2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)最新文献

{"title":"Cervical Cancer Identification using Deep Learning Approaches","authors":"Shien Nie Kong, D. O. Dwi Handayani, Hou Kit Mun, Pei Pei Chong, T. Mantoro","doi":"10.1109/ICCED56140.2022.10010544","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010544","url":null,"abstract":"The presence of cervical cancer is not apparent as its incubation period is long. A pap smear screening is the only diagnostic method; examining the pap smear slides uses a microscope. However, problems happen where humans make mistakes during the diagnostic process, causing inaccurate results and delaying the individual who needs to receive comprehensive treatments. With hopes to improve the current situation, assisting cervical cancer diagnosis with artificial intelligence techniques is suggested. This paper will use ResNet101v2, an upgraded residual network from ResNet, to develop a cervical cancer detection model to predict the severity of cervical cells. The Herlev dataset distributed 70% into training and 30% into validation; remaining 98 unique images will be used during the testing stage. Transfer learning techniques were introduced to develop the model. Using the testing images, the model reached 71.4% accuracy contributing better accuracy compared to other research studies in predicting the cervical cells using 7 classification classes. The model shows potential for clinical cytotechnologists’ during the pap smear diagnosis.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133415140","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}

{"title":"Optimal Solution for Data Storage Security in Cloud Computing (OSDSS-CC)","authors":"Alaya Al Azzani, A. Alkalbani","doi":"10.1109/ICCED56140.2022.10010354","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010354","url":null,"abstract":"In the recent decade, Cybersecurity and Cloud Computing have become important research and development areas due to the high necessity of applications in this paradigm for different sectors such as education, economy, and public services. The use of encryption algorithms becomes more critical in trying to secure the data during the transmission and storage of the data. There is leakage in the optimal security solution for cloud computing that covers all factors such as performance and security. This research work aims to analyze the Cybersecurity weakness in the Cloud Computing applications and optimize a modern solution that concentrates on performance and security at the same time. As a first step in analyzing the Cloud Computing environment security, three modern security mechanisms for Cloud applications are simulated and evaluated in terms of security and performance. Analyzed results provide an essential indicator for using this security mechanism and show the need to design an optimal security mechanism suitable to cover the main security parameters for the cloud environment. This motivates to propose an optimal method for using advantage of the studied security algorithms to enhance cloud computing security with optimal cost.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122977919","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}

{"title":"Measurement of Information Security Awareness of Students as Financial Technology Users in Indonesia","authors":"Nurul Fathiya, Andra Amelia, Athirah Fanesa, Putri Mentari, C. Candiwan","doi":"10.1109/ICCED56140.2022.10010346","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010346","url":null,"abstract":"The ease of transacting using fintech increased in its use for transactions. Fintech in Indonesia has high potential, where fintech is dominated by the payment sector by 38%, lending by 31%, personal finance and wealth management by 8%, and the rest is filled by other sectors. The use of fintech has attracted a lot of attention, but the continued use of fintech remains in doubt. Some users are hesitant to continue using fintech because it has considerable risks. The risks borne by fintech users start from entering personal data into the information system so that users can use the facilities in the application used. Thus allowing misuse of information provided by users. This research aims to measure the information security awareness of students as fintech users in Indonesia. The study was conducted on 400 students in Indonesia who use fintech and uses the analytical hierarchy process (AHP) by developing dimensions of attitude, knowledge, and behavior to measure a person’s information security awareness. The results showed that the total level of information security awareness was 88% and was in the good category. However, there is one focus area in the moderate category with the lowest level of 73%, namely adoption of security control so that students as users of fintech services should use different passwords/pins on some of their fintech account in order to avoid the risk of leakage of user data.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622763","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}

{"title":"Applications of Artificial Intelligence and Big Data for Strengthening the Role of Intelligence in Human Security","authors":"Ahmad Shahrul Mubarok, A. Susandi, Teddy Mantoro","doi":"10.1109/ICCED56140.2022.10010514","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010514","url":null,"abstract":"The state, through the government, should ensure national security and stability through the policies taken. For this reason, fast and accurate information and data are needed to form the basis for policymaking. Intelligence actors have an essential role in gathering information to identify and prevent threats within and outside the country. Technological advances affect changes in threats and increase the classification of these threats. Covid-19 is a real example of the threat in this era because almost all countries feel its impact. In addition to affecting health, the Covid-19 pandemic also impacts the global economic sector. The economic impact is felt by small and medium industries, goods and services products, and also the tourism sector. The method that is used in this study is a literature review from various national and international journals. AI and Big Data can be used to track viruses in real time, collect adequate public health data, and integrate vaccine use. Also, AI and Big Data can provide local decision and policy-makers with informed, evidence-based predictions. the threat of the Covid-19 pandemic and other infectious diseases that disrupt public health and have a broad impact must be anticipated and addressed in order to achieve the safety and existence of the nation and state. The author wants to emphasize the importance of the role of intelligence in anticipating threats from the visible to the invisible.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128763197","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}

{"title":"Federated Deep Learning for Automated Detection of Diabetic Retinopathy","authors":"Nadzurah Zainal Abidin, Amelia Ritahani Ismail","doi":"10.1109/ICCED56140.2022.10010636","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010636","url":null,"abstract":"Diabetic retinopathy (DR) is a primary cause of impaired vision that can lead to permanent blindness if not detected and treated early. Unfortunately, DR frequently has no early warning signs and may not generate any symptoms. According to recent figures, over 382 million people worldwide suffer from DR, with the number expected to climb to 592 million by 2030. Patients with DR may not be treated in time given the apparent large number of DR patients and inadequate medical resources in specific places, resulting in missed treatment possibilities and eventually irreversible vision loss. Color fundus diagnosis requires highly experienced experts to recognize the existence of tiny features and the relevance of DR. Unfortunately, manually diagnosing DR is time-consuming, tedious and error-prone. At the same time, the effect of manual interpretation is highly dependent on the medical expert experiences. Deep learning is a machine learning algorithm with potential for detecting the significance of DR. However, deep learning still suffers from high computational cost, requires tons of training data, over fitting, and non-trivial hyper parameter tuning. Thus, in order to build a model that can compete with medical experts, deep learning algorithms must feed a huge number of instances or pool data from other institutions. Federated learning allows deep learning algorithms to learn from a diverse set of data stored in multiple databases. Federated learning is a novel method for training deep learning models on local DR patient data, with just model parameters exchanged between medical facilities. The objectives of this research is to avoid the requirement sharing DR patient data, since such approaches expedite the development of deep learning models through the use of federated learning. Primarily, we propose a federated learning which decentralizes deep learning by eliminating the need to pool data in a single location. In this research, we present a practical method for the federated learning of deep network based on retinal image of diabetic retinopathy.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115138325","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}

{"title":"Analysis of Information Technology Governance on Process Management Services and Management of Information Technology Security Using COBIT 2019 (Case Study: PT XYZ)","authors":"Keysha Salsabila Saputra, Maulidia Isnaini, Tasya Agrefine S, S. Mariam, Candiwan","doi":"10.1109/ICCED56140.2022.10009924","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10009924","url":null,"abstract":"The development of disruptive technology in the modern era is the basis for the important role of Information Technology (IT) for every component, even in an organization, to achieve its targets and goals. At PT XYZ, there are some stages which include general administration, accounting, and finance that cannot be separated from the application system usage. Management must measure whether the application system is safe from any risk, mainly from security issues, frequently issues that management receives are web-based cyber-attacks, and the recovery process often impedes the course of business activity. Management’s efforts to reduce the obstacles that occur begin with knowing the ability of information technology governance, so governance and processes are needed to analyze. This study aims to provide recommendations related to the management of IT services and IT security, which refers to the COBIT 2019 framework by focusing on the domain processes of APO13 Managed Security, BAI06 Manage IT Changes, and DSS02 Managed Service Requests and Incidents at PT XYZ. The process of this research begins by collecting data through documentation analysis methods, interviews, and distributing questionnaires with an analysis of the ideal conditions desired by the company so that it will produce a gap analysis by COBIT 2019 and COBIT 2019 Implementation. Furthermore, recommendations will be made to improve services in the aspects of people, process, and technology based on the results of the questionnaire analysis, such as offering several choices of software or applications and drafting documents regarding work instructions for fulfilling IT services and handling IT problems.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115194395","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}

{"title":"Development of Seven Segment Display Recognition using TensorFlow on Raspberry Pi","authors":"Teddy Surya Gunawan, Haque Mohammad Reduwan, M. Yaacob, N. F. M. Azmin, N. Ismail, Md Yusof Ismail, M. Kartiwi, Azhar A. Rahman","doi":"10.1109/ICCED56140.2022.10010449","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010449","url":null,"abstract":"LED Seven Segment displays have become embedded in our daily lives, beginning with small devices like wristwatches and progressing to massive mechanical equipment used in industry. Every day, the application base for LED seven-segment displays expands in commercial and industrial applications. To facilitate industrial automation, the previous process’s displayed information must be recognized as the input to the subsequent process, even more so when direct communication between processes is not visible. The purpose of this paper is to develop a recognition system for seven segment displays on the Raspberry Pi. The design of hardware, software, and algorithms will be discussed. SSD-MobileNet will be used for subsequent experiments, out of the two architectures evaluated, Tiny YOLO and SSD-MobileNet. Experiments on the Raspberry Pi 4 demonstrate that our proposed system is capable of accurately and quickly detecting seven-segment displays.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127331852","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}

{"title":"Augmented Reality-Based Learning Media for Simple Distillation Practical Apparatus","authors":"Suchika Rahmadona Vakasari, I. Farida, F. S. Irwansyah","doi":"10.1109/icced56140.2022.10010642","DOIUrl":"https://doi.org/10.1109/icced56140.2022.10010642","url":null,"abstract":"In the simple distillation practicum, some of the equipment used was the equipment that was not always used during practicum. Augmented reality has become one of the technologies that could help in introducing simple distillation equipment. This study aims to describe the display of learning media, analyze the validation test results, and analyze the results of feasibility using the Multimedia Development Life Cycle (MDLC) method. The study results obtained that the display of AR media consisted of the main page, consisting of an information menu containing guide information and profile, logout, and log In. The scan AR page consisted of a camera menu to access the marker and assembling menu for assembling simple distillation equipment and a video of assembling simple distillation equipment The validation results showed an R-count value of around 0.90 and a feasibility test of 94%, which means that the learning media could be stated to be used properly.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126886240","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}

{"title":"Bert Transformer Model for Indonesian Mood Music Dataset","authors":"T. T, E. Abdurachman, Y. Heryadi, Amalia Zahra","doi":"10.1109/ICCED56140.2022.10010511","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010511","url":null,"abstract":"Music is an inseparable part of human life. Music can affect a person's mood. One's soul can unite in music. Music influences a person to feel sad, happy and relaxed. In terms of music, in general Indonesia has two types of music. Regional and national music. National music consists of several genres. Some of them are Dangdut, Pop, Ballad, and Rock. The many genres and developments in the world of music open the opportunities to the field of computer science to be explored further regarding to the amount of data contained in music processing (songs). In this study, the authors used self-processed data sets. The songs were downloaded from Youtube channel then were converted and only required chorus. In practice, the author used the Bidirectional Encoder Representations from Transformers (BERT) model. In learning, this model machine is used to improve accuracy. The resulting accuracy of this method is 81% with a chorus of more than 1000 songs.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014413","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}

{"title":"Comparison of Classification Data Mining Models Predicting Heart Disease in Europe","authors":"D. Gustian, Rian Nugraha, Adriansyah Muhamad Alfaudzan, Austin Almayda","doi":"10.1109/ICCED56140.2022.10010414","DOIUrl":"https://doi.org/10.1109/ICCED56140.2022.10010414","url":null,"abstract":"Heart disease is the no.1 killer disease globally, and it has become a scary thing for the whole world is, no exception in Europe. Data obtained from the World Health Organization in 2020, nearly 10 million people died, while by 2030, it is predicted to reach 50 million per year. In Europe, the death rate from heart disease spread in various countries such as France, which reached 213 deaths per million population, Spain with 481 million deaths, and the Netherlands with about 491 population deaths. The study provided a classification model of heart disease predictions by comparing the best random forest methods with naive Bayes. The results of this study if the decision making of the best model depends on the needs that occur in various European countries. For example, the northern European states, especially Scandinavia such as Denmark, Sweden and other European countries that are characterized by light brown and dark brown colors that have a heart disease value above 2,000 are recommended to use a classification model with the Naive Bayes method because, while for countries that are light blue with dark blue with an infected value below 2,000 it is recommended to use the Random forest method.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385840","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}