Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034583
Yosia Bagariang, M. I. Nashiruddin, Nachwan Mufti Adriansyah
Internet of Things is predicted to be the future of digital business in Indonesia. The trend of IoT implements on a large scale as more sensors and devices are connected. LoRa is one of the promising applications of the Internet of Things with long-range wireless communication in broad areas. LoRa network deployment in Advanced Metering Infrastructure (AMI) is one of the most significant components in smart metering which measure and collect data from smart meters to be analyzed utilities distribution and consumption. In the IoT, the scenario must achieve two main goals: efficient communication like massive connectivity and defense against environmental conditions. In this paper, we propose the LoRa for Advanced Metering Infrastructure in three utilities (electric, gas, and water meter) to implement in urban, suburban, and rural areas in case of Medan and surroundings by calculating capacity and coverage planning to find out the optimum number of LoRa gateways. Use the method of forecasting IoT connected devices then predict the amount of LoRa gateways need with two scenarios. The first scenario requires the capacity planning by volume demand of the customer's supply of the system. Meanwhile, the second scenario needs coverage planning mostly depends on the link budget and geographical. The simulation results in Forsk Atoll 3.3.2 indicate that the whole determined areas can serve by the number of gateways that have to obtain within acceptable the mean of best signal levels is –76,28 dBm, –75,7 dBm, –81,67 dBm for the urban, suburban and rural scenario, respectively.
{"title":"LoRa-based IoT Network Planning for Advanced Metering Infrastructure in Urban, Suburban and Rural Scenario","authors":"Yosia Bagariang, M. I. Nashiruddin, Nachwan Mufti Adriansyah","doi":"10.1109/ISRITI48646.2019.9034583","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034583","url":null,"abstract":"Internet of Things is predicted to be the future of digital business in Indonesia. The trend of IoT implements on a large scale as more sensors and devices are connected. LoRa is one of the promising applications of the Internet of Things with long-range wireless communication in broad areas. LoRa network deployment in Advanced Metering Infrastructure (AMI) is one of the most significant components in smart metering which measure and collect data from smart meters to be analyzed utilities distribution and consumption. In the IoT, the scenario must achieve two main goals: efficient communication like massive connectivity and defense against environmental conditions. In this paper, we propose the LoRa for Advanced Metering Infrastructure in three utilities (electric, gas, and water meter) to implement in urban, suburban, and rural areas in case of Medan and surroundings by calculating capacity and coverage planning to find out the optimum number of LoRa gateways. Use the method of forecasting IoT connected devices then predict the amount of LoRa gateways need with two scenarios. The first scenario requires the capacity planning by volume demand of the customer's supply of the system. Meanwhile, the second scenario needs coverage planning mostly depends on the link budget and geographical. The simulation results in Forsk Atoll 3.3.2 indicate that the whole determined areas can serve by the number of gateways that have to obtain within acceptable the mean of best signal levels is –76,28 dBm, –75,7 dBm, –81,67 dBm for the urban, suburban and rural scenario, respectively.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116355235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034656
Abidatul Izzah, B. A. Nugroho, W. Mahmudy, F. A. Bachtiar, T. A. Cinderatama, Y. A. Sari
Particle swarm optimization (PSO) algorithm has been widely used to solve many problems. However, PSO has limitation in dealing with premature convergence when each particle unable to move to find the global optimum solution. This research has investigated the various conditions for the PSO to determine when a premature convergence happened. We used city parks in Kediri City, Indonesia as an object for a city tour optimization. Furthermore, PSO by adding mutation operator belongs to Genetic Algorithm and dividing the swarm group into sub-swarm are used to investigate the convergence condition because they have been proven can successfully avoid a premature convergence. The result shows that the solutions produced by the addition of these operators can find better solutions compared to the simple PSO.
{"title":"Convergence Analysis in Swarm Intelligence for City Tour Optimization","authors":"Abidatul Izzah, B. A. Nugroho, W. Mahmudy, F. A. Bachtiar, T. A. Cinderatama, Y. A. Sari","doi":"10.1109/ISRITI48646.2019.9034656","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034656","url":null,"abstract":"Particle swarm optimization (PSO) algorithm has been widely used to solve many problems. However, PSO has limitation in dealing with premature convergence when each particle unable to move to find the global optimum solution. This research has investigated the various conditions for the PSO to determine when a premature convergence happened. We used city parks in Kediri City, Indonesia as an object for a city tour optimization. Furthermore, PSO by adding mutation operator belongs to Genetic Algorithm and dividing the swarm group into sub-swarm are used to investigate the convergence condition because they have been proven can successfully avoid a premature convergence. The result shows that the solutions produced by the addition of these operators can find better solutions compared to the simple PSO.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123995452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034655
B. Pambudi, Indriana Hidayah, S. Fauziati
Identification of financial transactions as suspicious or fraudulent transactions that are indicated as money laundering is mostly done manually so that it is not optimal. Data mining techniques can be a solution to overcome the limitations of the manual method. The main challenge in applying data mining techniques for financial fraud detection is an imbalanced dataset, where the proportion of fraud class is much smaller than non-fraud. This causes the model to produce unbalanced precision and recall, resulting in a low f1score. It means that the model can predict one class well, but not with another class. In this paper, the approach to fraud detection in financial transactions is carried out with classifier optimization based on Support Vector Machine (SVM). Optimization is performed by tuning the kernels and hyperparameters combined with the Random Under Sampling (RUS) technique. Specifically, RUS is used to handle imbalanced datasets and cut model training time. With this combination technique, the classifier can detect fraud more effectively with an increase in precision of 40.82% and f1-score of 22.79% compared to the previous study. A combination technique can be an approach to cover weaknesses left behind by a single method.
{"title":"Improving Money Laundering Detection Using Optimized Support Vector Machine","authors":"B. Pambudi, Indriana Hidayah, S. Fauziati","doi":"10.1109/ISRITI48646.2019.9034655","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034655","url":null,"abstract":"Identification of financial transactions as suspicious or fraudulent transactions that are indicated as money laundering is mostly done manually so that it is not optimal. Data mining techniques can be a solution to overcome the limitations of the manual method. The main challenge in applying data mining techniques for financial fraud detection is an imbalanced dataset, where the proportion of fraud class is much smaller than non-fraud. This causes the model to produce unbalanced precision and recall, resulting in a low f1score. It means that the model can predict one class well, but not with another class. In this paper, the approach to fraud detection in financial transactions is carried out with classifier optimization based on Support Vector Machine (SVM). Optimization is performed by tuning the kernels and hyperparameters combined with the Random Under Sampling (RUS) technique. Specifically, RUS is used to handle imbalanced datasets and cut model training time. With this combination technique, the classifier can detect fraud more effectively with an increase in precision of 40.82% and f1-score of 22.79% compared to the previous study. A combination technique can be an approach to cover weaknesses left behind by a single method.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132212358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034594
Helmi Satria Nugraha, S. Suyanto
Many questions posed by users to particular customer service with a short dialog (such as a chatbot) cause difficulties to answer. These reduce the user satisfaction level to the service. A question answering (QA) system can be developed to solve this problem by providing relevant answers to the user questions. One of the commonly used methods to build a QA is a question retrieval (QR) that provides answers based on the most relevant stored- questions. However, interpreting two questions those are essentially the same but in different words is quite challenging. Besides, the limitation of the data set to learn is also interesting. This paper investigates a data augmentation based on typographic and synonym as well as evaluates the use of sub-word (instead of word) features to get the best word-embedding in the question. The word-embedding is then used to search the cosine similarity between a query and the stored-questions. Finally, the user receives an answer based on the question with the highest cosine similarity. Evaluation on a quite low data set shows that the proposed data augmentation is capable of significantly improving the system performance. Besides, the sub-word feature is better for word-embedding in the short conversation than the whole-word one.
{"title":"Typographic-Based Data Augmentation to Improve a Question Retrieval in Short Dialogue System","authors":"Helmi Satria Nugraha, S. Suyanto","doi":"10.1109/ISRITI48646.2019.9034594","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034594","url":null,"abstract":"Many questions posed by users to particular customer service with a short dialog (such as a chatbot) cause difficulties to answer. These reduce the user satisfaction level to the service. A question answering (QA) system can be developed to solve this problem by providing relevant answers to the user questions. One of the commonly used methods to build a QA is a question retrieval (QR) that provides answers based on the most relevant stored- questions. However, interpreting two questions those are essentially the same but in different words is quite challenging. Besides, the limitation of the data set to learn is also interesting. This paper investigates a data augmentation based on typographic and synonym as well as evaluates the use of sub-word (instead of word) features to get the best word-embedding in the question. The word-embedding is then used to search the cosine similarity between a query and the stored-questions. Finally, the user receives an answer based on the question with the highest cosine similarity. Evaluation on a quite low data set shows that the proposed data augmentation is capable of significantly improving the system performance. Besides, the sub-word feature is better for word-embedding in the short conversation than the whole-word one.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130261134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034647
Nadhif Muhammad Rekoputra, R. Harwahyu, R. F. Sari
OpenAirInterface (OAI) 5G is an open source-based software that can implement LTE-based telecommunications systems and it’s protocols, it uses the 3GPP standard on a public computer. OpenAirInterface 5G is used to conduct research on the development of 4G to 5G at a lower cost. In this work, we evaluate the concept of Virtual Networks on the OpenAirInterface 5G network by evaluating the performance of our OpenAirInterface 5G system on a cloud platform managed by Juju Orchestration. The cloud platform used is managed by Metal as a Service (MAAS) to help allocate the network infrastructure, both physical and virtual. Juju Orchestration is used to accelerate and automate the deployment and configuration of OpenAirInterface applications on cloud platforms. The performance evaluation is done by testing the bitrate, latency, jitter, streaming performance, and browsing performance. The performance evaluation shows that the performance quality of the OAI cloud is better, although the differences are not significant. Besides that, the OAI cloud still has it’s big advantages in terms of its scalability process for the ease of automation deployment of applications carried out by Juju Orchestration.
OpenAirInterface (OAI) 5G是一种基于开源的软件，可以实现基于lte的电信系统及其协议，它在公共计算机上使用3GPP标准。OpenAirInterface 5G用于研究4G向5G的低成本发展。在这项工作中，我们通过评估我们的OpenAirInterface 5G系统在Juju Orchestration管理的云平台上的性能，评估了OpenAirInterface 5G网络上虚拟网络的概念。所使用的云平台由Metal as a Service (MAAS)管理，以帮助分配物理和虚拟的网络基础设施。Juju Orchestration用于加速和自动化OpenAirInterface应用程序在云平台上的部署和配置。性能评估是通过测试比特率、延迟、抖动、流性能和浏览性能来完成的。性能评价表明，OAI云的性能质量更好，但差异不显著。除此之外，OAI云在其可伸缩性过程方面仍然具有很大的优势，可以轻松地自动化部署由Juju Orchestration执行的应用程序。
{"title":"Performance study of OpenAirInterface 5G System on the Cloud Platform Managed by Juju Orchestration","authors":"Nadhif Muhammad Rekoputra, R. Harwahyu, R. F. Sari","doi":"10.1109/ISRITI48646.2019.9034647","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034647","url":null,"abstract":"OpenAirInterface (OAI) 5G is an open source-based software that can implement LTE-based telecommunications systems and it’s protocols, it uses the 3GPP standard on a public computer. OpenAirInterface 5G is used to conduct research on the development of 4G to 5G at a lower cost. In this work, we evaluate the concept of Virtual Networks on the OpenAirInterface 5G network by evaluating the performance of our OpenAirInterface 5G system on a cloud platform managed by Juju Orchestration. The cloud platform used is managed by Metal as a Service (MAAS) to help allocate the network infrastructure, both physical and virtual. Juju Orchestration is used to accelerate and automate the deployment and configuration of OpenAirInterface applications on cloud platforms. The performance evaluation is done by testing the bitrate, latency, jitter, streaming performance, and browsing performance. The performance evaluation shows that the performance quality of the OAI cloud is better, although the differences are not significant. Besides that, the OAI cloud still has it’s big advantages in terms of its scalability process for the ease of automation deployment of applications carried out by Juju Orchestration.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117333664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034620
Dhamir Raniah Kiasati Desrul, A. Romadhony
Abusive language is an expression used by a person with insulting delivery of any person's aspect. In the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse. An abusive language detection system is important to prevent the negative effect of such comments. Detecting abusive language in the online comment section is a challenge since abusive languages can be expressed in various words. Moreover, only a few studies have been conducted in Indonesian language. In this paper, we present an Indonesian abusive language detection system by tackling this problem as a classification task and solving it using the following classifiers: Naive Bayes, SVM, and KNN. We also performed feature selection procedure based on Mutual Information value between words. The experimental results show that SVM is the best classifier for detecting the abusive language in news comment with an accuracy score of 90,19% and the use of Mutual Information able to improve the classification accuracy by 1.63%. Mutual Information can increase the accuracy performance of the classifier.
{"title":"Abusive Language Detection on Indonesian Online News Comments","authors":"Dhamir Raniah Kiasati Desrul, A. Romadhony","doi":"10.1109/ISRITI48646.2019.9034620","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034620","url":null,"abstract":"Abusive language is an expression used by a person with insulting delivery of any person's aspect. In the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse. An abusive language detection system is important to prevent the negative effect of such comments. Detecting abusive language in the online comment section is a challenge since abusive languages can be expressed in various words. Moreover, only a few studies have been conducted in Indonesian language. In this paper, we present an Indonesian abusive language detection system by tackling this problem as a classification task and solving it using the following classifiers: Naive Bayes, SVM, and KNN. We also performed feature selection procedure based on Mutual Information value between words. The experimental results show that SVM is the best classifier for detecting the abusive language in news comment with an accuracy score of 90,19% and the use of Mutual Information able to improve the classification accuracy by 1.63%. Mutual Information can increase the accuracy performance of the classifier.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133511596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034638
F. Zulfira, S. Suyanto
The characteristics of glaucoma that can be observed through a fundus image is a peripapillary atrophy (PPA). Hence, identifying glaucoma based on fundus images can be carried out by observing the PPA occurrence. Research conducted by detecting the presence of PPA has been done a lot but still uses two classes of PPA namely no-PPA and PPA. It cannot distinguish between mild-PPA and severe-PPA which causes treatment equalization. Support Vector Machine (SVM) has had success in classifying PPA, so it will be used to classify PPA from the retinal fundus image dataset into multi classes, i) no-PPA, ii) mild-PPA and iii) severe-PPA. Multiclass PPA classification can detect glaucoma and also know the severity which then determines the treatment and treatment to be carried out. Testing on two datasets containing 110 images of retinal fundus (85 as the training-set and 25 as the testing-set), the proposed method gives high accuracies of 95% and 94% respectively.
{"title":"Multi-Class Peripapillary Atrophy for Detecting Glaucoma in Retinal Fundus Image","authors":"F. Zulfira, S. Suyanto","doi":"10.1109/ISRITI48646.2019.9034638","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034638","url":null,"abstract":"The characteristics of glaucoma that can be observed through a fundus image is a peripapillary atrophy (PPA). Hence, identifying glaucoma based on fundus images can be carried out by observing the PPA occurrence. Research conducted by detecting the presence of PPA has been done a lot but still uses two classes of PPA namely no-PPA and PPA. It cannot distinguish between mild-PPA and severe-PPA which causes treatment equalization. Support Vector Machine (SVM) has had success in classifying PPA, so it will be used to classify PPA from the retinal fundus image dataset into multi classes, i) no-PPA, ii) mild-PPA and iii) severe-PPA. Multiclass PPA classification can detect glaucoma and also know the severity which then determines the treatment and treatment to be carried out. Testing on two datasets containing 110 images of retinal fundus (85 as the training-set and 25 as the testing-set), the proposed method gives high accuracies of 95% and 94% respectively.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133349033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034614
Ahmad Taufiq Musaddid, Agus Bejo, Risanuri Hidayat
Recognition of vehicle license plate based on computer vision is very useful for replacing human eye from manually identifying license plate. In practice, the algorithm of licence plate recognition needs to be robust to various orientations, noises and illuminations of captured plates. Conventionally, one of the challenging processes is segmenting the characters of detected plate. The segmented characters are extracted to perform recognition. Thus, performance of character segmentation affects the final result. This research aims to perform character segmentation of Indonesian license plate by applying detection of character using Convolutional Neural Network (CNN) and sliding window with bounding box refinement. In this proposed method, CNN is used to distinguish character and non-character region. To feed regions to CNN, sliding window technique is applied. The final bounding boxes are finally refined to increase the accuracy. And the developed model was tested on 130 Images of Indonesian vehicle license plate which contain 982 characters in total, and yielded 87.06% of accuracy.
{"title":"Improvement of Character Segmentation for Indonesian License Plate Recognition Algorithm using CNN","authors":"Ahmad Taufiq Musaddid, Agus Bejo, Risanuri Hidayat","doi":"10.1109/ISRITI48646.2019.9034614","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034614","url":null,"abstract":"Recognition of vehicle license plate based on computer vision is very useful for replacing human eye from manually identifying license plate. In practice, the algorithm of licence plate recognition needs to be robust to various orientations, noises and illuminations of captured plates. Conventionally, one of the challenging processes is segmenting the characters of detected plate. The segmented characters are extracted to perform recognition. Thus, performance of character segmentation affects the final result. This research aims to perform character segmentation of Indonesian license plate by applying detection of character using Convolutional Neural Network (CNN) and sliding window with bounding box refinement. In this proposed method, CNN is used to distinguish character and non-character region. To feed regions to CNN, sliding window technique is applied. The final bounding boxes are finally refined to increase the accuracy. And the developed model was tested on 130 Images of Indonesian vehicle license plate which contain 982 characters in total, and yielded 87.06% of accuracy.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128870496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034659
Kevin Jonathan, A. Sari
In recent years, we have seen very significant development in software technologies. Many new and more sophisticated technologies such as Artificial Intelligence, Virtual Reality, and Internet of Things are being adopted for daily use. One of the emerging technologies is blockchain. Every day, more and more businesses and industries start implementing blockchain to their systems. Although the features of blockchain may give us a convenient and reliable services, the security of this recent innovation is still a question to be concerned about. One of them is the majority attack that has happened to the Bitcoin network involving a group of miners having more than 51% of the computing power of the entire network, and there are some other issues as well. Before blockchain could be widely adapted for practical usage, we would have to take a deeper look into the issues and threats it could have, which we address in this paper.
{"title":"Security Issues and Vulnerabilities On A Blockchain System: A Review","authors":"Kevin Jonathan, A. Sari","doi":"10.1109/ISRITI48646.2019.9034659","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034659","url":null,"abstract":"In recent years, we have seen very significant development in software technologies. Many new and more sophisticated technologies such as Artificial Intelligence, Virtual Reality, and Internet of Things are being adopted for daily use. One of the emerging technologies is blockchain. Every day, more and more businesses and industries start implementing blockchain to their systems. Although the features of blockchain may give us a convenient and reliable services, the security of this recent innovation is still a question to be concerned about. One of them is the majority attack that has happened to the Bitcoin network involving a group of miners having more than 51% of the computing power of the entire network, and there are some other issues as well. Before blockchain could be widely adapted for practical usage, we would have to take a deeper look into the issues and threats it could have, which we address in this paper.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034557
Reza Sugiarto, A. H. Saputro, W. Handayani
Commonly, a leaf venation is visualized using an image acquired from an RGB camera in the visible light spectrum and processed morphologically. In this paper, we propose a novel method for visualizing of the leaf venation. The proposed method consists of a hyperspectral camera on the Visual-Near Infrared band as image acquisition system and a Multi-Layer Perceptron Classifier (MLPC) as a classification algorithm. In this study, we compare some activation functions and optimizers to find the proper classification model for leaf venation. The Red Amaranth leaf was used as a sample that acquired using the hyperspectral camera at band 400 – 1000 nm. We choose two classes to represent the leaf part namely a vein area and non-vein area. The five-square pixels in the leaf image were used to represent the vein and non-vein object. The averaging of the spatial area at the full band was conducted as a spectral feature of the object. Five-fold cross-validation was performed to evaluate the performance of the proposed method. Accuracy, precision, and recall scores were computed for each classification model. The best classification result has accuracy 94.9% using activation function linear and solver function of Limited-memory Broyden–Fletcher–Goldfarb– Shanno (lbfgs). The best model is then used for visualizing venation using the hyperspectral image. The result shows that the best model could visualize primary and secondary veins in the leaf. Thus, the proposed system can be used for visualizing leaf venation on Red Amaranth leaf.
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