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检索条件"机构=Computer Science Department BINUS Graduate Program Computer Science"

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Developing an online information portal for enhancing society awareness of RPTRA (A case study RPTRA MAYA ASRI 13) 7

Developing an online information portal for enhancing societ...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Arifin, Yulyani Gunawan, Elizabeth Paskahlia Ohyver, Margaretha Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The long COVID-19 pandemic has limited the activities of Ruang Publik Terpadu Ramah Anak (RPTRA) such as environment cleaning, repair of RPTRA infrastructure, learning and others, as well as the lack of public awareness about the existence and importance of RPTRA to the public and foreign tourists encouraging the RPTRA to make changes in the dissemination of information about the activities carried out in the RPTRA through the use of technology to survive and grow even during the Covid-19 Pandemic. The methodology used in this research is Group Discussion Forum with the administrators of the RPTRA Maya ASRI 13 to learn about the needs and expectations of the RPTRA administrators regarding the web portal to be built. Then, the implementation of the conceptual model, development, and evaluation of the effectiveness of the RPTRA information web portal was carried out. The contribution in this research is to implement a conceptual model on the information portal built on the platform of the Website with the PHP framework, which is then evaluated on the results of the pre-test and post-test about RPTRA the Wilcoxon-Signed Rank Test to test the effectiveness of the information web portal for general users. The results obtained from these tests indicate that the RPTRA Information Web Portal can help the public learn more about the existence of RPTRA Maya Asri 13. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Portals

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Fuzzy eco-DSM for treating medical waste 2

Fuzzy eco-DSM for treating medical waste

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Utama, Ditdit Nugeraha Rustamaji, Eri Fauziyah, Amelia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System UIN Syarif Hidayatullah Jakarta Indonesia

Decision support model (DSM) is familiarly applied in a lot of areas and issues. The model is commonly benefited by decision maker to solve semi / unstructured problems. One of them should be used to treat medical waste by considering six selected parameters and three decision alternatives. In this study, environmental issue is main concerned aspect that the constructed model consequently becomes ecological DSM (eco-DSM). Three main methods are operated to construct the model;they are fuzzy logic, Euclidean distance, and full-factorial optimization methods. The model is analysed and designed via object oriented method. Lastly, the study shows simulation results based on empirical data of medical waste from one class 'A' state hospital in Jakarta, Indonesia. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Fuzzy logic

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijanarko, Bambang Dwi Fitria Murad, Dina Heryadi, Yaya Tho, Cuk Hashimoto, Kiyota BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket Campus Phuket83120 Thailand

ISBN: (纸本)9798350320954

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia's population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss. © 2023 IEEE.

关键词： Chemical activation

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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Fuzzy eco-DSM for road traffic engineering 2

Fuzzy eco-DSM for road traffic engineering

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Utama, Ditdit Nugeraha Nurlatifani, Sofie Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System UIN Syarif Hidayatullah Jakarta Indonesia

Numerous efforts have been reasonably done to eliminate the road traffic congestion, however it occurs every working day, particularly in metropolitan. This study positively proposed a model which is not only able to show the effect of road traffic congestion ecologically, but also can recommend the strategy for road traffic engineering with methodically elaborating the environmental attributes as a dominant aspects considered. It is called as ecological decision support model (eco-DSM). To construct the model, two main methods were operated and combined: fuzzy logic and water flow algorithm (WFA). The method fuzzy logic exploited to exterminate the bias facet on parameters, and the WFA utilised to model the road traffic congestion and vehicle flow and to optimize its velocity based on emission calculation. The constructed model itself was designed thru the method object oriented, and the empirical road traffic congestion data of the Juanda Street in Tangerang Selatan city were functioned. Finally, the model showed the simulation result for suggesting the best decision alternative for road traffic engineering. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Fuzzy logic

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Image Size, Color Depth, Age variant on Convolution Neural Network 1

Image Size, Color Depth, Age variant on Convolution Neural N...

引用

1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Pranoto, Hady Budiharto, Widodo Hendric Spits Warnars, Harco Leslie Matsuo, Tokuro Heryadi, Yaya Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781538694220

Facial recognition as of biometric authentication used in the field of security, military, finance and daily use is become a trend or famous, because of its natural and not intrusive nature. Many methods for face recognition such as holistic learning, the use of local features, shallow learning and deep learning, some methods are susceptible to variations in pose change, illumination, expression and age variation. State of the art of face recognition today is a deep learning technique that delivers high accuracy. In this paper author replicate an face recognition using deep learning architecture called OpenFace Convolutional Neural Network. In this research author make variation on the size of image, color dept and age, and see how that factor impact on accuracy of face recognition in that architecture. As the result from the research, the accuracy of a model depends on the image size, color depth, and age variation, but in OpenFace CNN that recognition still provides fairly good accuracy when reducing the size of image and color depth, as long as the image can still be detected on the landmark facial, so the alignment process can be done on the face image. © 2018 IEEE.

关键词： Face recognition

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A systematic literature review of machine learning application in COVID-19 medical image classification 7

A systematic literature review of machine learning applicati...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Daniel Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays 1

Systematic Literature Review: An Intelligent Pulmonary TB De...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Jimmy Cenggoro, Tjeng Wawan Pardamean, Bens Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aided diagnosis (CAD), particularly in medical images classification, significantly contribute to early TB detection. The current state-of-Art CAD for medical images classification applications using a method base on deep learning techniques. The problem faced in this deep learning technique is that, in general, it only uses a single modal for the model. In contrast, in medical practice, the data used for TB analysis not only focuses on images but also includes clinical data such as demographics, patient assessments, and lab test results. This systematic literature review describes different deep learning methods using single modal or multimodal techniques that combined images with other clinical data. We conducted a systematic search on Springer, PubMed, ResearchGate, and google scholar for original research leveraging deep learning for Pulmonary TB detection. © 2021 IEEE.

关键词： computer aided diagnosis

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