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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study 13

Pain Classification Using Statistical Feature Extraction Usi...

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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Sudimanto Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia

ISBN: (纸本)9798350323184

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people's psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%. © 2023 IEEE.

关键词： Extraction

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Design and Development Immersive Learning Media Using Augmented Reality Technology to Preserve Barong Art 11

Design and Development Immersive Learning Media Using Augmen...

引用

11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Puspitasari, Chasandra Hamim, Miftahul Zaini, Ahmad Arianto, Fairuz Firnanda Rizky, Raihan Daffa School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Entrepreneurship Business Creation Binus Business School Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Augmented Reality (AR) is a technological advancement that employs interactive digital images to render two-dimensional objects more tangible. This technology has the capability to be integrated with a wide range of digital information in order to enhance its appeal. This study describes the creation of an augmented reality (AR) application that utilises markers to promote and preserve Indonesian culture, with a particular focus on Barong Art. Utilising the ADDIE framework-Analysis, Design, Development, Implementation, and Evaluation-this application is constructed. The application effectively presents these cultures in an interactive and captivating fashion, showcasing proficient object detection at distances of 30 cm, 40 cm, and 50 cm in 0.1 seconds, with a particular emphasis on angles of 30° and 40°. In the future, animations and 3D models will be incorporated to further enhance the gaming experience. Although the present emphasis is on children, forthcoming iterations strive to accommodate a wider spectrum of ages. This research highlights the potential of augmented reality (AR) technology in safeguarding cultural heritage, specifically in the current digital era where smartphone games are surpassing traditional games. It also emphasises the need for more advanced augmented reality (AR) applications that are entertaining and educational. © 2024 IEEE.

关键词： Contrastive Learning

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Comparing Artificial Neural Network and Decision Tree Algorithm to Predict Tides at Tanjung Priok Port 8

Comparing Artificial Neural Network and Decision Tree Algori...

引用

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

作者： Musadi, Arif Tertius, Christian Carlos Steven, Jonas Saputri, Hanis Amalia Suryaningrum, Kristien Margi Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Tanjung Priok Port is an international port in Indonesia located in Tanjung Priok, North Jakarta. For all activities carried out at Tanjung Priok Port to run smoothly, this research was made which aims to predict the height of tides using the Artificial Neural Network (ANN) and Decision Tree methods with a quantitative approach. Artificial Neural Network (ANN) is a technique inspired by the way the biological nervous system works, namely in brain cells in processing information received by humans. while Decision Tree is also known as a decision tree which is an algorithm for building a decision hierarchy structure. The process of making a Decision Tree starts from the Root Node to the Leaf Node which is done recursively. This research was conducted to predict the height of tides in January 2018 - June 2018. By using both methods that have been computed, the ANN method produces a smaller MSE value than the Decision Tree method. The ANN method produces an MSE value of 0.003727983. While the Decision Tree method produces an MSE value of 0.009870259. If the dataset used has larger amount of data and the architecture of each algorithm is more complex, then the calculation results obtained will be more accurate. © 2023 The Authors. Published by Elsevier B.V.

关键词： Artificial Neural networks Decision tree Tidal prediction

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Improving SMS Spam Detection Through Machine Learning: An Investigation of Feature Extraction and Model Selection Techniques 8

Improving SMS Spam Detection Through Machine Learning: An In...

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

作者： Siagian, William Setiadi, Melisa Rachel Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

When it comes to SMS, the topic of spam is a big obstacle that requires urgent attention. The majority of SMS messages that fall under the category of spam are commercial communications, which are obviously illegal and unethical, followed by phishing scams and messages that may disturb the user's peace of mind. Users may find this to be extremely inconvenient as it consumes energy to erase those messages, slows down websites, and may even infect computers with viruses. We must therefore distinguish between SMS messages that are spam and those that are not in order to prevent this. We are recognizing this in this project utilizing machine learning algorithms. In this paper, we will talk about the algorithms we employ, compare them all to the dataset we use, and finally select the algorithm that will be most effective at determining which SMS messages fall under the spam category. Based on comparisons of precision and accuracy, we choose the optimum algorithm, then for the results of the research in our paper, we found that the best non-pretrained machine learning model was GRU with the best feature extractor being GloVe, with an accuracy of 91.9171% and precision of 91.9187%. However, the pretrained machine learning model BERT still outperformed the non-pretrained model with an accuracy of 99.0166% and precision of 99.0179%. © 2023 IEEE.

关键词： Feature extraction

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Markerless Augmented Reality Application for Indonesian Traditional House Education 8

Markerless Augmented Reality Application for Indonesian Trad...

引用

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

作者： Boediono, Jonathan Apriliano Saputra Aulia, Muhammad Rizqi Maulana, Fairuz Iqbal Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This paper proposes the development of a markerless augmented reality (AR) application for Indonesian traditional house education. The application aims to provide an immersive and interactive way of visualizing traditional houses from various regions of Indonesia, with the potential to raise public awareness about the importance of preserving traditional culture. The prototyping method was employed as the most appropriate approach to software development, and the ARFoundation platform was used to integrate plane detection into the AR application. The results showed that 80% of tested devices were compatible with ARCore, and the AR plane detection could detect up to 250cm from the device position. In addition, a feedback survey conducted on 17 testers showed that the application design and mechanism brought a good experience to users, with 53% stating that the app is easy to use. This research project contributes to the development of technology-based solutions to promote Indonesian culture and preserve traditional houses, especially for younger generations who may be less familiar with traditional culture. © 2023 The Authors. Published by Elsevier B.V.

关键词： Application ARCore Augmented Reality Rumah Adat Traditional House

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Designing Women's Safety Application for Emergency Situations 5

Designing Women's Safety Application for Emergency Situation...

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

作者： Artamadja, Danzel Faza, Tara Hanifan Irena, Florencia Maulana, Muhammad Ilham Tobing, Gerardo Lumban Widianto, Mochammad Haldi Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Women's safety is a serious issue in Indonesia, where women endure harassment and violence in public spaces. This paper proposes the design of a mobile application for women's safety in emergency situations. The application aims to prevent violence such as kidnapping and sexual assault by providing location sharing, communication feature, and loud SOS (Save Our Souls) alarm. The authors used quantitative for this research method. The novelties of this research are application can send emergency message and location by SMS in real time. The location sent is in the form of a link that is integrated with Google Maps. Furthermore, the alarm with max volume won't stop until user clicks stop button. The authors conducted an initial survey to gather requirements for the application design and decide which features to include. The design is specifically made for emergency situations, including button placement and features that only need one tap to activate. The authors implemented the design and concluded that the features can assist women in emergency situations. The survey results shows that safety applications can make women feel safer and validate the importance of the application. © 2023 IEEE.

关键词： Location

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Deep Learning Architectures for Facial Emotion Analysis 23

Deep Learning Architectures for Facial Emotion Analysis

引用

8th International Conference on Sustainable Information Engineering and Technology, SIET 2023

作者： Chandra, Bryan Kindy, Grace Oktaviani Gunawan, Kelvin Susanto Satria, Gavin Putra Edbert, Ivan Sebastian Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798400708503

Mental illnesses, which affect 1 in 4 people globally, can harm a person's life if they are not detected and treated properly. To visually assess a person's mental condition, facial emotion recognition plays a huge role. One way to help automate this process is to use a trained model that can detect a person's emotion using their face as an input. With the advances of technologies capable of detecting a person's emotion, various models have been developed. To find out which model works better to detect a person's emotion, a study to compare EfficientNet-B2 and VGG19 is done. Both models are trained and evaluated against the FER+ dataset with 30 epochs. On the test split of the dataset, VGG19 achieved an accuracy of 90.58%. EfficientNet-B2 on the other hand achieved an accuracy of 78.22% on the test split of the dataset. Both models achieved low accuracy and F1 scores on disgust, fear, and contempt emotions. © 2023 ACM.

关键词： Emotion Recognition

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Implementation of Handwriting Recognition and Answer Evaluation with Recurrent Neural Network 8

Implementation of Handwriting Recognition and Answer Evaluat...

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

作者： Pangestu, John Suryajaya Kho, Kelvin Suryani, Dewi Andika, Leo Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Many teachers still utilize exams to gauge their pupils' knowledge. However, as the teacher must comprehend each answer provided by each student individually, analyzing the exam response can take a long time. Additionally, different handwriting styles may lead to errors in scoring the response, resulting in an erroneous mark. This study suggests a web-based program that can read students' handwriting to assist teachers in assessing students' responses. The objective of this study was to develop a web-based application that can accept student responses as input and output correctness. © 2023 The Authors. Published by Elsevier B.V.

关键词： Exam Handwriting Recognition Recurrent Neural Network

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Online Learning Platform Analysis During COVID- 19 Pandemic in Indonesia 8

Online Learning Platform Analysis During COVID- 19 Pandemic ...

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

作者： Lim, Christoper Martin Adnyana, Made Adi Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Covid-19 forces Indonesia's education institutions to operate learning online. The continuity of online learning is influenced by several factors, such as personal space, environment, and learning system. Out of all categories, Learning Management System (LMS) has the most impact on online learning. LMS has a role in facilitating several activities, starting from scheduling classes to assignment collection. There are several choices when choosing LMS that are publicly available. The authors chose three LMSs that are the most popular: Google Classroom, Microsoft Teams, and Edmodo. This research aims to know the factors that affected the online learning process in Indonesia during the COVID-19 pandemic and what influenced the selection of LMS. This research uses a Systematic Literature Review and PRISMA to identify factors that affect the online learning process and learning management system selection. From this research, the authors have found that internet connection is the most discussed factor related to things that affect the online learning process. Next are Learners' and Instructors' Engagement and LMS. The authors have also found that easy use, class management, assignment, file sharing, and communication features are the main factors in LMS selection. From this result, the authors hope that this research can increase the quality of online learning in Indonesia. This research can be further developed by discussing more LMS that covers several scopes. © 2023 The Authors. Published by Elsevier B.V.

关键词： COVID-19 Edmodo Google Classroom Indonesia Learning Management System Microsoft Teams Online Learning

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Building 3D Object Model for Tourism in Monas using CityEngine 8

Building 3D Object Model for Tourism in Monas using CityEngi...

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

作者： Revikasha, Muhammad Farrel Adiputra, Thoriq Wima Hudaya, Muhammad Ilham Ngarianto, Heri Irwansyah, Edy Fitrianah, Devi Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

A prominent monument in DKI Jakarta is the Monument Nasional (abbreviated as Monas). This moniker serves as both a symbol for the city of Jakarta and a representation of the people of Jakarta and Indonesia. Apart from being a monument, Monas also has a history of the Indonesian nation. In this study, 3D models are designed to display visualizations of Monas and surroundings in 3D form with the aim of helping tourists who want to visit Monas in preparing and increasing their understanding of the tourism in Monas. This study was conducted with the help of CityEngine software that created the 3D models of Monas implemented and displayed on a website with a web scene format. Using image-based 3D modelling, the main geomatics techniques is photogrammetry, it allows for new methods to create virtual 3D. To do this, various components including route visualizations must collaborate. To do this work we have a framework that leverages the technology and data sources already available to reduce the time and work required for prospective 3D applications. With the existence of a Monas 3D visualization that can be accessed through web browser, it expected that it will make tourists not complicated to obtain various tourism information and increase the tourism in Jakarta. © 2023 The Authors. Published by Elsevier B.V.

关键词： 3D Model 3D Visualization CityEngine National Monument Tourism

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