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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Dian Lestari Vandha Pradwiyasma Widartha Nirwana Haidar Wahyu Dirgantara Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012–2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9 % ), Conference Paper (4.2 % ), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), Computer science (2.6 % ), Environmental science (2.6 % ), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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The impact of copper oxide nanoparticles reinforced nanocellulose acetate membrane on antibacterial and dye removal Potency

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Results in engineering 2024年 23卷

作者： Yanuhar, Uun Suryanto, Heru Amin, Muhamad Maulana, Jibril Mohamad Lal, Mohammad Tamrin Caesar, Nico Rahman Study Program of Aquatic Resources Management Faculty of Fisheries and Marine Sciences Universitas Brawijaya East Java Indonesia Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Negeri Malang Jl. Semarang 5 Malang65145 Indonesia Department of Aquaculture Faculty of Fisheries and Marine Universitas Airlangga Jl. Dharmahusada Permai Surabaya60115 Indonesia Borneo Marine Research Institute Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Doctoral Program of Environment Science Post Graduate Program Universitas Brawijaya Jalan Veteran East Java Malang65145 Indonesia

With the rapid growth of the textile, leather, cosmetics, and pharmaceutical industries, concerns about pollutants in these sectors, especially dye pollutants, are increasing. This research characterizes bacterial nanocellulose acetate membranes made from Sargassum extract reinforced with copper oxide nanoparticles and tests their antibacterial properties and dye removal in water. Sargassum sp. extract was fermented using Acetobacter xylinum for 10 days, producing bacterial nanocellulose, which was then esterified to create bacterial nanocellulose acetate. Copper oxide nanoparticles were added to the bacterial nanocellulose acetate solution in varying concentrations, cast, and oven-dried. The membrane was analyzed using Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscope, antibacterial activity, and dye removal. Results showed uniform copper oxide nanoparticles distribution on the membrane surface. Structure analysis revealed diffraction peaks at 35.44° and 38.57°, indicating a reduced crystalline index but improved membrane effectiveness. Antibacterial tests showed a zone of inhibition against bacterial growth, increasing with higher copper oxide nanoparticle content. The addition of copper oxide nanoparticles also influenced the dye removal capacity for Metanil Yellow, Congo Red, Eosin Yellow, Methylene Blue, and Malachite Green. The kinetic analysis showed that the adsorption process follows a pseudo-first-order model, with higher correlation coefficients (R2) compared to the pseudo-second-order model. © 2024 The Author(s)

关键词： Fourier transform infrared spectroscopy

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia Computer science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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AI In Healthcare: Using Bayesian Networks To Predict Response To Immune Checkpoint Blockades

AI In Healthcare: Using Bayesian Networks To Predict Respons...

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National Conference on Radio science (NSRC)

作者： Ahmed H. Saadawy Aly F. Hissam Eman Badr Omar M. Fahmy Biomedical Sciences Program Zewail City of Science and Technology Giza Egypt Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Omar Mamdouh Fahmy Electrical Engineering Dept. Badr University in Cairo Giza Egypt

Cancer remains a leading cause of death worldwide, with its heterogeneous and patient-specific nature complicating treatment. Immunotherapy, especially immune checkpoint blockades (ICB), shows promising potential by reactivating the immune system to detect and destroy cancer cells. However, their effectiveness largely depends on the patient’s clinical and molecular characteristics, making them unsuitable for everyone. This highlights the importance of developing predictive and interpretable models that could distinguish potential responders from non-responders. This study aims to utilize gene expression data to predict patients’ responses to ICB and identify key genes whose expression is associated with treatment outcome using probabilistic Bayesian networks (BNs). To address the high-dimensional nature of gene expression data, we propose and compare the use of machine learning-based feature selection (FS) and clustering by weighted gene co-expression network analysis (WGCNA). To our knowledge, this is the first study to apply BNs along with FS and WGCNA dimensionality reduction techniques to tackle ICB response using transcriptomic data. We achieved a maximal classification accuracy of 75% and identified eight genes and two gene clusters directly influencing ICB response. These results may offer more biological insights and mechanisms underlying ICB response. The proposed pipeline will contribute to guiding treatment decisions, minimizing harmful side effects, and reducing the financial costs associated with ineffective treatments.

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning Computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning Computer Vision holds tremendous benefits for homeowners.

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Identification of Beef Freshness Using Linear Discriminant Analysis (LDA) Algorithm and Extraction of HSV Values

Identification of Beef Freshness Using Linear Discriminant A...

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Electronic and electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Riduwan Napianto Agung Tri Prastowo Agung Deni Wahyudi Wajiran Yohanes Egi Pratama Yudoutomo Arico Ayani Suparto Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Information Technology Education Study Program STKIP PGRI Situbondo Situbondo Indonesia

ISBN: (纸本)9781665465427

To determine the quality of beef, it is usually seen using a flashlight and matching it with a predetermined color standard. However, this method is time consuming and the results are inconsistent due to human visual limitations. This study aims to identify the freshness of beef based on its color characteristics using feature extraction method with HSV and LDA method for identification. The identification process begins with the transformation process from RGB images to L*a*b, then image segmentation serves to separate one object from another and image identification uses the LDA method with feature extraction using HSV. The use of color feature extraction with the HSV algorithm to assist in obtaining information from the colors contained in the image for easy identification. Furthermore, the LDA algorithm has a function to obtain the maximum projection to obtain a smaller dimensional space so that the desired patterns can be separated through the boundary line of a linear equation so as to form a certain class or group. Based on the test results using the confusion matrix, it produces a precision value of 80%, recall of 84%, and accuracy of 82%.

关键词： Visualization Image segmentation Image color analysis Shape Feature extraction Mathematical models Linear discriminant analysis

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Effect of feedback on learning motivation of primary teacher education students in primary school physics courses 10

Effect of feedback on learning motivation of primary teacher...

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10th National Physics Seminar, SNF 2021

作者： Sianipar, L.K. Sitompul, H. Sanjaya, L.A. Puspa, R.W.D. Pertiwi, W.A. Qoiriyah, W. Department of Physics Education Universitas Pelita Harapan Tangerang15811 Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Jakarta Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Islamic Education Management Study Program Faculty Islamic of Education and Teacher Training Muhammad Azim Institute Jambi Indonesia Informatics Engineering Study Program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

Based on the results of the questionnaire distributed to Primary Teacher Education Students, 96% of students said that Physics is difficult to understand because the material is very complex, many formulas, especially the learning process is done online. This study aims to describe the influence of providing feedback on the motivation of Primary Teacher Education Students in primary school physics courses. The method used in this research is descriptive qualitative. Meanwhile, the instrument used was a questionnaire which was distributed to 115 elementary school education students. The result of the research shows that giving feedback consistently is able to motivate students. This can be seen from the results of the questionnaires distributed, each indicator is of very good value. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Transforming disposable masks to sustainable gasoline-like fuel via pyrolysis

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Environmental Advances 2024年 15卷

作者： Sari, Mega Mutiara Inoue, Takanobu Salsabilla, Vionna Chantika Septiariva, Iva Yenis Mulyana, Rahmad Prayogo, Wisnu Arifianingsih, Nur Novilina Suhardono, Sapta Suryawan, I. Wayan Koko Department of Environmental Engineering Faculty of Infrastructure Planning Universitas Pertamina Jakarta 12220 Indonesia Department of Architecture and Civil Engineering Toyohashi University of Technology Aichi Toyohashi 441-8580 Japan Civil Engineering Study Program Faculty of Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Department of Civil Engineering Faculty of Engineering Universitas Negeri Medan North Sumatra Medan Indonesia Department of Environmental Engineering Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Bandung 40116 Indonesia Department of Environmental Science Universitas Sebelas Maret. Central Java Surakarta Indonesia

The past COVID-19 pandemic has led to a surge in the use of disposable masks, posing a significant environmental challenge due to the swift accumulation of waste. This study explores the potential of pyrolysis to transform this waste into valuable pyrolysis oil with characteristics akin to gasoline, addressing both waste management and energy derivation concerns. Pyrolysis, a thermal decomposition process, was applied to mask materials, producing an oil with a high calorific value of 45.93 MJ/kg. The chemical makeup of this pyrolytic oil was meticulously characterized using Pyrolysis-Gas Chromatography-Mass Spectrometry (PY-GC/MS) and Fourier-transform Infrared Spectroscopy (FTIR), confirming the presence of hydrocarbons similar to those found in gasoline. Achieving an efficiency of 41.865 %, the pyrolytic approach demonstrated a promising potential for not only managing mask waste but also creating an alternative energy carrier. However, the study advises that before considering this pyrolytic oil for engine use, a thorough evaluation of its combustion quality, engine compatibility, and environmental impact is essential. This investigation advances the discussion on sustainable waste management, offering insights into the generation of alternative fuels from daily waste streams, while also highlighting the necessity for comprehensive analyses to ensure the practicality and efficiency of such novel energy solutions. © 2023 The Author(s)

关键词： Alternative fuel sources Disposable masks Energy recovery techniques Medical waste valorization Thermochemical conversion

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T*$\boldsymbol{\varepsilon}$—Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems

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IEEE Robotics and Automation Letters 2022年第2期7卷 4102-4109页

作者： Doron Pinsky Petr Váňa Jan Faigl Oren Salzman Technion Autonomous Systems Program (TASP) Technion Haifa Israel Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Computer Science Technion Haifa Israel

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both (i) computationally expensive and (ii) unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by $\boldsymbol{1+\varepsilon}$ times the cost of the optimal solution for any user-provided $\boldsymbol{\varepsilon}$ ) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.

关键词： Costs Planning Robots Image edge detection Vehicle dynamics Runtime Heuristic algorithms

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