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Genetic Algorithm in Image Inserting with Modified Least Significant Bit Method to Find The Best MSE Value

Journal of Physics: Conference Series 2020年第1期1566卷

作者： S F Ramadhan P Sihombing Sutarman Department of Information and Technology Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia.

Inserting data into digital media tends to change the large size of the media cover file, this is because the inserted pixel has a different size from the insertion value, i.e. with a larger or smaller value, for example a pixel value of 10 is inserted with larger or smaller data from the value 10. So that the pixel value is getting further from the value of 10, this causes the value of Mean Squared Error (MSE) of the media of the cover (cover) to become larger. In this study, the selection of pixels in the image is in accordance with the value of the insertion data as the insertion place with the Genetic algorithm (GA). Insertion of data on selected pixels using the GA algorithm is performed using the Modified Least Significant Bit (MLSB) algorithm at the position of the least significant bits. To find out the reliability of the proposed algorithm, the data insertion experiment was carried out by comparing the conventional LSB algorithm with the proposed algorithm where the results obtained a significant MSE value, i.e. the average MSE value of the LSB algorithm was 20.54 and the proposed algorithm was 7.12.

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Numerical analysis of general-vibration-based method for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM). The research uses simplified structural models to explore the sensitivity of the index to damages, to compare the index performance with a traditional but popular damage detection method, and to understand the local/global predictive capability of the index. The research uses two simple models, namely, single- and two-degree-of-freedom systems. The results suggest that the damage index is local, that can only monitor damages occurring near the points of measurements, but it is sensitive to damages, unlike the natural frequency, which is global but less sensitive.

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Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Sfenrianto Sfenrianto Muhammad Yasin Kaman Nainggolan Mesra Betty Yel Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Informatics Management Politeknik LP3I Jakarta Indonesia 10450 Master of Computer Science - Postgraduate Programs STMIK Nusa Mandiri Faculty of Computer Science STIKOM CKI Jakarta Indonesia

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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COVID-19 Testing Pipeline: Lesson Learned

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Dian Amirulloh Digdo Sudigyo Arif Budiarto Ika Nurlaila Erlin Listiyaningsih Andrew Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information System Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in Indonesia is seen to be uncontrollably increasing that urges the government to leverage the capacity for the disease detections. Real-time polymerase chain reaction (RT-PCR), rapid test and computed tomography (CT) scan are the most common methods to determine if one has been infected regardless of whether or not the common symptoms of such Corona Virus Disease 2019 (COVID-19) surface. Among these three, RT-PCR is considered the gold standard for qualitative and quantitative assessment of SARS CoV-2 detection. The present paper aims at elaborating the framework of Roche's RT-PCR machine employed specifically for SARS CoV-2 detection performed by Genetics Indonesia which is deemed to be efficient and relatively quicker than other detection kits. RT-PCR machine detected SARS Cov-2 with RNA amplification curve equals to 10 copies RNA below the cut off value of Crossing point (Cp) positive control. Also elucidated in the paper is the implementations of EAV RNA and LightCycler® 96 RT-PCR System through which analysis time, amounts of individual required sample, as well as the reagents, can be accordingly reduced.

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Bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile Data on Nasopharyngeal Carcinoma

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Digdo Sudigyo Gisti Rahmawati Dicka W. Setiasari Risky H. Poluan Tjeng Wawan Cenggoro Arif Budiarto Alam Ahmad Hidayat Sri R. Indrasari Afiahayati Sofia M. Haryana Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Study Program of Biotechnology Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Medicine Public Health and Nursing Gadjah Mada University Yogyakarta Indonesia 55281 Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

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AUTOMATION OF PROPELLER INSPECTION AND FINISHING

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NAVAL ENGINEERS JOURNAL 1985年第4期97卷 124-131页

作者： STERN, H METZGER, R Howard K. Stern:is presently vice president of Robotic Vision Systems Inc. He received a bachelor of electrical engineering degree from College of the City of New York in 1960. Mr. Stern joined Dynell Electronics Corporation in 1971 and became part of the Robotic Vision Systems Inc. staff at the time of its spin-off from Dynell. He was program manager of the various three-dimensional sensing and replication systems constructed by Dynell and Robotic Vision Systems. As program manager his responsibilities encompassed technical administrative and operational areas. The first two portrait sculpture studio systems and the first three replication systems built by Robotic Vision Systems Inc. were designed manufactured and operated under his direction. Before joining Dynell Mr. Stern was a senior engineer at Instrument Systems Corporation and chief engineer of the Special Products Division of General Instrument Corporation. Prior to these positions Mr. Stern was chief engineer of Edo Commercial Corporation. At General Instrument and Edo Commercial he was responsible for the design and manufacture of military and commercial avionics equipment. Mr. Stern is presently responsible for directing the systems design and development for all of the company's programs. Robert J. Metzger:is currently engineering group leader at Robotic Vision Systems Inc. He graduated summa cum laude from the Cooper Union in 1972 with a bachelor of electrical engineering degree. Under sponsorship of a National Science Foundation graduate fellowship he graduated from the Massachusetts Institute of Technology in 1974 with the degrees of electrical engineer and master of science (electrical engineering). In 1979 Mr. Metzger graduated from Polytechnic Institute of New York with the degree of master of science (computer science). Since 1974 Mr. Metzger has been actively engaged in the design of systems and software for noncontact threedimensional optical measurement for both military and commercial applications. Of particular note are his c

Ship's propellers are currently measured by manual procedures using pitchometers, templates and gauges. This measurement process is extremely tedious, labor intensive and time consuming. In an effort to provide increased accuracy, repeatability and cost effectiveness in propeller manufacture, an automated propeller optical measurement system (APOMS) has been built which rapidly and automatically scans an entire ship's propeller using a 3-D vision sensor. This equipment is integrated with a propeller robotic automated templating system (PRATS) and the propeller optical finishing system (PROFS) which robotically template and grind the propeller to its final shape, using the APOMS-derived data for control feedback. The optical scanning and the final shape are both controlled by CAD/CAM data files describing the desired propeller shape. An automated propeller balancing system is incorporated into the PROFS equipment. The APOMS/PRATS/PROFS equipment is expected to provide lower propeller manufacturing costs.

关键词： PROPELLERS

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Comparative Analysis of Gaudy Booch and Ivar Jacobson Methods in Mercu Buana Mobile Application Database Design

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Journal of Physics: Conference Series 2019年第1期1179卷

作者： I Ranggadara Sfenrianto Computer Science Department Universitas Mercu Buana Indonesia Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Universitas Bina Nusantara Jakarta Indonesia

Attendance, presence, or attendance recording is very important for every lecture, office or other organizational activities. Currently, recording student attendance activities at Mercu Buana University is by calling one by one the students by the lecturer, then the lecturer signs attendance on the special application form for lecturers that have been provided. For this reason, it is necessary to design a database to create the database. The analysis used is SWOT to compare between Gaudy Booch and Ivan Jacobson methods. This comparison is used to find out which method is appropriate in implementing a better database. Based on the database design that is made to further facilitate archiving of attendance, increase data efficiency. From the results of the research with class diagram description objects, the class diagram and database model that we use is from Ivar Jacobson, because it shows how actors from class diagrams point to Object-Oriented Software Engineering that has advantages that are easy to learn and have notation simpler.

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Analysis of denial of service attack on web security systems

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： S. Iswandi Walad Muhammad Zarlis M.IT Syahril Efendi Department of Information and Technology Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia. Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia.

In this study, an analysis of the Web security system from a Denial-of-Service Attack (DoS) with SYN Attack, Ping of Death, Land Attack, Smurf Attack and UDP Flood types. The server computer that will be tested for DoS attacks is a local web server including *** IP: 54.169.71.56, *** IP: 104.152.168.14, *** IP: 156.67.210.132, *** IP: 202.162. 207,101 and *** with IP Address 213.136.83.52 port 80. The attack on the local web server computer was carried out through attacking cloned computers as many as 5 zombie computers and then observing data traffic on each Web server computer. The test results use ping to the local web server computer, where the target web server sends a replay request time out and the check results in the Google Chrome browser are slow to inaccessible conditions. Ping access time the greater the packet, the greater the average time when accessing a local web server with 5,000 packets is 242.68 seconds, 10,000 packets is 310.28 seconds, 20,000 packets are 337.4 seconds, 30,000 packets are 377.2 seconds, 40,000 packets are 398.4 seconds, 50,000 packets are 545.4 seconds and 100,000 packets are above 600 seconds with the condition that the web server will recover after 600 seconds where the system recovery process depends on each local web server.

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