International Journal on Advanced Science, Engineering and Information Technology
Not a member yet
2006 research outputs found
Sort by
Performance and Prospects of Mobile Rice Mill Unit
Get PDFRice milling is one of the important steps in post-harvest operations to get good quality white rice. As an innovation in improving customer service, a mobile rice mill unit (MRMU) has recently been operated in various rice-producing areas. The objective of this study was to evaluate the performance and prospects of MRMU based on technical and economic analysis. The research was conducted in East Lampung Regency, Lampung, Indonesia, by observing three MRMUs. Each mill was observed for three working days to obtain MRMU performance data, namely grain quality, milled yield, working time, fuel consumption, working capacity, and quality of white rice produced. Other information includes machine price, the machine age, estimated economic life, investment, interest rate, fuel consumption, operator wage, milling charge, and repair and maintenance costs. Results showed that MRMU had an actual capacity between 63.29–98.82 kg/hour with a milled yield between 60.41–64.96%. The white rice produced has a proportion of head rice 58.26–61.42%, with a whiteness index less than the SNI for rice quality standards. The unit cost of the rice milling process using MRMU was an average of 457.91 IDR/kg. At a milling charge of 666.67 IDR/kg, the operation of MRMU is economically feasible at an annual working hour higher than 1000 h. In addition, the MRMU operation was not economically acceptable at a milling charge of 500 IDR/kg. With the rapid growth in the rice milling numbers, an unbiased regulation is required to avoid unhealthy competition among the MRMU enterprises
Study of Caffeine as an Anticorrosion for API 5LX52 Steel in Geothermal Environment Simulation
Get PDFThe research objective was to study the thermodynamics and kinetics of caffeine as anticorrosion in geothermal environment simulations. The weight loss method following the ASTM G31, and the Electrochemical Impedance Spectroscopy (EIS) method following the ASTM G59-91. The data obtained from this study are the value of polarization resistance (Rp), solution-electrode interface (Cdl), the electrolyte resistance (Rs), corrosion current density (icor), corrosion rate (CR), and inhibition efficiency. The results obtained are caffeine has a higher inhibition efficiency at lower temperatures. The caffeine inhibitor efficiency of 90% was achieved at a caffeine concentration of 20 ppm in a 3.5% of NaCl + 500 ppm of H2S solution at a temperature of 70C, pH 4, and stirring speed of 250 rpm. The ∆Gads, ∆So, and Qads prices for caffeine are negative, while the Ea and ∆Ho prices for caffeine are positive. This shows that the adsorption strength of the caffeine molecule with iron (Fe) is good in the test environment. The nitrogen (N) atom in caffeine is considered to be alkaline as in ammonia (NH3) which can accept a proton to give NH4+. The minimum Cdl value that occurs at a caffeine concentration of 20 ppm, indicating a double layer at the steel interface with the solution, is considered an electric capacitor. This happens because there are four N atoms in the caffeine molecule. This research is useful for manufacturing green inhibitors in hydrogen sulfide environments such as geothermal environments
Determining Optimal Zone Radius of Zone Routing Protocol Based on Deep Recurrent Neural Networks in the Next Generation Wireless Backhaul Networks
Get PDFNext-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One of the essential innovations to maintain the wireless backhaul performance is combining the existing routing protocol technology and the deep learning concept. The concept of deep learning is gaining traction as a powerful way to add intelligence to wireless networks with complex topologies and radio environments. This is because conventional routing protocols do not learn from their previous experiences with various network anomalies. This paper proposed a predictive model of zone radius value using the deep recurrent neural network variant, namely the long short-term memory recurrent neural network (LSTM-RNN) algorithm. Determination of zone radius value conducted by measuring the whole of nodes routing zone using various network performance as input parameters such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Performance measurements such as mean square error (MSE), error distribution histogram, training state, regression, correlation, and time series response are gauged and compared for static and mobile node environments. Results showed that the proposed algorithm can accurately predict zone radius for both environments. However, the accuracy of the proposed algorithm is higher when implemented in a static node environment
Performance Analysis of 4-DOF RPRR Robot Manipulator Actuation Strategy for Pick and Place Application in Healthcare Environment
Get PDFDirect and indirect physical contact of humans and objects become the main medium of transmissible diseases such as COVID-19. Some strategies have been proposed to mitigate the risks of infections by minimizing physical contact, such as using robotics technology. Tele-robotics is one of the sub-fields in robotics that aims to implement physical surrogates for monitoring and controlling robots from remote distances, either autonomous, semi-autonomous, or manually guided. This paper discusses experimental research for evaluating the performance of a 4-DOF robot manipulator for pick and place tasks on small medical objects, such as test tubes in table-top scenarios. The robot manipulator is designed as an RPRR manipulator and is equipped with a gripper attached to its end-effector. Inverse kinematics and trajectory planning methods have been successfully implemented in real-time. The inverse kinematic method utilizes a pseudo-inverse Jacobian solver, and the trajectory generation utilizes a sigmoid function. The performance analysis results show that pick and place missions have been demonstrated with minimum tolerable position error, which is not more than 3.5 mm. The robot manipulator can satisfy high precision during repetitive experiments and maintain its accuracy in picking and placing standard test tubes from one rack to another within its working space. The smooth trajectories of the end-effector are achieved by implementing the sigmoid function. Thus, it satisfies the requirement for handling objects with minimum vibrations even during the actuation process with maximum speed
Various High Density Point Cloud Registration Results Analysis in Heritage Building (Penataran Temple, Penglipuran Village, Bali)
Get PDF3D modeling of functional buildings is growing rapidly because of its excellence. 3D modeling can also be applied to heritage buildings, but it is still rarely done due to the variety of detail, and the unique shape of the objects in heritage buildings make it more difficult to be modeled. In consequence, an adequate instrument with different scanning densities is required in this case, namely Terrestrial Laser Scanner (TLS) and Handheld Laser Scanner (HLS). This study, conducted at Penataran Temple in Penglipuran Village, Bali, aims to analyze the registration result of point clouds acquired by TLS and HLS, as well as its capability to build the 3D model. Point cloud data is acquired using TLS and HLS tools, and the information about the object is obtained by doing interviews with the Chief of Penglipuran Village and the villagers. The point cloud that TLS acquires is registered with Iterative Closed Point (ICP) algorithm, while the point cloud of HLS is registered by using Helmert and Affine transformation method. Based on the results, the registration of HLS point cloud data to the TLS Affine method has better quality accuracy than the Helmert method. In addition, point cloud data scanned by HLS can present 3D models with a better Level of Detail (LOD) than point cloud data from TLS
Adoption of Visual Programming Environments in Programming Learning
Get PDFProgramming education is gradually integrated into the school and university curricula. Accordingly, studies in the Computer Science education field have highlighted issues such as high failure rate, memorizing, bugs, the complexity of concepts, motivation, and uconfidence faced by students when learning a programming language, specifically object-oriented programming. These issues require specific learning environments to reach the target audience. Therefore, the objectives of this article are to identify the issues based on previous work and to verify those issues by interview feedback conducted with lecturers in the Department of Computer Science and Information Technology at the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia. Following that, the theoretical principles underpinning environments were studied to explore the suitability of these environments for university education based on the identified issues. The investigated environments included Turtle Graphics, Alice, BlueJ, Greenfoot, Snap!, and NetsBlox, a co-located collaborative block-based programming, and OOPP were studied and used to teach and learn object-oriented programming. Based on the interviews with experts, we found that students still had issues when learning programming, which involved memorizing, bugs, the complexity of concepts, unconfidence, communicating with students during a calamity (distance learning), and a number of students in labs. We conclude that these environments focus on some issues and ignore others, and no single environment satisfies all these issues, which causes students to be demotivated. In a further study, all these issues will be addressed by developing a learning environment, and its effectiveness shall be tested
Performance of Soybean Genotypes in the Acidic Dryland and Wetland
Get PDFThe agroecosystem for soybean cultivation in Indonesia is diverse, such as the acidic dryland and the wetland after rice cultivation. This research aims to evaluate the performance of seed yield and agronomic traits and identify soybean genotypes with good adaptation in the acidic dryland and wetland environments. Twelve soybean genotypes were evaluated for their yield and yield components in Lampung (dry land) and Banyuwangi (wetland). The result showed the similarity in the days to flowering and maturity between land types. The average performance of the plant height, number of branches, number of fertile nodes, and number of filled pods in the wetland was higher than in the acidic dryland. The average 100 seeds weight in acidic dryland and wetland were 13.00 g and 17.13 g, respectively. The seed yield in the acidic dryland and wetland were 2.12 t/ha and 3.37 t/ha, respectively. Based on the seed yield, there were three groups of adaptive genotypes. The first group consists of genotype adaptive in the acidic dryland (SPL-186, 2.85 t/ha), the second group consists of genotype adaptive in the wetland, namely (SPL-183, 3.59 t/ha), and the third group consists of genotypes adaptive in both land types (SPL-182 and SPL-181, 3.05 and 3.07 t/ha, respectively). The SPL-182 and SPL-181 maintained their high potential yield both in the acidic dryland and wetland, implying adaptable genotypes. Those genotypes are recommended to be developed in acidic dryland as well as the wetland. These findings pave the way for increasing soybean yield productivity
Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation
Get PDFA hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification
Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment
Get PDFConvolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information
Android Application Appy pie to support Students Writing Stories Skill Through Flipped Classroom Learning Models
Get PDFNowadays, millennials are required to be more active and interested in information and technology. Similarly, in the learning process, it is also recommended to use technology actively. One of the technology-based learning media is appy pie. Appy pie is one of the online application builders available on the internet. This research aims to develop an android application called Appy pie to support students' writing stories skills through flipped classroom learning models and to know the responses of teachers and students when using the application. This type of research is Research and Development (R&D) using ADDIE design, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The participants were 20 students in the fifth grade of elementary school. Based on the results of the research, it is found that the development of appy pie to support students' writing stories through flipped classroom learning model is assessed by three material and media practitioners to know the practicality of the product. The assessment results show that the average value of media practitioners is 95, and the average value of material practitioners is 84.67. The results show that the media is very feasible and effective to be used. The average student response to the assessment is 90.25, and the teacher response to the assessment is 96 with excellent criteria. Future research, application of pie media can be applied to all learning materials