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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Niccosan Tommy, Ray Kurniawan, Christian Makalew, Brilly Andro Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

This research is aimed to develop and evaluate an Electronic Human Resource Management (E-HRM) system called WorkEv to assist in monitoring and improving employee's performance. The application was developed based on Website using Django and Bootstrap framework. The evaluation process was conducted using Delphi method. Based on the results of the User Acceptance Test (UAT) with the Delphi method, the WorkEv application can be accepted by the target user because the design and development results meet the expectations of the target user's needs, namely easy-to-use navigation and control, simple and clear User Interface (UI) design, and the existing functions and features are effective in helping small to medium scale businesses/businesses in performing human resource management (HR). © 2022 IEEE.

关键词： Acceptance tests

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New Architecture Quantum Perceptron using Quantum Circuit

New Architecture Quantum Perceptron using Quantum Circuit

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2021 International Conference on Data science, Artificial Intelligence, and Business Analytics, DATABIA 2021

作者： Solikhun, Solikhun Efendi, Syahril Zarlis, Muhammad Sihombing, Poltak Universitas Sumatera Utara Doctoral Program Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9781665426800

the learning algorithm that is not optimal is due to the incomplete use of quantum in the perceptron quantum learning algorithm which is the background of this research. Previous research has shown that the proposed architectural form still needs to increase its probability value to be more optimal. The previous probability was 90.7%. The proposed model is a quantum circuit architecture in a quantum perceptron algorithm consisting of a quantum bit gate. The researchers conducted training and testing of the proposed quantum circuit architecture using quantum computer of the IBM Quantum Experience. This research produces a perceptron quantum architecture model using a quantum circuit that can solve problems regarding data classification. After measuring the proposed quantum circuit architecture, it shows a 100% probability. After training and testing, the probability is 100%, the same as that at the time of measurement. Then it produces the same output of |111100000000000000〉. The resulting probability shows a significant increase from the previous 90, 7%. © 2021 IEEE.

关键词： Probability

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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Enhancing User Interface and Experience in Skin Type Classification and Skincare Product Recommendation System for Young Adults 9

Enhancing User Interface and Experience in Skin Type Classif...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Himawan, Felicia Soerjadi, Vania Paramitha Handoyo, Yonathan Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The escalating interest of skincare among Indonesian young adults reflects the rising trend in the skincare industry. However, this heightened interest also brings several challenges, like the lack of knowledge and confusion when selecting the right skincare products for our skin type. This underscores the need for an effective and efficient skin type classification and product recommendation system. This paper addresses this challenge by focusing on enhancing a skincare application's user interface and overall experience. This research employed User-Centered Design (UCD), implementing Design Sprint, and Goal-Directed Design (GDD) methodologies to achieve a high value of user satisfaction. The Sprint process is used to create the design prototype efficiently, and the Goal-Directed method is performed to create specific, measurable goals. Our evaluation shows that an iterative design process based on user feedback results in higher user satisfaction. The analysis indicated a significant increase of approximately 25% in both effectiveness and efficiency among users when using the application. Initially, the first iteration reveals a low value of approximately 59% in overall effectiveness and efficiency. However, a substantial boost to around 83.5% was seen after a redesign. This improvement proved that User-Centered Design methodologies highly influence user perception and satisfaction. © 2024 IEEE.

关键词： design sprint goal-directed design skincare UI/UX user-centered design

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Trend analysis in competitive programming using topic modeling 9th

Trend analysis in competitive programming using topic modeli...

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

作者： Ramahalim, Albert Yulius Karuniawan, Christoffer Edbert Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora. © 2024 The Authors.

关键词： Modeling languages

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Implementation of Gamification to Help Teenagers Prevent Diseases 9

Implementation of Gamification to Help Teenagers Prevent Dis...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Haryanto, Michael Djaja, Fabian Putera Chrisandy, Michael Achmad, Said Moniaga, Jurike V Bina Nusantara University Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Adolescence is an important part of growing up to become an adult;keeping adolescent physique healthy is very important because poor dietary choices and a sedentary lifestyle will lead to diseases that they shouldn't have experienced yet. That is why teenagers need to be engaged to do more health- promoting behaviors, one way is by implementing gamification. This paper seeks to study which kind of gamification elements can be implemented from past research and whether the results were effective or not to prove whether gamification is a valid and effective strategy. At the same time, we are creating a gamified prototype for the mobile platform. The methodology starts with a systematic review of PubMed, MDPI, and Google Scholar to get a general idea of which gamification elements are the most implemented. Then a prototype will be made with the most used gamified elements from the systematic review. And then participants ranging from 10 - 19 years old will test the prototype and will give their opinions via a survey, From this, it is proven by our own gamified prototype survey results that using gamification as a way to engage teenagers is a fun and engaging way to do more health-promoting behaviors with a mostly positive feedbacks with the average rating of 8.33 out of 10 given from the participants. © 2024 IEEE.

关键词： Gamification

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Quantitative analysis of sign language translation using artificial neural network model 9th

Quantitative analysis of sign language translation using art...

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

作者： Wijaya, Fendy Dahendra, Leonardo Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system. © 2024 The Authors.

关键词： Neural machine translation

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The future of virtual reality: Prospect and problems 9th

The future of virtual reality: Prospect and problems

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

作者： Lie, Andrew Alfonso Buntoro, Owen Tamashi Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors. © 2024 The Authors.

关键词： Virtual environments

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Harnessing Deep Learning for Ocular Disease Diagnosis 9th

Harnessing Deep Learning for Ocular Disease Diagnosis

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

作者： Ryan, Jessica Nathaniel, Dave Andrew Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Vision impairment, often caused by preventable ocular diseases can be challenging to diagnose accurately and prone to human error. Automation using technology, particularly deep learning, offers a promising solution to aid in accurate and efficient disease detection. This study explores the use of different CNN models specifically VGG-16, VGG-19, ResNet-50, and ResNet-152v2, for detecting ocular diseases. Simple fine-tuning is applied to these models, and their performance is compared to identify the most effective model. The purpose is to show how different models contribute to establishing reliable illness detection systems. The results reveal that most of these models perform well with even minimal fine-tuning. Among the models, ResNet-152v2 achieved the highest training accuracy of 90.36% demonstrating its capacity to learn from the training data. In contrast, ResNet-50 offered a more balanced performance with marginally lower accuracy, making it a robust choice for general application. © 2024 The Authors.

关键词： classification deep learning ocular diseases

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Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting 9th

Enhancing Waste Classification with YOLOv8 Models for Effici...

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

作者： Vieri, Deverel Susanto, Rendy Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%. © 2024 The Authors.

关键词： Contrastive Learning

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