing the regression value obtained during the training process. The proposed method was validated by comparing the estimated value with the actual measured solar radiation value for each month in 2019 for the same five locations used for training the networks. The reliability of the proposed method was confirmed with the minimum R2 of 0.951 for the estimation of hourly direct solar radiation and 0.983 for hourly diffuse solar radiation. Further improvement in the accuracy of estimating the daily peak radiation value is considered to improve the total accuracy of the estimation model in the future
A Revisit of the Energy-Economy-Environment Nexus with Multi-Regression
Get PDFEconomic development leaves its residues on the environment, then it is believed as the cause of environmental damage. Recognizing the real cause of environmental damage during the development process is crucial, as it could prevent the government from using dirty energy sources in developing the economy. This study believes that the real cause of environmental degradation is energy consumption. Considering the importance of the energy-economy-environmental nexus where energy is hypothesized as the driver of the two–the economy and the environment, this study conducts a multi-regression analysis where the economy and environmental degradation are the dependent variables affected by energy consumption as the independent variable. Thus, the study aims to investigate whether energy consumption is the real driver of the economy and environmental degradation by comparing energy consumption impacts on both. The sample was all countries (world and economies group) from 1990-2013. The economy's elements expected to contribute to CO2 emissions (FDI, Trade, Urban population) are also under investigation. The results show that the energy coefficients are always positive and have the largest value in almost all models, indicating that energy drives the economy and environmental quality (represented by CO2 emissions). Following the second hypothesis, the impacts of Urban population, FDI, and Trade on CO2 emissions depend on the development level of the three variables. This study is expected to make the policymakers aware that the energy type they choose could improve the economy and environmental quality or put both as a trade-off