International Journal on Advanced Science, Engineering and Information Technology
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Improvement of Dye Sensitized Solar Cells Efficiency Utilizing Diethyl Carbonate in PVA Based Gel Polymer Electrolytes
Get PDFLow conductivity of gel polymer electrolytes (GPEs) containing double iodide salts is critical for efficiency in dye-sensitized solar cells (DSSCs). The presence of diethyl carbonate (DEC) plasticizer affects the amorphousness and ionic conductivity of polyvinyl alcohol (PVA)-based GPEs and DSSCs performance. In this work, PVA-based GPEs containing a variation of DEC have been produced, characterized, and applied in the DSSCs fabrication. The structural properties of GPEs were analyzed using X-ray diffraction (XRD). The ionic conductivity was determined from electrical impedance spectroscopy (EIS). Based on XRD, GPEs for all prepared compositions have been identified as an amorphous phase. From the EIS measurement, it was found that GPE with the composition of 5.46 PVA - 8.19 EC - 10.92 PC - 60.06 DMSO - 5.73 TPAI - 5.73 KI - 1.34 I2 - 2.57 DEC (in wt. %) having highest conductivity of 11.19 ± 0.20 mS cm-1 with activation energy, Ea of 0.09 eV. The graph of conductivity versus temperature following the Arrhenius rule has been plotted. The GPEs dominate the highest conductive with 2.57% of DEC and showed the DSSCs efficiency of 6.42%. Common DSSCs parameters resulted in short-circuit current density (Jsc) of 17.58 mA cm-2, fill factor (ff) of 0.63, and open-circuit voltage (Voc) of 0.58 V. In conclusion, DEC improves the ionic conductivity as well as amorphous properties of the GPE, and therefore enhance the DSSCs’ efficiency
Suitability Area of Groupers (Serranidae) Cultivation in Floating Net Cage (FNC) System in Kepulauan Seribu
Get PDFAquaculture is one of the steps taken by the DKI Jakarta Provincial Government to manage fishery resources. Kepulauan Seribu, in recent years, has experienced a decline in the production of fresh fish; therefore, the cultivation of the Grouper floating system has become an alternative solution. Successful breeding is supported by site selection due to assembling on the terms of Groupers life that includes water quality and oceanography. Water quality parameters include salinity, pH, dissolved oxygen, and oceanographic parameters, that is, sea surface temperature, depth, and current velocity. The method of analysis in this research is descriptive spatial based on the overlay results of Landsat 8 image data processing and in situ measurement data during the field survey. The analyst statistics was descriptive to analyze the relationship between the region of conformity with the results of Grouper fish production. The results of the statistical and spatial analysis found that the location of floating net cages in the Kepulauan Seribu in the class is very appropriate and appropriate. Meanwhile, the relationship between regional suitability and Grouper production in the Kepulauan Seribu is weak because production is related to other driving factors
A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance
Get PDFThe Internet-of-Things (IoT) is an important technology and is considered the future of the Internet. Healthcare is described as one of the important areas in IoT used for remote patient monitoring. Real-time remote monitoring health applications are important as delays in data transfer between the cloud, and the application may be unacceptable. Fog computing refers to a geographically distributed computing system with several devices connected to the same network to achieve flexible and collaborative computation, storage, and communication services. Fog computing is mainly used for efficient data processing between sensors and cloud computing as it reduces the volume of data exchanged between sensors and the cloud, thereby improving the whole system’s efficiency. Wireless sensor networks (WSN) are also used in health monitoring systems to simultaneously transfer huge data volumes (of different priority levels and length values) to the fog computing system. Hence, there is a need to appropriately implement a task scheduling mechanism that can accurately prioritize tasks irrespective of their length. This study aims to systematically review the existing fog computing technologies in the Internet of things HealthCare (IoTH) systems and improve the performance of the available static task scheduling algorithms using the Tasks Classification (TC) method where task importance is paramount. The performance of the suggested approach was evaluated based on the Max-Min scheduling algorithm (SA)
Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery
Get PDFForest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area
Numerical Analysis of the Roof Slope Effect on the Building Thermal Comfort and the Need for Roofing Materials in Tropical Area
Get PDFRoof is the most affected building envelope element by local climate changes such as solar radiation, rain, wind, etc. The design of a building's roof will have a significant impact on the building's thermal conditions and comfort. This study aims to numerically analyze and optimize the slope of a gable roof on an 8 m × 12 m residential building with 3 m walls located in a tropical climate region. The parameter analyzed in this parametric study on galvanized steel gable roofs is the slope angle impact in the interval between 150 to 450, with an angle increment at 50. The thermal aspect of the analyzed building is modeled numerically using the TRNSYS simulation tool coupled with CONTAM for aerodynamic modeling. The results showed that the greater the roof slope angle, the more comfortable the room condition was due to the amount of heat release that occurred in the attic zone before penetration into the occupation zone. Otherwise, the greater the angle of inclination, the greater the roof geometry that leads to construction material addition for the frame and roof covering. Therefore, it is necessary to perform numerical analysis to determine the optimal slope of a gable roof that provides maximum thermal comfort in a room with low roofing material requirements. Analysis and optimization of convective heat dissipation from the attic zone through natural ventilation or infiltration to reduce indoor thermal gain is an outlook for further research
Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta
Get PDFDengue hemorrhagic fever (DHF) is a common disease in tropical countries such as Indonesia that is often fatal. Early predictions of DHF case numbers help reduce the risk of community transmission and help related authorities develop prevention plans and strategies. Previous research shows that temperature, rainfall, and humidity indirectly affect DHF spread patterns. Therefore, this research uses and compares three machine learning models—restricted Boltzmann machine-backpropagation neural network (RBM-BPNN), artificial neural network-genetic algorithm (ANN-GA), and artificial neural network-particle swarm optimization (ANN-PSO)—to predict DHF case numbers in DKI Jakarta, the capital of Indonesia, which is in the DHF red zone. RBM and PSO are used to calculate optimal initial weight and bias before starting the prediction stage with ANN; meanwhile, GA updates weight and bias during the backward pass in ANN. The data includes temperature, rainfall, and humidity, plus previous DHF case data for five districts in DKI Jakarta from Jan. 6, 2009, to Sept. 25, 2017. We used Arima, Autocorrelation, and Pearson correlation for pre-processing data. The DHF case data fluctuates strongly and requires the moving averages method. The data consists of 70% training data and 30% testing data. The results show that each district requires different model architectures for the best predictions. `The best RMSE prediction of DHF cases with RBM-BPNN in Central Jakarta is 3,78%; the best RMSEs using ANN-GA in North and East Jakarta are 5,65% and 5,99%, respectively. The ANN-PSO model had the largest RMSE value in every district, with an average of 8,43%
Optimization of Mutation Testing Challenges to Fixing Faults
Get PDFOne of the challenges of mutation testing is fixing faults. In the debugging phase, all live mutants were repaired. Programs need high mutation scores to be declared reliable program codes. Each mutation test can allow the identification of multiple mutants. This is what confuses the faults fixing process. The objective of this research is to get the shortest route so that it can help in sorting the mutant types during application improvement after testing. The optimization is needed considering the number of mutants in each mutation testing. The problems related to optimization are very complex. It takes a suitable method to find the shortest path by paying attention to each point. There are 30 projects chosen randomly. The operator mutations that are often killed when testing mutations are AOIU and COI. The proposed optimization for mutant repair sequence is the ant colony system (ACS). The route selection using the Ant Colony System algorithm resulted in route optimization of 1.528254. Meanwhile, if the genetic algorithm is used, the score is 1.767643. Optimization results are very helpful for developers in improving code in mutation testing. Research states the best order for handling mutants using ACS. This research can be further developed with the addition of class-level mutant cases which are produced using class mutation operators. Class mutation operators have different characteristics from traditional mutation operators. In particular, it requires changes to the program structure, such as the definition of class variables
Quality Requirements of Electronic Procurement System for Enhancing its User Experiences (UX)
Get PDFPresently, electronic (e-) procurement system is crucial for buying and selling supplies, and services between the government and individuals over the online environment. E-procurement is one of the Business to Consumer (B2C) applications that has the benefits of reducing transaction costs and supplier’s payment with increased information quality and accuracy of system. Nonetheless, users are still facing difficulties in using the e-procurement system thus causing them to feel dissatisfied. Moreover, studies related to user experience (UX) and e-procurement systems are also lacking in terms of UX interaction with this system. Users also face the problems such as lack of transparency and efficiency, corruption, and complicated procedures. There are other barriers faced by them such as prejudice, resource constraints and lack of experience. Therefore, this study aims to identify quality requirements of the e-procurement system which enhance their user experiences. This study uses a qualitative method (open-ended interviews) for data collection. The selection of the participants was done through purposive sampling and analyzed using a qualitative data analysis tool. The results revealed from interviews, that there are seven quality requirements that are important namely, effectiveness, efficiency, satisfaction, security, user interface aesthetics, ease of use and learnability. This study will be useful for system developers, and designers to put emphasis on these quality requirements for better user experience in enhancements of e-procurement in the future
Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia
Get PDFThis paper aims to develop an automatic system for pregnancy risk detection in Indonesia. The system requires a sophisticated approach to achieve the required performance as a sensitive field. Existing works are developed using small-sized datasets and limited classification features. Moreover, all features treated equally make the detection results hard to interpret which features contribute more. To address these issues, we propose to combine more complex features, data augmentation methods, and feature selection techniques. We prefer to use all 118 pregnancy indicators and 400 instances from Puskesmas as an original dataset. Next, the new datasets are used to build two data augmentation methods, i.e., GMM and CTGAN. Each data augmentation method generates 2,000 new synthetic instances. Following this, five machine learning methods combined with three feature selection approaches, i.e., RFE, Random Forest, and Chi-Square, are implemented in all datasets. Through experiments, we observed that feature selection techniques play an essential role in improving classification accuracies. While the GMM-based augmentation demonstrated performance improvement, the CTGAN-based synthetic dataset depicted low performances. The best accuracy on all experiment settings reached 95%. By using Random Forest combined with RFE on a GMM-based dataset, the highest accuracy was achieved using only five features. Another notable result is that both XGBoost and Decision Tree reached the same 95% accuracy on the GMM-based dataset on only nine features. The overall results show that appropriate data augmentation and feature selection are a matter for achieving better performance in this research
Identifying Water Pollution Sources Using Real-Time Monitoring and IoT
Get PDFWater is a natural resource essential for basic human life; however, water pollution is deteriorating in major water sources, such as rivers, seas, and lakes. This study evaluated and identified specific pollution sources owing to numerous industrial and other potential sources of pollution along the enormous length of a Siak river. A water pollution detection system was installed and deployed at river measurement stations having the potential to pollute, particularly near industrial sites releasing chemicals and wastewater. Data obtained from the system was analyzed using an algorithm to detect and assess any abnormal behavior change in the data over time. Six detection systems were deployed around the river, primarily in residential and industrial areas. As the studied river is one of the deepest in Indonesia, this research focused only on analyzing and identifying the sources of polluted water around Pekanbaru where many people, including water supply companies, utilize the river water. Water pollution sources were identified at sensor nodes two and four, which indicated through abnormal data that various types of material were present in the river and were detected using the sensors system. Several processes are required to improve the location data accuracy, e.g., improving the algorithm using training data, performing several iterations, increasing data from the sensor, and repeating the process many times to establish the source coordinate, as shown in the results