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

作者： Sirait, Tamsir Hasudungan Meyliana Arifin, Yulyani Yulianto, Budi Bina Nusantara University Binus Graduate Program Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Simulation-based learning is one of the most powerful tools to support the educational process. This learning offers an opportunity to learn based on experiences that are very similar to real-world environments. In the context of logistics, which can be considered a complex system, significant efforts are required to enhance students' understanding and practical skills. For that reason, simulations are used so that benefits such as skills and knowledge in the field of logistics can be achieved. However, there are currently several challenges in developing simulation-based learning in higher education. These challenges include the need for simulations that are appropriate for the current learning context, where the students are generally from Generation Z, and how to improve their learning quality in the era of Industry 4.0. To address these challenges, more suitable simulations need to be developed, and higher education institutions must carefully examine how to develop them, including identifying the essential components in the development of simulations for logistics learning. In this research, a systematic literature review using the PRISMA method and also creating a conceptual model of simulation that better meets the current needs of logistics education will be proposed. The results of this research can be used as a basis for developing updated simulations to better prepare students for the challenges of Industry 4.0. © 2025 IEEE.

关键词： industry 4.0 logistics simulation development simulation learning simulation learning component

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition 8

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Rabiha, Sucianna Ghadati Kartowisastro, Iman Herwidiana Setiawan, Reina Budiharto, Widodo Bina Nusantara University Binus Graduate Program - Computer Science. Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Science Department Binus Graduate Program - Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665474481

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture. © 2022 IEEE.

关键词： Face recognition

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth

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Procedia computer science 2024年 245卷 834-843页

作者： Darel Yeshe Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta Indonesia 11480

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study.

关键词： Machine Learning Predicting Regression Algorithm Technology Education Artificial Intelligence

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Digitalised human needs to support intra-organisational knowledge sharing among knowledge workers

Digitalised human needs to support intra-organisational know...

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作者： Herlina, Maria Grace Iskandar, Karto Hadipoespito, Randy Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An organisation's success relies more on dynamic knowledge management (KM). A successful knowledge management system is inextricably linked to employee behaviour, namely intra-organisational knowledge sharing. Individual needs significantly influence human behaviour, such as innovation. Most need-based motivation theories make similar assumptions regarding basic needs such as achievement, affiliation, and power. Using the Rasch Model and structural equation modelling (SEM) methodologies, this research transforms human needs into digital form to enhance intra-organisational knowledge sharing among knowledge workers. The study hypothesis employs SEM. It indicates that the need for achievement, affiliation, and power influence knowledge sharing significantly, with the strongest significant influence being the need for power. Various information and communication technology (ICT) tools have recently been developed based on web, mobile, and desktop applications. The study mapped the three categories of motivators based on the appropriate ICT tools for intraorganisational knowledge sharing. © 2024 Inderscience Enterprises Ltd.

关键词： Motivation

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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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A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem 13

A Hybrid Approach with BRKGA and Data Mining for the Early/T...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mendonça, Israel Fatyanosa, Tirana Noor Aritsugi, Masayoshi Silva, Pedro Henrique González Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan Brawijaya University Faculty of Computer Science Malang Indonesia Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality. © 2024 IEEE.

关键词： Scheduling algorithms

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

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Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

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Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

Regional Classification of Indonesian Folk Songs Based on K-...

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

作者： Mahardhika, Ferdinand Warnars, Harco Leslie Hendric Spits Nugroho, Anto Satriyo Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Organisasi Riset Elektronika Dan Informatika Jakarta10340 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

One of the biggest challenges in Music Information Retrieval (MIR) is classification. MIR is a subset of Machine Learning (ML), where the methods found in computer science can be used to solve problems in the music domain area. Indonesia is an archipelago country inhabited by around 1,340 ethnic groups spread across 38 provinces. This ethnic group has a variety of cultures, including folk songs, and almost every area in Indonesia has folk songs. In this study, we conducted a regional classification of 500 Indonesian folk songs from 10 regions in Indonesia. We build a model for regional classification tasks using the MIR approach. This model combines feature selection and dimension reduction capabilities. By using hyperparameter tunning and a cross- validation of 5 folds, the result of our proposed model for regional classification of Indonesian folk songs using K-NN with PCC-LDA can obtain an accuracy rate of 84.7%. © 2023 IEEE.

关键词： Music

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Modified Recommender Algorithm based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Methodology with Entropy Weighting Method (EWM) 4

Modified Recommender Algorithm based on Technique for Order ...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Pacaon, Mark Reden S. Ballera, Melvin A. Technological Institute of the Philippines Graduate Program Manila Philippines Technological Institute of the Philippines Department of Computer Science Manila Philippines

ISBN: (纸本)9798350376111

This research exhibits an algorithm adapted from the Technique for Order of Preference by Ideal Solution (TOPSIS) methodology, integrating the Entropy Weighting Method (EWM) for objective and efficient recommendation processes. Decision-making methodologies have indicated gaps of subjective influence affecting performance values on several implementations with additional limitations on the flexibility of handling complex problems that require multiple factors while maintaining an acceptable running time. The study takes this into account and formulates an adaptable and efficient framework that offers objective and data-driven recommendations for diverse situations. Future endeavors include subjecting the framework to real-life scenarios and testing its effectiveness. © 2024 IEEE.

关键词： decision-making entropy weighting method modified algorithm multicriteria TOPSIS

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