JOIV : International Journal on Informatics Visualization
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Prototype of Integrated National Identity Storage Security System in Indonesia using Blockchain Technology
Get PDFApproximately 29 institutions in Indonesia issue were identifying numbers, such as ID cards, driving licenses, BPJS, etcetera. In general, the identity storage system is designed with a centralized system and managed by each government agency. However, this system has some disadvantages, like data replication and redundancy. Furthermore, the Indonesian government is now undertaking a program through the Ministry of Home Affairs to use population data for public services by providing access to organizations cooperating for population data use. With a centralized database managed by a single entity, data abuse can occur and rely on third parties, the sole authority of the national identity data. The blockchain-based solution described in this paper to integrate a national identity system can provide the advantages of a population data utilization program. The system designed can facilitate convenience in sharing and updating population data while also ensuring the security and integrity of the population data. The citizens do not have to worry about the possibility of data misuse by user institutions. Blockchain technology offers decentralization through the participation of members across a distributed network. There is no single point of failure, and no single user may alter the transaction record. Our proposed approach could help the government of Indonesia secure citizens' private information and increase transparency in information management
Design on Novel Door Lock Using Minimizing Physical Exposure and Fingerprint Recognition Technology
Get PDFDigital door locks are widely used not only in general homes such as houses and apartments, but also in spaces where external intrusion must be prevented based on high security and convenience. Recently, smart door locks with additional technologies such as fingerprint recognition and Bluetooth communication have also been developed, and the door lock market is on the rise. Digital door locks are more convenient to use compared to the existing key-type door locks. However, there are often cases of exploiting security vulnerabilities such as exploiting and invading the user's trace remaining on the door lock. This paper proposes a door lock with a structure that can complement the shape of the current door lock exposed to the outside and minimize the user's fingerprint trace. In addition, a method of reinforcing security is applied using fingerprint recognition through image processing and a random pattern number arrangement. An experiment was conducted to confirm whether the door lock of this type was actually usable, and the recognition of partially damaged fingerprints was also confirmed. It was shown that the door lock structure proposed in this paper can maximize security by combining fingerprint recognition technology and random pattern numbering while minimizing external exposure
Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis
Get PDFPixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively
Smart Campus Governance Design for XYZ Polytechnic Based on COBIT 2019
Get PDFTechnological developments drive growth in the industrial revolution and digital transformation era. Technological developments during the industrial revolution 4.0 affect characteristics, especially in work. In responding to the change in technological developments in employment, the Ministry of Public Works has the task of conducting public works affairs in the government environment in an orderly manner to support the president in administering state government. XYZ Polytechnic is a state university as a new pilot under the Ministry of Public Works, Republic of Indonesia. As a basis for future development as well as towards a smart campus and then getting policy directions for the development of smart campus governance at the XYZ Polytechnic, it is necessary to design IT governance, especially in the reconstruction of adaptive and responsive policies and the development of structured governance with structured information systems. This study uses COBIT 2019 as the framework for governance design. With the method from the field preparation stage, interviews then assessed and evaluated existing policies and conditions of field activities to create governance designs according to COBIT 2019. The research results contained a technology governance management design with 17 processes. Based on the capability assessment and gap analysis results, recommendations were made for XYZ Polytechnic, as discussed in the results section. The suggestions are in the form of recommendations related to people, processes, and the use of technology. The recommendations act as evaluation material to improve organizational performance by providing good smart campus governance to students and internal members of XYZ Polytechnic
Data-Centric Learning Method for Synthetic Data Augmentation and Object Detection
Get PDFThis paper proposes a deep learning framework for decreasing large-scale domain shift problems in object detection using domain adaptation techniques. We have approached data-centric domain adaptation with Image-to-Image translation models for this problem. It is one of the methodologies that changes source data to target domain's style by reducing domain shift. However, the method cannot be applied directly to the domain adaptation task because the existing Image-to-Image model focuses on style translation. We solved this problem using the data-centric approach simply by reordering the training sequence of the domain adaptation model. We defined the features to be content and style. We hypothesized that object-specific information in images was more closely tied to the content than the style and thus experimented with methods to preserve content information before style was learned. We trained the model separately only by altering the training data. Our experiments confirmed that the proposed method improves the performance of the domain adaptation model and increases the effectiveness of using the generated synthetic data for training object detection models. We compared our approach with the existing single-stage method where content and style were trained simultaneously. We argue that our proposed method is more practical for training object detection models than others. The emphasis in this study is to preserve image content while changing the style of the image. In the future, we plan to conduct additional experiments to apply synthetic data generation technology to various other application areas like indoor scenes and bin picking
Customization of Cost Allocation Monitoring Report for Improving Activity-Based Costing Process
Get PDFIn the age of a global competition environment, accurate costing measurement is essential for every company. The more accurate allocation process to final outputs indicates the potential impact a company's decision has on costs. Activity-based costing is a technique for allocating organizational costs to activities that utilize the organization's resources and then tracing the costs of these activities to products, consumers, or distribution channels that generate profits or losses for the business. With a large number of cost allocations in the business processes, it makes it difficult for companies to identify the number of costs that have been allocated, especially if the data that must be processed is in large quantities. To overcome this problem, it is necessary to require cost mapping for the business process from resource to cost center to compare the number of costs that have been allocated. This research discusses the application of monitoring reports by using ALV customization in XYZ Ltd. This report was created using an iterative and incremental model approach. The simulation results show a 50% reduction of the time to execute the customization monitoring report, and it only takes one step to generate reports and analyze data. The results of this research are expected to be used as a study to provide the right solution in facilitating the process of checking the cost allocation on ABC to objectively monitor and analyze each business process (resource, activity, and cost object) and support the decision-making process
A Framework for Personalized Training at Home Based on Motion Capture
Get PDFAs the number of single-person households continues to increase, contents related to exercise at home are increasing. Therefore, this paper proposes a framework for efficient home fitness. The home fitness framework proposed in this paper is based on a method of acquiring expert motion information and providing it to users. To this end, content creation and provision are implemented by using Kinect and Unreal game engine. In addition, it is possible to provide customized home fitness in consideration of the user's athletic ability. The proposed system first captures the expert's motion and stores it. We propose a method that can efficiently store stop motion and dynamic motion storage. In the case of each user, since there is a difference in exercise ability for each individual, an adverse effect may occur if an excessively accurate motion is requested. Therefore, to solve these problems, we present the parts that can be considered for each joint. For the exercise motion provided by the expert, a method was provided that allows the user to adjust the degree of matching for each joint in consideration of user's own exercise ability. That is, joint parts that do not require exact matching could be completely excluded from matching. For parts that would be subject to large changes, a range of errors is specified. As the training progresses, the error range is reduced, and the excluded parts are presented to be matched gradually. Such adjustments are made based on expert feedback. In this way, it was possible to improve the exercise effect gradually. This paper proposes an effective method for personalized home fitness according to the user's athletic ability. This will apply to various fields besides home fitness
Design of a Big-data-Based Decision Support System for Rational Cultural Policy Establishment
Get PDFThis paper proposes a technique for designing a decision-making system based on big data to support rational cultural policy decisions. To identify a rational cultural policy, it is necessary to extract a comparable index for cultural policy and analyze and process factors in terms of cultural supply and cultural consumption. Analyzed and processed supply indices and consumption indices become the basic input data for calculating additional cultural indices that can be measured at the cultural level of each region. Regional cultural indices are treated as independent variables in terms of cultural supply, and target variables are considered in terms of cultural demand. Two corresponding types of regression models are established. Based on the eXtreme gradient boosting and light gradient boosting machine algorithms, which are representative algorithms for calculating cultural indicators, we attempted to construct and analyze a model of the proposed system. The developed model is designed to predict the demand index according to the regional cultural supply index. It was confirmed that the demand side could be changed based on supply-side items by using the proposed technique to support decision-making. Due to the complexity of the policy environment of modern society, mixing various policy tools targeting multiple functions is accepted as a common basis for policy design, but institutional arrangements are needed to reflect the results of various data analyses in budget decision-making. This will be possible to produce data based on effectiveness and suggest appropriate rational policies and decisions
Image Captioning with Style Using Generative Adversarial Networks
Get PDFImage captioning research, which initially focused on describing images factually, is currently being developed in the direction of incorporating sentiments or styles to produce natural captions that reflect human-generated captions. The problem this research tries to solve the problem that captions produced by existing models are rigid and unnatural due to the lack of sentiment. The purpose of this research is to design a reliable image captioning model that incorporates style based on state-of-the-art SeqCapsGAN architecture. The materials needed are MS COCO and SentiCaps datasets. Research methods are done through literature studies and experiments. While many previous studies compare their works without considering the differences in components and parameters being used, this research proposes a different approach to find more reliable configurations and provide more detailed insights into models’ behavior. This research also does further experiments on the generator part that have not been thoroughly investigated. Experiments are done on the combinations of feature extractor (VGG-19 and ResNet-50), discriminator model (CNN and Capsule), optimizer (Adam, Nadam, and SGD), batch size (8, 16, 32, and 64), and learning rate (0.001 and 0.0001) by doing a grid search. In conclusion, more insights into the models’ behavior can be drawn, and better configuration and result than the baseline can be achieved. Our research implies that research in comparative studies of image recognition models in image captioning context, automated metrics, and larger datasets suited for stylized image captioning might be needed for furthering the research in this field
Applications of Big Data Analytics in Traffic Management in Intelligent Transportation Systems
Get PDFBig Data technology is emerging as a mass technology that can be applied to many industries in life. Decisions in a wide range of fields may benefit greatly from the information provided by Big Data and Analytics research. One of the areas that have benefited the most from this technology is transportation, which is known as an important field in the development of each nation and possesses a huge treasure of data that traditional technologies cannot handle. Indeed, many countries have applied Big Data-based intelligent transportation systems because it is a traffic system that interacts with vehicles and people on the road, thereby reducing traffic congestion and traffic accidents year by year in many countries. The article presents the applications of Big Data technology in smart traffic systems, thereby providing the perspective of a smart city with a smart traffic system as a critical factor. This paper's analysis indicated that smart cities could be born and further developed through the linkage of Big Data technology and smart traffic systems with smart traffic systems as the core. In addition, the results also showed that the obstacle that needs to be studied at this time is the policy and legal framework for Big Data technology. Therefore, a system managed by the state or shared between the state and the private sector should be studied in the future, aiming to harmonize interests and develop the system extensively