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Towards the Fun Therapy on People with Mental Disorder – Analysis on Serious Game for Mental Therapy 6

Towards the Fun Therapy on People with Mental Disorder – An...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Pratama, Galih Dea Soeparno, Haryono Arifin, Yulyani Gaol, Ford Lumban Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Mental health is one of the states of well-being which enables people to cope with various things in everyday life. Despite that, mental health is often overlooked, driving the emergence of numerous mental disorders on people. Along the presence of technological advancements, there is serious game which is utilized to entertain and achieve non-game goals related to real-life issues. Due to that, serious game can be leveraged as a media put into health sectors, particularly in presenting fun therapy to people diagnosed with mental disorders. This research presents systematic literature review on publications discussing the utilization of serious game in providing fun therapy for people with mental disorders, focusing on matters like the mental disorders emphasized within, the game elements presented in each publication, and ways to evaluate the serious game. The result indicates overall well-received responses regarding the usage of serious game in serving the purpose, albeit with different ways on evaluation process based on the mental disorders each publication addressed for. © 2024 IEEE.

关键词： Serious games

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Digital Nudge Evaluation on COVID-19 tracing Application 9

Digital Nudge Evaluation on COVID-19 tracing Application

引用

9th International Conference on Information and Communication Technology, ICoICT 2021

作者： Sukmaningsih, Dyah Wahyu Abdurachman, Edi Purwandari, Betty Trisetyarso, Agung Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781665404471

Nudge is considered as an intervention to change user behavior and influence decision-making. Mobile apps have become a part of our everyday life. In this pandemic era, governments use mobile apps' technology to control the spread of COVID-19 infection. Many governments implemented COVID-19 tracing apps, and citizens are forced to use the apps to record their contact tracing and alert them if they have contact with an infected person. The key to control the increasing spread of the virus also depends on citizens adhere to health protocol. Government has experience difficulty in enforcing the protocol without strict surveillance. Therefore, building awareness of risk is essential-this study attempt to design nudge intervention into COVID-19 tracing apps. From the evaluation of the original apps, four nudge interventions can be applied in the application. © 2021 IEEE.

关键词： COVID-19

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The Role of Industry 4.0 Technology in Enhancing Small Medium-Sized Enterprises Stability and Growth 15

The Role of Industry 4.0 Technology in Enhancing Small Mediu...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Aprilinda, Yuthsi Warnars, Harco Leslie Hendric Spits Meyliana, Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350383829

The purpose of this study is to find out and analyze what has been done by previous studies in knowing the problems faced by SMEs today and how the influence of Industry 4.0 technology in dealing with problems in Small and Medium Enterprises (SMEs), so that they become developed and can maintain their business continuity. Most studies show that SMEs have considerable barriers when trying to use the potential of Industry 4.0 to improve the competitiveness and long-term sustain ability of their business. This systematic literature review uses the Kitchenham technique to identify SME problems and the role of Industry 4.0 technologies in SMEs in various industrialized and developing countries. Through several databases including: IEEE, science Direct, Web of science, Taylor & Francis, Emerald and Springer between 2019 - 2023 were used to find relevant research articles in this study. The research findings offer valuable perspectives on the strategies, obstacles and prospects that SMEs in different countries face in adjusting to Industry 4.0. The contribution of academics in this research is to provide solutions by designing more robust approaches to promote the business sustainability of SMEs and their role in driving economic progress in their countries. © 2023 IEEE.

关键词： Sustainable development

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Explainable Supervised Method for Genetics Ancestry Estimation 1

Explainable Supervised Method for Genetics Ancestry Estimati...

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

作者： Budiarto, Arif Pardamean, Bens Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781728133331

Ancestry estimation is one crucial stage in genomic research. It generates scores that represent the admixed genetics profile as the result of human evolution. In the previous research, we implemented multiple unsupervised methods to estimate these scores from large genomics data obtained from the 1000 Genome Project. These methods were limited to only cluster the samples to the five global populations in the dataset. The main challenge arose when implementing these methods to cluster the samples into more specific subpopulations. In this paper, we proposed a supervised approach to answer this challenge. Two state-of-the-art supervised machine learning methods, XGBoost and Deep Neural Network (DNN), were applied to the same dataset. These methods were aimed to classify samples, both into five main populations and also into 26 sub-populations. In the first classification task, both methods achieved similar results to our previous unsupervised approach. Interestingly, for the second classification task, which posses a relatively higher difficulty, DNN yielded better performance in the train, validation, and test dataset, despite its overfitting problem. Furthermore, the feature importance scores from each model were calculated using Shapley Additive Explanations (SHAP) method. Finally, 11 overlapped SNPs from all models were evaluated based on the reported Minor Allele Frequency (MAF) from the 1000 Genome Project. Overall, only using these 11 SNPs, we could differentiate each population in regards to its average MAF. © 2021 IEEE.

关键词： Genome

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

引用

6th International Conference on Information Management and Technology, ICIMTech 2021

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

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： computer vision

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Impact of Intention to Use to a Document Management System (Case Study Approach) 3

Impact of Intention to Use to a Document Management System (...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Hartono, Sugiarto Kosala, Raymondus Raymond Supangkat, Suhono Harso Ranti, Benny Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781538658215

Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve the objectives conducted by identification problem on the research model developed from the model of Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology to the use of document management system. Furthermore, the data obtained from the respondents tested outer model, inner model test, hypothesis test and to produce a comprehensive analysis will be conducted Importance Performance Analysis. This research will result in a comprehensive analysis and able to provide recommendations and solutions for improvement from the implementation DMS has been done earlier. © 2018 IEEE.

关键词： Information services

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Smart village design using enterprise architecture framework model 6

Smart village design using enterprise architecture framework...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Faisal Suharjito Bina Nusantara University BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

This paper describes the design of smart village that can be describe as an indicator for the smart village in Indonesia, where the Indonesian population is now more than 250 million peoples. with several islands as much as 17, 504 islands, consisting of 53.3% were in urban areas, the rest spread in rural areas, but it will turn around if there is no solution currently to address the disparity in the village in terms of infrastructure and technology. One of the concepts can be applied to overcome these problems is "Smart Village". Conducting a study of four villages on the island of Java in Indonesia, which was awarded the village of Information Technology, the study aims to research the indicators that make the village as the village of Information Technology. By using the TOGAF Enterprise Architecture analysis, the result of study are the components that exist in the ICT village winners and what the other components needs to complement the design of smart village which can be a reference in Indonesia in future. © 2021 IEEE.

关键词： Rural areas

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Virality Prediction on Twitter Using Combined CNN and BERT Models 4

Virality Prediction on Twitter Using Combined CNN and BERT M...

引用

4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Benedecas, Hilarius Ryan Auxilio Girsang, Abba Suganda Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

Nowadays, social media serves as a platform for sharing and receiving information. One indicator of the importance of information is its virality. Virality is an interesting topic in the world of Natural Language Processing (NLP), including the prediction of virality and identifying features that influence it. Research in this area has utilized Bidirectional Encoder Representations from Transformers (BERT) models and yielded promising results. Previous studies aimed to predict virality based on specific features. This research aims to predict virality by combining the abilities of Convolutional Neural Network (CNN) and BERT models. The BERT model transforms textual data into numerical and adds new features, while the CNN model excels in numerical classification. Data for this study is sourced from Twitter and in Indonesian language. The proposed model demonstrates improvements compared to the model from previous research. Specifically, it shows a 7.1% increase in precision, a 9.9% increase in recall, a 3.8% increase in accuracy, and an 8.1% increase in F1-Score compared to the previous model. These findings contribute to future research on similar topics, particularly in leveraging multiple models for prediction tasks. Overall, the study provides insights into effectively utilizing the strengths of various models and combining them in prediction tasks. © 2024 IEEE.

关键词： Prediction models

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Long short-term memory for hate speech and abusive language detection on Indonesian YouTube comment section 11

Long short-term memory for hate speech and abusive language ...

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2021 11th International Workshop on computer science and Engineering, WCSE 2021

作者： Salim, Calvin Erico Rudy Suhartono, Derwin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9789811817915

Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than English. This research examines hate speech detection problem in form of Bahasa Indonesia. Millions of comments and text posts are added to various social media and discussion platforms. Manual classification in all of the internet as hate speech and offensive language is a near impossible and time-consuming task. This research uses Long Short-Term Memory (LSTM) and Bidirectional Long Short Term Memory (Bi-LSTM) for the method of classifying hate speech and abusive language. The final accuracy is 88,44% by using 200 neurons with Bi-LSTM method. Most common challenges are different languages, out of vocabulary words, long range dependencies, and sarcasm. © 2021 11th International Workshop on computer science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Long short-term memory

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A Systematic Literature Review: Object Detection in Semi-Autonomous Car 6

A Systematic Literature Review: Object Detection in Semi-Aut...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Sunjaya, Eric Kurniadi Daniel, Christian Kurniawan, Afdhal Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Traffic accidents are a serious problem in developing countries. These accidents can be caused by poor infrastructure and the arrogance of drivers. In addition, semi-autonomous systems are automotive technologies that have been developed in recent years and are starting to be implemented in existing vehicles. This literature review paper will mainly discuss how object detection works in semi-autonomous systems, how semi-autonomous systems operate, and whether semi-autonomous cars can reduce the number of traffic accidents. Its main objective is to build driver confidence in semi-autonomous systems by assisting in driving and supporting the development of them. In the process of writing this paper, the method we use follows the guidelines stated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P). © 2023 IEEE.

关键词： neural network object detection semi-autonomous car semi-autonomous system

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