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Empowering Military in Tactical and Warfare Area with Virtual Reality Technology: A Systematic Literature Review 8

Empowering Military in Tactical and Warfare Area with Virtua...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Steven, Lonard Hauw, Jason Kenneth Keane, Muhammad Billy Gunawan, Alexander Agung Santoso Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The rapid advancement of virtual reality technology offers numerous real-world benefits across various sectors worldwide, including the military. As early adopters of this technology, the military has found multiple medical and practical applications. However, the research contributions of virtual reality technology, particularly in practical military applications, remain limited. This is primarily due to limited data availability and a lack of interest from researchers and military institutions. To investigate this issue, the authors are conducting a systematic literature study focused on practical military virtual reality, specifically tactical and combat operations. Following the PRISMA framework and employing searching and inclusion-exclusion techniques, the authors identified twenty-five relevant research outputs that extensively discuss these topics, both qualitatively and quantitatively. Notably, fifteen of these studies focus on combat and weapon training simulators, and interestingly, researchers are beginning to integrate other technologies such as robotics, big data, haptics, body area networks, and signal processing. The majority of the quantitative research conducted in this area has demonstrated success, with eight out of ten papers showing positive outcomes. To improve research quality, the authors propose two main solutions: implementing combat and weapon training within the military system and encouraging innovative and in-depth research in this domain. In conclusion, the analyzed virtual reality products can serve as a platform for military forces to support personnel in enhancing and maintaining their personal development and skills. © 2023 The Authors. Published by Elsevier B.V.

关键词： military simulator software tactical virtual reality warfare

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Building Serious Game Design to Prepare University Students for The World of Work 9

Building Serious Game Design to Prepare University Students ...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Steven Malik, Victor C. Harefa, Jeklin Alexander Chowanda, Andry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350304084

Pandemic hits the world catastrophically, leading to the disruption on our everyday life including how students learn in the pandemic era. Most of the students nowadays have difficulties preparing the way to face the world of work. This research proposes a serious game design to prepare university students for the world of work by combining the Six Facets of Serious Game Design and Adam's Ernest Game Design. Six Facets of Serious Game Design is the design pattern for serious games (e.g., pedagogical games) [1]. Two systems (i.e. the serious game and the baseline system) were designed, deployed and evaluated to evaluate the effectiveness of the proposed serious game design. The serious game design was built on an Android mobile phone. Moreover, a simple simulation that has identical scenarios with the serious game was also built as the baseline in the evaluation process. Thirty-seven respondents who will be or have been entering the workplace for their first time participated in the evaluation process. A Wilcoxon rank test was applied to the data as the data does not come from a normal distribution. The results show that the proposed serious game design provides more effective results than the baseline. The user experiences' score provided by the serious game design system is statistically higher than the baseline system. Moreover, the serious game system increases the learners' motivation compared to the baseline system. © 2023 IEEE.

关键词： Game design

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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Implementation of Augmented Reality for Solar System Subject in Primary school 8

Implementation of Augmented Reality for Solar System Subject...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Dirgantara Deha, Muhammad Thalenta Santoso, Roy Jones Tho, Cuk School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

This study investigates the implementation of Augmented Reality (AR) technology in elementary school education to enhance students' learning experience about the Solar System. The study adheres to the ADDIE instructional design model, which involves five stages: Analysis, Design, Development, Implementation, and Evaluation. A specific Android-based AR application, named SolAR System, is developed, enabling students to interact with 3D objects and gain knowledge about the planets through an immersive approach. The study incorporates a needs analysis and performance analysis, employing observation methods to gather relevant data. Evaluations are conducted to assess the application's validity, practicality, and effectiveness, employing measures such as pre-tests and post-tests. An experimental study involving 30 primary school students is conducted, demonstrating a notable improvement in their understanding of the Solar System, thereby indicating the effectiveness of AR as an educational tool. However, challenges pertaining to device accessibility and school policies concerning smartphone usage are identified during the implementation phase. In conclusion, this research affirms that AR can significantly enhance students' learning experience regarding the Solar System and recommends future development of AR applications for other mobile operating systems, such as IOS. © 2023 IEEE.

关键词： Augmented reality

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Analysis of Machine Learning Algorithms in Predicting Chronic Kidney Diseases Based on Performance: A Systematic Literature Review 4

Analysis of Machine Learning Algorithms in Predicting Chroni...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Alpaullivarez, Angelique Aurielle Aurelia, Miracle Monica, Sheila Suri, Puti Andam School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Chronic Kidney Disease (CKD) has been increasing nowadays from 750 million cases in 2019 to 850 million in 2023. Lack of access and late detection are one of the reasons why CKD cases have been increasing for all these years. Various Machine Learning (ML) algorithms have been studied to detect CKD and this paper will discuss and compare different types of algorithms to detect CKD early before progressing to advanced stages based on its performance. Although the use of ML to predict chronic kidney disease in the medical world is still hard to find, in other literature reviews with similar topic, it is found that various ML algorithms have been developed with the goal of assisting medical professionals in predicting CKD. Highly accurate ML algorithms have also been discovered using Artificial Neural Network, Random Forest, and XGBoost, despite needing further improvement in quality to be able to be applied on a large scale. In this paper, 5 commonly used ML algorithms namely XGBoost, Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and logistic regression were analyzed. Using Systematic Literature Review as this paper's methodology, this paper provides detailed explanation on how the algorithms work, implemented, and their performance were also analyzed by using 5 parameters: AUC, accuracy, precision, recall or sensitivity, and F1-Score. It was found that XGBoost achieved the highest score among all parameters except for AUC, which highest score was possessed by Artificial Neural Network. © 2023 IEEE.

关键词： Forecasting

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Detection of Cancer Cells in Tissue Histopathology Images Using a Convolutional Neural Network 4

Detection of Cancer Cells in Tissue Histopathology Images Us...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Abdullah, Hasna Salsabilla Ramananda, I Putu Denio Pranatha Adisaka, Nandatama Bagus Pangestu, William Suryadharma Priyanto, Winita Teukeku Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Cancer is a chronic disease with a high increase in the number of cases worldwide. In women, breast cancer is the most common cancer. One way to detect breast cancer is through histopathologic images of the tissue. Currently, histopathology detection is done manually, so it has shortcomings in the form of human limitations in classifying large numbers of images. In this research, a Convolutional Neural Network (CNN) model is developed to detect cancer cells in histopathology images automatically. The dataset used to train the model is 120,000 histopathology images labeled 1 (cancer) and 0 (normal) in the same ratio. Training the model is done with a batch size of 128 and epochs of 30. The confusion matrix and AUC-ROC method measure accuracy. From this study, the CNN model proved accurate in predicting tissues with cancer cells with a precision value of 94.76 percent, recall of 98.07 percent, and F-1 score of 96.39 percent. © 2023 IEEE.

关键词： Neural network models

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Enhancing Hate Speech Detection in Social Media Using IndoBERT Model: A Study of Sentiment Analysis during the 2024 Indonesia Presidential Election 5

Enhancing Hate Speech Detection in Social Media Using IndoBE...

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5th International Conference on computer and Applications, ICCA 2023

作者： Yulfa, Ramadhan Ihsani Setiawan, Benediktus Hengki Lourensius, Gerry Gilbert Purwandari, Kartika Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350303254

Legislative elections, presidential elections, and village head elections are all on the schedule for Indonesia in 2024. Law No. 7 of 2017 governs these elections, emphasizing the ideals of direct, public, free, confidential, honest, and fair elections. With the growth of social media, political personalities are employing these channels for campaign activity, resulting in a variety of user responses, including hate speech. Detecting hate speech is critical for preventing and mitigating the bad impact it may have on society. The purpose of this article is to investigate the usage of IndoBERT, a fine-tuned BERT model, to improve hate-speech identification during the 2024 Indonesia Presidential Election. The research focuses on hate speech in the context of the presidential election and the challenges raised by social media. In this paper, we utilized Twitter as a case study to develop effective hate-speech identification techniques. The method comprises fine-tuning IndoBERT using a dataset of Indonesian tweets on the election that have been preprocessed to reduce noise and classified as hate speech or non-hate speech using a pre-trained model. The accuracy, precision, recall, and F1-score of the model are used to assess its performance. The results reveal that the IndoBERT model detects hate speech in Indonesian Twitter data with high accuracy. A future study might look towards fixing typos in the dataset and examining expert recognition of hate speech scenarios. © 2023 IEEE.

关键词： Speech recognition

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The Impact of Playing Multiplayer Online Battle Arena Video Games on the Enhancement of Computational Thinking Skills Among Students in Indonesia 6

The Impact of Playing Multiplayer Online Battle Arena Video ...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Jumana, Athaillah Adhar Umboh, Josua Golden I. Ketut Dharma Wijaya, K. Gunawan, Alexander Agung Santoso Saputra, Karen Etania Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

This research examines the impact of playing Multiplayer Online Battle Arena (MOBA) video games on computational thinking skills among Indonesian college students. Using a quantitative approach, structured and valid questionnaires were administered to collect data and explore the potential benefits of video games in education. The findings indicate that playing MOBA games is associated with enhanced computational thinking skills. Additionally, Participants recognized the positive influence of playing MOBA games on computational thinking, highlighting improved strategic thinking, decision-making, and team coordination as main benefits. These results contribute to the existing research on gaming and cognitive skills, underscoring the potential of MOBA games for fostering computational thinking abilities. Educators and program developers can utilize these insights to enhance teaching and learning methods by integrating video games. © 2023 IEEE.

关键词： Students

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Exploring XGBoost as an Effective Machine Learning Algorithm for Stellar Spectral data Classification in Astronomy

Exploring XGBoost as an Effective Machine Learning Algorithm...

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

作者： Yoshino, Erio Juarto, Budi Kurniadi, Felix Indra Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339215

The discipline of astronomy has experienced a substantial metamorphosis due to the advent of contemporary telescopes, which facilitate the accumulation of extensive spectral information. The processing of data through traditional manual means has become unfeasible owing to its vast volume, thereby necessitating the adoption of automated classification. The advent of machine learning algorithms and artificial intelligence has enabled the efficient and accurate processing of spectral data, thereby facilitating a more comprehensive analysis of celestial objects. Prior research has investigated the efficacy of diverse machine learning algorithms in the context of stellar classification endeavors, as well as the significance of pre-processing spectral data. The present investigation centers on the utilization of XGBoost, a machine learning algorithm that exhibits the ability to handle heterogeneous data types and address imbalanced datasets, thus rendering it a viable option for classification endeavors. In this research, we found that Random Forest and XGBoost gave good result compared to other classifier. In contrast, reduction dimension using PCA are lowering the achieved result. © 2023 IEEE.

关键词： Machine learning

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Accuracy of Comparison Random Forest, Gradient Boosting Tree, Decision Tree, and Naïve Bayes Algorithms in Predicting the Size of Companies Where data Scientist Works 5

Accuracy of Comparison Random Forest, Gradient Boosting Tree...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Putra, Dias Perdana Wilson Alexander, Ang Rizal, Sahrian Putra Amin Rais, Muhamad Nathaniel, Dominique Christopher Iskandar Pohan, Husni Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Therefore, this research discusses the comparison of accuracy between machine learning algorithms such as Naïve Bayes, Decision Tree, Random Forest, and Gradient Boosting Tree for data scientist salary data in predicting the size of the company where they work. This research method involves data from the Kaggle platform. The data includes attributes such as salary paid in the year, salary currency, employee type, company location, job title, salary in USD, experience level in the year, salary, employee residence, company size, and remote ratio. The results of this study show that the three algorithms which is Random Forest, Decision Tree, and Gradient Boosting Tree, have a high level of accuracy compared to Naïve Bayes in predicting the size of the company where data scientists work. However, the performance of Random Forest is less optimal due to unbalanced data which results in the results of Random Forest being less accurate than Decision Tree. A high level of accuracy can affect the classification results so that the better the accuracy level, the better the classification results. Meanwhile, Gradient Boosting Tree obtained excellent accuracy results from the three machine learning algorithms. The conclusion of this research is that Gradient Boosting Tree and Decision Tree can be a good choice to predict the size of the company where data scientists work. However, it is important to ensure that the data is balanced so that the Random Forest algorithm can run more optimally. © 2023 IEEE.

关键词： Decision trees

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