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Application internet of things in healthcare Covid-19: A systematic review

AIP Conference Proceedings 2023年第1期2594卷

作者： H. Citrawati G. P. L. Barnabas T. A. Santoso Suharjito Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Coronavirus, or Covid-19, is a new virus first discovered in Wuhan, China, at the end of 2019. This virus attacks the human respiratory area, which can cause respiratory problems and even loss of life. Covid-19 virus transmission is also high-speed and very easy; it is transmitted through airborne and human droplets. Therefore, to reduce the transmission rate of this virus, several researchers revealed that social distancing is one way to suppress the transmission of the Covid-19 virus. In the era of technology, The Internet of Things (IoT) is one of the systems that can help to solve this problem. The Internet of Things (IoT) cancan use one or more devices connected to the network, and it can exchange data and information effectively and efficiently. In this study, the researchers review several IoT technologies that can be used to detect COVID-19 symptoms and implement social distancing rules based on the latest literature. The review results show that IoT technology has been used to monitor the progress of COVID-19 patients in real-time with various sensors connected to geographic positioning systems, mobile devices, and cloud computing. Several research proposals for the development of IoT technology have been identified, such as the detection of COVID-19 patients in crowds, prediction of the spread of the virus in an area, and implementation of health protocols in various crowded locations.

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Personnel System Development with Integrated Military Education Information System (MEIS) in Supporting the Personnel Position and Career 7

Personnel System Development with Integrated Military Educat...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Setyaningrum, Leli Supangkat, Suhono H. Warnars, Harco Leslie Hendric Spits Abraham, Juneman Abdurachman, Edi Bina Nusantara University Doctorate Program of Computer Science Jakata Barat11530 Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung40132 Indonesia Nusantara University Graduate Program Doctor of Computer Science Bina Computer Science Department BINUS Jakarta11530 Indonesia Bina Nusantara University BINUS Faculty of Humanities Psychology Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Personnel and education data very important for organizations, including the military. All data related to military personnel will give effect positions and careers in the military field. The integration of personnel and education data can assist in processing and decision making. An analysis is needed so that the developed system can be useful for the long term. Analysis with IT balanced scorecard and COBIT® 2019, will be able to help and fill the gaps in the existing system based on the objectives of the military organization itself. It is shown that the domains BAI05 (Managed Organizational Change) and DSS02 (Managed Service Requests and Incidents) are still not being implemented well. The system developed will have the features and capabilities to support the organization in achieving its goals, including in the field of staffing and education. Besides, it provides convenience in making decisions in terms of positions related to personnel careers. © 2020 IEEE.

关键词： Decision making

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Genome data mining approach to identify potential protein in crop plants

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

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Exploration of Technology Home-based Learning Guidance for Parents and Students during the Covid-19 Pandemic 9

Exploration of Technology Home-based Learning Guidance for P...

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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Suzanna, Suzanna Sasmoko, Sasmoko Lumban Gaol, Ford Oktavia, Tanty Matsuo, Tokuro Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Information System Department Tokyo140-0011 Japan

ISBN: (纸本)9781728173979

With the sudden attack of the Corona Virus Disease 2019 (COVID-19), some 300 million children in the world have been forced to study at home because of the mass closure of schools that began on March 18, 2020. This has caused many obstacles in the teaching and learning process. Every effort is made to minimize the COVID-19 pandemic while ensuring the teaching and learning process is not interrupted. If the school closures too long and the child is trapped inside the house this will have a negative impact on the physical and mental health of the children. This situation makes parents become worried and look for alternative education in addition to formal schooling for their children. Of the various types of education offers that exist homeschooling or commonly known as home-based learning provides a way out for the current situation. Now children are quarantined in their homes and they will return to school once this epidemic fades. But with this disaster, parents' views on homeschooling or home-based learning can be different from before. They may still be marked if home-based learning can be a long-term education. With the existence of COVID-19, most parents might finally realize that education without formal schooling is not a crisis but an opportunity to take their children to a better future. This research was conducted by utilizing a descriptive qualitative method and within the descriptive qualitative method, the concept of document analysis would be utilized the most. It was expected that the results of this research would explore what type of technology parents could prepare for their children to use to initiate home-based learning. © 2020 IEEE.

关键词： Viruses

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning

Improving Candle Direction Classification in Forex Market Us...

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International Conference on Informatics and Computing (ICIC)

作者： Raymond Sunardi Oetama Yaya Heryadi Lukas Wayan Suparta Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

ISBN: (纸本)9798350345728

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned.

关键词： Support vector machines Training Exchange rates Market research Data models Kernel Informatics

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Corrigendum to “Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches” [Comput. Electron. Agric. 188 (2021) 106359]

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computers and Electronics in Agriculture 2023年 207卷

作者： Gregorius Natanael Elwirehardja Jonathan Sebastian Prayoga Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta 11480 Indonesia

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Blockchain Technology Open Problems and Impact to Supply Chain Management in Automotive Component Industry 6

Blockchain Technology Open Problems and Impact to Supply Cha...

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6th International Conference on Computing, Engineering, and Design, ICCED 2020

作者： Surjandy Meyliana Spits Warnars, Hareco Leslie Hendric Abdurachman, Edi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728191898

The study of the adoption of Blockchain Technology (BT) of the Supply Chain Management System (SCMS) automotive components industry still in infancy. BT's previous research topic mostly in virtual currency, security, and IoT. However, the automotive market's significant development causes several issues in the SCMS automotive components industry, such as fake components. Blockchain Technology (BT), as identified, might be possible as a way out for the development of SCMS automotive components. There are several research questions in this research, such as how BT can facilitate SCMS open issues? What is the BT impact in the SCM area? The study method used the systematic literature review (SLR) method to gather all information from 12 publishers and an international index journal. Seven open issues identified in this study and SCM's core operational aspect, such as supplier, logistics, manufacturer, distribution, and customer, have been identified impact with BT. © 2020 IEEE.

关键词： Automotive industry

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A Blockchain Technology-Based for University Student Enrollment Process 6

A Blockchain Technology-Based for University Student Enrollm...

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6th International Conference on Computing, Engineering, and Design, ICCED 2020

作者： Meyliana Surjandy Fernando, Erick Cassandra, Cadelina Eka Widjaja, Henry Antonius Chandra, Yakob Utama Prabowo, Harjanto School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728191898

University in Indonesia contemporary is looking for a solution to improve the university documentation such as certificate and transcript. But fake diploma certificate and transcript is an issue reported in several articles recently. The new technology called Blockchain has been elaborate and predicted can be used to solve the issue because of blockchain characteristics which are immutable, secure, tamperproof and unchangeable. The enrollment process is one of the vital process for university before the document can be made. Therefore, the research tries to elaborate deeply to create a model of the university student enrollment process. The research question of this research is how Blockchain Technology can be merge in the enrollment process? The model has been validated by several universities in Indonesia. Forum Group Discussion had been performed to discuss for enrollment process. In the end, many benefits come up from blockchain technology utilization to produce a reliable document such as diploma certificate and transcripts. © 2020 IEEE.

关键词： Blockchain

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Fairuz Iqbal Maulana Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.

关键词： COVID-19 Sentiment analysis Analytical models Data analysis Social networking (online) Blogs Data visualization

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Identification of Features in ANN's Black Box using Dimensional Reduction and Correlation Analysis: A Case Study of Cardiovascular Disease Dataset

Identification of Features in ANN's Black Box using Dimensio...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Boby Siswanto Yaya Heryadi Wayan Suparta Antoni Wibowo Haryono Soeparno Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia

ISBN: (纸本)9781665499705

Artificial Neural Network (ANN) is a machine learning algorithm that can perform classification. ANN has limitations; namely, it has a black box working principle, which is unsure which feature is the most influential. This study is to identify the most influential features inside the ANN's black box using a classification model by applying Principal Component Analysis (PCA) dimension reduction combined with Pearson correlation analysis. The result of the proposed model can identify the name of the main features of the data inside the ANN's black box. This study uses two public Kaggle cardiovascular datasets. The first dataset consists of 13 features, and the second dataset consists of 12 features. The result is height and gender are the most influential features in the first dataset with the correlation value of 0.734; sex and smoking are the most influential features in the second dataset with the correlation value of 0.728. Black box model result with 2 PCA's features against a model with height and gender features in the first dataset resulting from the same accuracy on the test dataset of the classification prediction results with the value of 49.90%, while on the second dataset 58.30%.

关键词： Dimensionality reduction Analytical models Correlation Mechatronics Machine learning algorithms Artificial neural networks Predictive models

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