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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Kardawi, Muhammad Yusuf Gunawan, Alexander Agung Santoso Bina Nusantara University Profesional Engineer Program Faculty of Engineering Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Player evaluation and learning outcomes of "NusaVenturas": A serious game for enhancing player knowledge and interest in Indonesian traditional foods 9th

Player evaluation and learning outcomes of "NusaVenturas": A...

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

作者： Sanjaya, Vito Sebastian Tanoto, Nicholas Valenthinus Tandinata, Nicholas Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The popularity of Indonesian traditional food has been declining due to the increasing influence of foreign cuisine. This study presents the development method that combining Game Development Life Cycle (GDLC) with Experiential Game Learning Design Framework and evaluation of a serious game "NusaVenturas" using usability, learning outcome, and Game Experience Questionnaire (GEQ) that designed to introduce, promote interest, educate, and preserve knowledge of Indonesian traditional foods. "NusaVenturas" is a unique 2D serious game that integrates puzzle-platformer elements with time-management cooking mechanics. The study involved 37 participants aged 18 to 23. Evaluation results indicate that "NusaVenturas" significantly enhances players' knowledge of Indonesian traditional foods, with statistical analysis confirming the significance of the findings. © 2024 The Authors.

关键词： Food ingredients

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Development of "Peter's First-Aid Adventure" Virtual Reality-Based Serious Game in First-Aid Education: Usability Analysis of Virtual Reality Interaction Tools 9th

Development of "Peter's First-Aid Adventure" Virtual Reality...

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

作者： Rainer, Alexander Setiono, Angeline Leonardrich, Kevin Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Incidents that threaten lives can occur unexpectedly, highlighting the importance of immediate first aid. Studies show that Virtual Reality (VR)-based Serious Games (SG) are excellent alternatives for learning, especially for physical training, as they provide immersive and realistic scenarios where users can learn and practice their skills in a safe environment. Therefore, this research proposes the design of a VR-based SG for learning first aid procedures. A total of 31 participants tested the game's usability using both VR controllers and hand tracking, playing the game with both tools in random order, and then completing the XR-GUQ and Interaction Categories questionnaire. The results indicate that controllers offer higher usability than hand tracking. However, the game can still effectively utilize both interaction tools, given their satisfactory usability ratings. © 2024 The Authors.

关键词： Virtual environments

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File Classification Containing Secret Key in Git Repository Using Character Level CNN and Shannon Entropy 4th

File Classification Containing Secret Key in Git Repository ...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Muhammad, Rafie Musdholifah, Aina Master Program in Computer Science FMIPA UGM Yogyakarta Indonesia Department of Computer Science and Electronics FMIPA UGM Yogyakarta Indonesia

ISBN: (纸本)9789819729760

Current technological developments are directly proportional to the number of information technology projects made. This project is usually in the form of a program formed from a code. To facilitate version control and contributions between developers, git tools are usually used which are placed on an online platform such as Github. If a repository on an online platform contains sensitive information such as secret key variables and the repository can be accessed by the public, then this can be used to harm the company or organization that owns the repository. Several ways have been done before to detect this secret key variable by using the regex method and using machine learning to minimize false positive results. In this research, we will try to build a text or file classification model that contains secret key variables using the character level CNN and Shannon entropy. The Shannon entropy algorithm is used during pre-processing to retrieve the group of words with the largest entropy value and the CNN character level will be used because it is considered suitable for this case since the value of the secret key is abstract and does not require a sequence context for the said secret key in a file. The results of this study were able to obtain an accuracy value of 96.1% for the accuracy value for the classification of files containing secret key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Character level CNN Git repository Secret key Shannon entropy Text classification

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Tree Counting with Deep Forest Algorithm for Kulon Progo District in Yogyakarta, Indonesia Using Pleiades Satellite Imagery

Tree Counting with Deep Forest Algorithm for Kulon Progo Dis...

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2024 Arab ICT Conference, AICTC 2024

作者： Wijaya, David Aditama, Ibrahim Seto Austen, Stanley Armando Widjaja, Calvin Surya Jabar, Bakti Amirul Irwansyah, Edy School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Tangerang Selatan15323 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Bandung40523 Indonesia Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350342086

Trees are one of the most important living things on the planet. Trees help by being one of the largest producers' oxygens on planet, by absorbing groundwater and by maintaining soil fertility. Trees can also be one of the keys to reducing existing air pollution. Therefore, the number of existing trees must be maintained. The first step in maintaining the number of existing trees is to count or map them. One of the easiest ways is to do this use an algorithm. This study uses a tree-counting algorithm called Deep Forest. Deep Forest is a Python package that can detect trees through RGB satellite imagery. This study used satellite imagery from Pleiades on an area in Kulon Progo district, Yogyakarta, Indonesia for the image-one of the four regencies within Yogyakarta Province in Indonesia. In addition to the Deep Forest algorithm, this research also used an application provided by Esri called ArcGIS Pro. With this application, data preprocessing such as labeling the data and creating the deep learning dataset for research is much easier, while ArcGIS Pro provides tools for labeling and exporting the data to jupyter notebook. The result obtained using the Deep Forest algorithm is the F1 score with an accuracy of 0.76 for the first experiment, 0.774 for the second experiment, and 0.779 for the final experiment. © 2024 IEEE.

关键词： Groundwater pollution

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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MASKED AUTOENCODERS ARE EFFICIENT CONTINUAL FEDERATED LEARNERS 3

MASKED AUTOENCODERS ARE EFFICIENT CONTINUAL FEDERATED LEARNE...

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3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Paul, Subarnaduti Frey, Lars-Joel Kamath, Roshni Kersting, Kristian Mundt, Martin Department of Computer Science TU Darmstadt Darmstadt Germany Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Darmstadt Germany

Machine learning is typically framed from a perspective of i.i.d., and more importantly, isolated data. In parts, federated learning lifts this assumption, as it sets out to solve the real-world challenge of collaboratively learning a shared model from data distributed across clients. However, motivated primarily by privacy and computational constraints, the fact that data may change, distributions drift, or even tasks advance individually on clients, is seldom taken into account. The field of continual learning addresses this separate challenge and first steps have recently been taken to leverage synergies in distributed settings of a purely supervised nature. Motivated by these prior works, we posit that such federated continual learning should be grounded in unsupervised learning of representations that are shared across clients;in the loose spirit of how humans can indirectly leverage others’ experience without exposure to a specific task. For this purpose, we demonstrate that masked autoencoders for distribution estimation are particularly amenable to this setup. Specifically, their masking strategy can be seamlessly integrated with task attention mechanisms to enable selective knowledge transfer between clients. We empirically corroborate the latter statement through several continual federated scenarios on both image and binary datasets. © 2024 CoLLAs. All Rights Reserved.

关键词： Federated learning

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Envisioning Risk Management for Smart Contracts: A Conceptual Model

Envisioning Risk Management for Smart Contracts: A Conceptua...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

This study proposes a smart contract risk management model built on the NIST Risk Management Framework (RMF) to help identify, assess, and manage the risks of smart contracts. While smart contracts are beloved as a means to automate and disintermediate business processes, their security vulnerabilities can be critical. The main issue discussed in this paper is the lack of a holistic approach to risk management smart contracts. The resulting framework consists of six steps: Risk identification, assessment, prioritization, mitigation, testing, and continuous monitoring (and was developed through reviewing existing literature on smart contract security and the NIST RMF). It is recommended that a case study be performed to prove the proposed model's effectiveness in managing the risks of smart contracts and minimizing financial losses and reputational harm. The paper presents a risk management framework for smart contracts to increase trust and adoption to enhance security while reducing financial losses and reputation damage. This has wider implications for the security of smart contracts and can be used as a starting point for future work. This study is expected to significantly contribute to smart contract security by introducing an organized way to address these contracts' risks using the NIST RMF. © 2023 IEEE.

关键词： Smart contract

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Investigating the Impact on Data Recovery in computer Forensics 1

Investigating the Impact on Data Recovery in Computer Forens...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

Defragmentation can potentially be employed as a tactic by perpetrators to conceal, misrepresent, or eliminate digital evidence. This study explores the effects of minor defragmentation, a potential method to conceal digital evidence, on recovering file system data in digital forensics. Our investigation sought to determine the influence of minor defragmentation on the effectiveness of data recovery and to identify methods that can augment the success rate post-defragmentation. We limited the scope of this study to defragmentation in Hard Disk Drives (HDDs), solid-state drives (SSDs), and USB drives. A mixed-method approach employs a literature review, case studies, and controlled experiments. Comparative analysis was used as the main data analysis technique to investigate its impact. Preliminary findings suggest that minor defragmentation hampers data recovery;however, certain strategies can augment success rates. These results can significantly influence the development of data recovery policies, particularly those of digital forensic analysts and law enforcement. The primary objective of this study is to bolster the efficiency and dependability of file system data recovery post-defragmentation while upholding ethical and legal standards. © 2023 IEEE.

关键词： Recovery

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

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