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6th International Conference on Intelligent Sustainable Systems, ICISS 2023

作者： Rusdi, Mikail Rifqi Enrico, T. S. Gregorio Salomo, Austin Ordell Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Undergraduate Program-Computer Science Jakarta Indonesia Computer Science Department Binus University BINUS Graduate Program – Doctor of Computer Science Jakarta11480 Indonesia Advanced Institute of Industry Technology Tokyo Japan

ISBN: (纸本)9789819917259

Global technological improvements are accelerating and influencing numerous industries, including education. Using machine learning, technological innovations can be utilized in the education industry. Machine learning is a subset of artificial intelligence (AI) that enables computers or teaching machines to make intel-ligent decisions by learning from all past data. The framework for machine learning involves acquiring and retaining a large quantity of data and translating it into orga-nized knowledge. AI advancements enable educators to acquire a deeper knowledge of their students’ learning progression. Using Personalized Machine Learning can increase the efficiency of student learning by analyzing their performance in real time and modifying teaching techniques and the curriculum based on this information. It helps to have a tailored approach that adapts to the individual in order to provide a better education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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Synthesis of coral-like structures of Pr-Yb co-doped YIG: Structural,optical, magnetic and antimicrobial properties

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Journal of Rare Earths 2024年第3期42卷 543-554,I0004页

作者： A.Fernandez Francisca P.Araujo Y.Guerra S.Castro-Lopes J.Matilla-Arias Idglan S.de Lima Edson C.Silva-Filho Josy A.Osajima F.Guerrero R.Pena-Garcia Federal University of Piau Graduate Program in Materials Science and EngineeringTeresinaPIBrazil Federal Rural University of Pernambuco Academic Unit of Cabo de Santo AgostinhoCabo de Santo AgostinhoPEBrazil Federal University of Piaui Department of PhysicTeresinaPBrazil Federal University of Pernambuco Graduate Program in Materials ScienceRecifePEBrazil University of Granma Departmentof Basic Sciences and Applied InformaticsFaculty of Technical SciencesBayamoCuba Federal University of Amazonas Department of PhysicManausAMBrazil Federal University of Pernambuco Department of PhysicRecifePEBrazil

Coral-like structures of the Y_(3-x)Pr_(x)Fe_(5-y)Yb_(y)O_(12),(0.00 ≤ x ≤ 0.04, 0.00 ≤ y ≤ 0.02) compound were synthesized using the sol-gel method. Structural investigation certified the YIG cubic crystal structure formation, without any secondary phase. It is shown that, the relatively large ionic radius of the dopant cations results in an expansion of the lattice parameter, variations in the Iona-O-Iondangle, Iona-O,Iond-O and Ionc-O bond distances and decrease in the average crystallite size. Fourier transform infrared(FTIR) and Raman measurements are essential to testify the single-phase formation of YIG crystal structure and are observed changes in the stretching and vibrational modes, respectively. The morphological study, energy dispersive spectroscopy(EDS) spectra and textural properties show corallike structures, peaks associated with Pr^(3+) and Yb^(3+) atoms and the effect of dopants on surface area,diameter, and pore volume, respectively. The optical analysis from diffuse reflectance spectra witnessed an increase in the optical gap band, a decrease in Urbach energy and blue shift in the charge transfer,correlated with the expansion of the unit cell due to the dopant's insertion in the YIG structure. A typical ferrimagnetic behavior is exhibited by the Y_(3-x)Pr_(x)Fe_(5-y)Yb_(y)O_(12)compound. The saturation magnetization(M_(s)), cubic anisotropy constant(K_(1)) and coercive field(H_(c)) increase with the Pr^(3+)cations content, as consequence of their magnetic nature and distribution around of Fe^(3+)ions due to the coexistence with the Yb^(3+). Finally, for the first time, antibacterial tests by mean of the direct contact method were performed for YIG co-doped with Pr^(3+)and Yb^(3+)and it is shown that, relatively high dosages of Pr^(3+) cations favored the activity against S. aureus, therefore, a new biological property for YIG doped with rare earths is presented.

关键词： Yttrium iron garnet(YIG) Coral-like structures Rare earths Band gap energy Magnetic properties Antimicrobial properties

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Fuzzy Risk Model for Research and Development department in Clinical Laboratory 9

Fuzzy Risk Model for Research and Development Department in ...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Legowo, Nilo Sarjdono, Wahyu Susanto, Eddy Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Information security risk is of utmost importance and a crucial concern, particularly within a clinical laboratory responsible for managing sensitive public health information. Various endeavors have been undertaken by institutions to tackle this pressing challenge effectively. This research seeks to develop a computer-based decision model for assessing information security risks. The model is scientifically constructed using the fuzzy logic method as its core approach and designed through an object-oriented approach. Impressively, the model successfully simulates 31 risk scenarios with an accuracy rate of 93.55%. © 2023 IEEE.

关键词： Fuzzy logic

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Hoax Classification in Imbalanced Datasets Based on Indonesian News Title using RoBERTa 3

Hoax Classification in Imbalanced Datasets Based on Indonesi...

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3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Muliono, Yohan Gaol, Ford Lumban Soewito, Benfano Warnars, Harco Leslie Hendric Spits School of Computer Science Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Hoaxes are something that can not be avoided, especially in Indonesia, where the literacy rate in Indonesia is quite low, they are easy to believe in news without doing fact check. The worst thing is that news that is trusted by the public may not be read completely through the entire content. They believe, from the title alone could already covers the entire content of the news. Media in the other side, are also competing to make controversial titles so that their traffic is improved. The research that will be carried out by us is where we can classify a news whether it is a fact or a hoax from the title alone. The RoBERTa (A Robustly Optimized BERT Pretraining Approach) model will be used in this study, because in several previous studies RoBERTa has proven to be good for classification. The accuracy achieved in this study also reached 99.52% with an accuracy validation of 93.84% which shows that even with an imbalanced dataset the classification shows a promising result by using the RoBERTa model which data is balanced using the undersampling method. © 2022 IEEE.

关键词： Classification (of information)

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical Engineering and informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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Server Cloud Based Security Surveillance Systems: a Design science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Angkasa, Abby Rasya Zahra, Amalia Fung, Wai-Keung Binus Graduate Program Computer Science Department Jakarta11480 Indonesia Cardiff Metropolitan University Cardiff School of Technologies Engineering Department Cardiff United Kingdom

ISBN: (纸本)9798331508579

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences. © 2024 IEEE.

关键词： Emotion Recognition

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Leveraging EEG Signals and Machine Learning for Schizophrenia Classification 6

Leveraging EEG Signals and Machine Learning for Schizophreni...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elfarsy, Abdelrahman El-Metwally, Sara Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

ISBN: (纸本)9798350373875

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. This paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. The dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. The three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96%), while SVM demonstrated strong precision and recall (95%). These findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis. © 2024 IEEE.

关键词： Support vector machines

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