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Implementation of Augmented Reality in Android-based Application to Promote Indonesian Tourism 8

Implementation of Augmented Reality in Android-based Applica...

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

作者： Anderies Marvella, Maevy Hakim, Nissa Adila Seciawanto, Priskilla Adriani Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is known for its iconic tourism industry. However, there is a significant popularity difference between each destination. This imbalance has been a problem that the Indonesian Ministry of Tourism has been trying to solve through the 'Wonderful Indonesia" campaign. Unfortunately, some did not perceive it as a success. With the breakthrough of Augmented Reality (AR) that is seen as a great factor in enhancing user experience in the tourism sector, this research aims to design and develop an immersive android-based application that implements Augmented Reality and Audio Guidance to promote and create branding for Indonesian tourism spots. This research uses a methodology divided into 4 phases;Analysis Requirement, Research and Collect data, System Design and Development, and Testing. A survey was conducted in the Analysis Requirement phase to find people's knowledge and preference for tourism spots in Indonesia. Another survey was conducted in the Testing phase to test the application's usability and whether the aim has been achieved. The main findings indicate that most local tourist respondents stated that the application developed is an effective way for them to explore and educate themselves about Indonesian tourism sites. Therefore, this application might be a sustainable answer and a new approach compared to what the Indonesian government has done in the past years. © 2023 The Authors. Published by Elsevier B.V.

关键词： Android Augmented Reality Indonesian Tourism Usability Testing

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Time-Based Steganography Image with Dynamic Encryption Key Generation 8

Time-Based Steganography Image with Dynamic Encryption Key G...

引用

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

作者： Wijaya, Kevin Lansky, Bryan Dewi, Cecilia Ariani Rojali Nabiilah, Ghinaa Zain Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The rapid advancement of technology has necessitated stronger protection measures for data exchange. Encryption, coupled with robust keys, is an effective method to safeguard sensitive information. This study aims to improve the security of key levels by implementing a combination of steganography and encryption. The approach involves using two keys, one in the form of a normal string and the other based on the insertion time. These keys are combined to create a new, stronger encryption key. The secret messages are then encrypted using this new key and inserted using the Least Significant Bit method in steganography. This integrated approach aims to provide a higher level of protection for data during transmission and storage. © 2023 The Authors. Published by Elsevier B.V.

关键词： separated by semicolons Type your keywords here

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Sentiment analysis of the Indonesian community toward face-to-face learning during the Covid-19 pandemic 8

Sentiment analysis of the Indonesian community toward face-t...

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

作者： Gozal, Andrew Giovanni Pranoto, Hady Hasani, Muhammad Fikri Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research aims to analyze how the opinions of the Indonesian people in the learning system in the middle of the covid-19 pandemic. The research method is carried out by performing the K-NN method to determine the accuracy level of the data used. The research data method is taken through public comments on Twitter social media using scrapping techniques with appropriate key word s. The data will then be processed through training and testing and classified using the K-NN method. After the data is classified, the accuracy, F1-Score, Recall, and Precision level will be tested using Confusion Matrix. The result showed that KNN performed well, with above 70% of the F1-score for each class. According to the confusion matrix, accuracy also showed promising results with 82%. Future research may include oversampling the class with fewer numbers. K-Fold cross-validation can also be used to see the general performance of the model. The same method may be used to find sentiment towards a political policy that is taken;whether a policy gets a good or bad response, if the response is bad, you can see the causal factors that cause the negative sentiment. In this way, you can find out the will of the public. © 2023 The Authors. Published by Elsevier B.V.

关键词： face to face learning k-nn sentiment analysis

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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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Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced dataset 8

Improving Batik Pattern Classification using CNN with Advanc...

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

作者： Filia, Beatrice Josephine Lienardy, Filbert Fernandes Laksana, I. Kadek Perry Bagus Jordan, Jayasidhi Ariyo Siento, Joyceline Graciella Honova, Shilvia Meidhi Hasana, Silviya Permonangan, Ivan Halim Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In image classification task, imbalanced dataset is a problem that often occurs. Batik pattern data also suffers this problem, mainly because of the poor quality of available images and rarity of certain patterns. In this research, we employed a novel ad- vanced augmentation and oversampling techniques on the imbalanced dataset to address this issue. This approach enhanced the diversity of the images, encompassing variations in color, contrast, wrinkles, and warps that may be present in batik *** employed two CNN models, DenseNet169 and VGG-16, along with three different training methods for our study. Thesemethods included training without oversampling and advanced augmentation, training with oversampling, and training with both oversampling and advanced augmentation. The results showed that the best accuracy was achieved with DenseNet169 with our oversampled and augmented dataset, with an accuracy of 84.62%. Additionally, VGG-16 also performed well on saiddataset, achieving an accuracy of 82.56%.Our results suggested that by using our oversampling & advanced augmentation onthe dataset,the model performance improved compared to plain data and oversampled data. © 2023 The Authors. Published by Elsevier B.V.

关键词： advanced augmentation batik pattern classification convolutional neuralnetwork imbalanced dataset oversampling

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A Literature Review of Challenges and Solutions in Cloud Security 10

A Literature Review of Challenges and Solutions in Cloud Sec...

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Salim, Luis Harjono, Stephanus Gunawan, Ferdinand Moniaga, Jurike Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350322729

Cloud computing has emerged as a revolutionary paradigm that provides on-demand access to shared computing resources over the internet. However, it also poses significant security and privacy challenges, particularly in terms of data protection and incident management. This literature review explores the challenges and solutions to achieve a secure cloud computing ecosystem, such as natural disasters, system failures, hacking/malicious activities, and human errors, and discusses potential solutions, including network redundancy, intrusion detection, and disaster recovery. It also discusses common best practices, incident management, and disaster recovery models to ensure availability and data reliability. These practices ensure that the cloud ecosystem will be resilient to attacks and disasters from external and internal factors. As cloud technology continues to grow, we are likely to see improvement in cloud security practices and technologies like integration with machine learning and deep learning models, advanced patterns, or blockchain utilization. These improvements will help patch current known vulnerabilities and challenges in hope of a better and more secure cloud solution and ecosystem. © 2023 IEEE.

关键词： Cybersecurity

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Privacy and Security in the Use of Voice Assistant: An Evaluation of User Awareness and Preferences 8

Privacy and Security in the Use of Voice Assistant: An Evalu...

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

作者： Dhiya'mardhiyyah, Alya Latif, Jazlyn Jan Keyla Tho, Cuk School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Recently, voice assistants have become increasingly popular with millions of users relying on them to complete daily tasks and access information. While it may be convenient to use, some may question its security and are concerned about their privacy. While the users are attracted to the beneficial services provided by voice assistants, they are risking their personal information. This study aims to evaluate user awareness regarding the privacy and security aspects of voice assistants, and their preferences for future development of voice assistants. The literature review in this study obtained three vulnerabilities of privacy and security in the use of voice assistants and the countermeasures that can be taken. The user-centric countermeasures to mitigate the vulnerability of voice assistants were chosen for analysis by systematically evaluating 151 relevant journal and conference articles from 6 databases and 52 were chosen for analysis. To evaluate user awareness and preferences regarding the use of voice assistants, a questionnaire with a total of 37 participants who have experience in using voice assistants was done. The results showed that the participants were aware of the voice assistant's vulnerability and were concerned that their data might be misused. © 2023 IEEE.

关键词： privacy privacy and security security user awareness user preferences voice assistant

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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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DeepLyric: Predicting Music Emotions through LSTM-GRU Hybrid Models with Regularization Techniques 8

DeepLyric: Predicting Music Emotions through LSTM-GRU Hybrid...

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

作者： Angelica, Felicia Hydro, Romington Sulistiya, Zefanya Delvin Muliono, Yohan Prasetyo, Simeon Yuda Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Music is a powerful medium that has the potential to evoke strong emotional responses in humans. Researchers have investigated the relationship between music and emotions and the growing interest in developing precise techniques for recognizing emotions in music. Growing interest among researchers in developing more precise techniques for recognizing emotions in music has evoked numerous studies to be conducted. The researchers trained LSTM and GRU (Gated Recurrent Unit) hybrid model on a dataset of 1,160 songs tagged with sadness, compassion, and tension. They were able to attain a test accuracy of 72.51%, although overfitting was discovered because of insufficient training data, a choice of dropout, and learning rate. The research proves the potential of machine learning methods for musical emotion recognition and recommends the use of regularization techniques to alleviate overfitting. It may be possible to create software that can recognize and respond to emotional states in musical contexts because of the research, which contributes to the growing body of knowledge on the connection between music and machine learning. © 2023 The Authors. Published by Elsevier B.V.

关键词： GRU Hybrid Model LSTM Music Emotion Recognition Natural Language Processing Song Lyrics

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Evaluating the Effectiveness of LightGBM for Fetal Health Prediction

Evaluating the Effectiveness of LightGBM for Fetal Health Pr...

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Aristan, Claudio Januar Yang, Nicolas Biliam, Viery Octavianus Kurniadi, Felix Indra Riccosan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350305197

The health of the fetus is essential for assuring that babies are born healthy. A mother's fetus must be in excellent health. The categorization of fetal health is essential for assessing the well-being and development of unborn infants. Despite this, numerous findings continue to indicate a high neonatal mortality rate. During pregnancy and labor, the Cardiotocography (CTG) is a common method for monitoring fetal health. It measures fetal heart rate and uterine contractions, allowing healthcare professionals to evaluate the baby's health and identify potential problems early on. However, interpretation of CTG data remains subjective, resulting in potential misdiagnoses of fetal health. This study seeks to reduce infant mortality rates caused by delivery complications by employing machine learning technology to assess a baby's health based on Cardiotocography test results. In this study, we evaluated the performance of three machine learning algorithms - XGBoost, Random Forest, and LightGBM - in predicting fetal health based on Cardiotocography data. We implemented various scenarios involving preprocessing techniques, feature selection methods, and hyperparameter tuning to enhance the model's prediction accuracy. The scenarios are without preprocessing, feature selection using Information Gain, feature selection using K-Best, Feature selection using both feature selection algorithm, without hyperparamter tuning and hyperparamter tuning with Grid Search. The result showed the use of all techniques combined proved beneficial, resulting in accuracies of 96% for XGBoost, 95% for Random Forest, and 97% for LightGBM. © 2023 IEEE.

关键词： Adaptive boosting

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