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Brain Tumor Detection and Localization from MRI Images Using Deep Learning Methods 8

Brain Tumor Detection and Localization from MRI Images Using...

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

作者： Prasetyo, Simeon Yuda Santika, Diaz D. School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Brain tumors are a significant health concern due to their potential to develop into cancer. With increasing cases and a high mortality rate, there is a critical need for more effective tools to aid in tumor identification. Deep learning-based methods have shown promise in detecting tumors and accurately localizing their positions and boundaries within the brain. This paper presents a unique computational experiment that simultaneously addresses both tasks using deep learning techniques. By leveraging the power of deep learning, this research aims to contribute to the development of advanced tools for improved brain tumor identification and diagnosis. The first task focuses on detecting the presence of tumors by examining various pretrained deep convolutional neural networks (CNNs) on publicly available MRI datasets. Through fine-tuning, the InceptionResNetV2 and VGG16 models achieve an impressive 99% detection accuracy on binary MRI images. The second task involves obtaining the precise position and boundaries of the tumor using three different CNN-based semantic image segmentation techniques: FCN, UNet, and Res-UNet. The computational experiment reveals that Res-UNet outperforms the other methods, yielding a high dice coefficient of 0.903 and intersection of union (IOU) of 0.825. Overall, this research showcases the potential of deep learning in advancing brain tumor identification and diagnosis. © 2023 IEEE.

关键词： Semantic Segmentation

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The Effect of Educational Platformer Game "Loving Ma" 8

The Effect of Educational Platformer Game "Loving Ma"

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

作者： Tamba, Nikolaus Hasudungan Andrian, Billy Vincenzo Yanfi, Yanfi nusantara, Pualam Dipa Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study aimed to create an educational game application based on the theme of motherly roles in the platformer genre. The game application is designed to feature three levels consisting of multiple-choice questions about the roles of a mother. The Extreme programming (XP) method is utilized to create the game application. Extreme programming (XP) is a branch of the Agile Development method used to develop systems with uncertain or rapid requirements changes. The steps of the XP method consist of planning, designing, coding, and testing. The game application is evaluated both internally and outside. Black-box testing and eight golden rules are used for internal review. The findings of black-box testing can be used to determine the viability of a game application. The evaluation findings of the eight golden rules are required to evaluate the game application's user interface. The Game Experience Questionnaire is used for external review (GEQ). The GEQ was distributed through Google Forms, and 67 respondents completed it. The result of the GEQ was then tested for validity and reliability, calculated to find the mean number and standard deviation, and analyzed. Therefore, it was concluded that the game application provides a good user experience. © 2023 The Authors. Published by Elsevier B.V.

关键词： Eight Golden Rules Extreme programming game application Game Experience Questionnaire platformer

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SRL Apps: The Support of ICT Adoption for Entrepreneurial Intention

SRL Apps: The Support of ICT Adoption for Entrepreneurial In...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Indriana Susanti, Leni Riccosan Utomo, Satrio Matin Alamsyah, Doni Purnama Bina Nusantara University Binus Business School Management Department Jakarta Indonesia Bina Nusantara University Binus Business School Entrepreneurship Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350392036

Startup Readiness Level (SRL) apps were used to measure students' level of business readiness. Student adaptation to SRL Apps needs to be evaluated to determine its impact on students' entrepreneurial intention. The study was carried out by measuring factors that support student ICT adoption, namely perceived ease of use and level of application innovation. A quantitative survey was used as the research method. Questionnaires were distributed to 542 students who used SRL Apps. Data from the questionnaire were processed and analyzed using SmartPLS, and the analysis of research results was presented in line with the design of the research hypothesis. The test results from the research model presented findings that there was a positive impact from perceived ease of use and innovation, which was assessed by students on the ease of ICT adoption. The research results explain that entrepreneurial intention can be directly increased through ICT adoption of SRL Apps. The findings explain the importance of ICT adoption in business applications to support students' interest in running a business. Clear measurements on SRL Apps are needed to evaluate student business achievements and to support student interest in continuing to run a business. © 2024 IEEE.

关键词： Students

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Enhancing Movie Recommendations: A Hybrid Filtering Approach Combining Collaborative and Content-Based Filtering

Enhancing Movie Recommendations: A Hybrid Filtering Approach...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Vemberly, Esther Ancilla, Eugenia Anderies, Anderies Chowanda, Andry Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327472

The exponential growth of information and the widespread use of the internet has increased the importance of recommender algorithms, particularly in movie recommendation systems across various industries. By helping users select movies that match their interests, these systems significantly improve the movie-watching experience. However, a common challenge faced by these systems is the cold-start problem, which makes it difficult to provide accurate recommendations for new users with limited historical data. To address this issue and improve recommendation accuracy, this research paper presents a hybrid approach combining collaborative filtering (CF) and content-based filtering (CBF) to provide movie recommendations, where the model makes use of user ratings and movie datasets. The CF component identifies similar users and suggests popular movies, while the CBF component examines movie attributes such as titles, genres, ratings, and descriptions to recommend movies. The methodology involves data acquisition, cleaning, partitioning, and the design of collaborative, content-based, and hybrid models., where the dataset was partitioned into 60% for training and 40% for testing. The evaluation is based on the MovieLens 100K Dataset, using Root Mean Squared Error (RMSE) and Fraction of Concordant Pairs (FCP) metrics. Eventually, the result of the hybrid model obtains a training and testing RMSE of 0.881858 and 0.999145, respectively. For the FCP value, the model achieves a training value of 0.751256 and a testing value of 0.641928. Overall, the proposed hybrid approach highlights the importance of using CF and CBF approaches in recommender algorithms as it gives a balance between accuracy and diversity of the datasets and shows potential in addressing the cold-start problem and providing personalized recommendations. Future studies can focus on improving the hybrid model and exploring other evaluation metrics for a more thorough review. © 2023 IEEE.

关键词： Collaborative filtering

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Using Machine Learning for the Prediction of Diabetes with Emphasis on Blood Content 8

Using Machine Learning for the Prediction of Diabetes with E...

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

作者： Nurdin, Averina Tane, Matthew Maximillian Tumewu, Raphael William Theodore Suryaningrum, Kristen Margi Saputri, Hanis Amalia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Diabetes is a condition associated with high levels of blood glucose and insulin deficiency that is becoming more prevalent among children and adolescents. It is the ninth leading cause of mortality globally at over 1 million deaths per year. Misdiagnosis of type 1 diabetes with type 2 are frequent and causes diabetic ketoacidosis, a critical complication. This paper seeks for the most accurate model between frequently used machine learning algorithms - Multilayer Perceptron, Support Vector Machine and Random Forest - in predicting diabetes among patients with focus on blood content analysis, specifically using the Laboratory of Medical City Hospital (LMCH) Diabetes Dataset, which was retrieved from Mendeley. Using hyperparameter optimization and ANOVA F-Value feature scaling, results show that Random Forest produced the highest accuracy at 1.00, followed by Multilayer Perceptron at 0.987 and Support Vector Machine at 0.96. © 2023 The Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence Deep Learning Diabetes Machine Learning Multi-layer Perceptron Random Forest Support Vector Machine

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Preserving Indonesian Culture in the Digital Age: Implementing Augmented Reality to Address Cultural Appropriation Issue 8

Preserving Indonesian Culture in the Digital Age: Implementi...

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

作者： Arista, Aldo W.P. Hendra, Daniel S. Juwono, Felix P. Abdillah, H. Permana, Frihandhika Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Cultural appropriation is a complex issue that can lead to the loss of a nation's cultural identity. With the widespread adoption of mobile phones in Indonesia, there is an opportunity to use technology, specifically augmented reality (AR). To address this issue, our user-friendly AR app allows users to interact with Indonesian culture in a virtual environment. Through AR technology, users can engage with Indonesian culture in an immersive and fun way. Our target market is the younger generation, and our app provides a user-friendly interface that allows users to scan AR markers to access different cultural settings. Users can choose from two cultural regions, Bali and Papua. Once a region is selected, a scene showcases the desired region's iconic places and objects accompanied by a traditional folk song. In developing the AR app, we used the agile software development method for approximately two months with four developers. At the end of the development phase, we conduct four testing aspects of the application-namely, angle testing, functionally testing, usability testing, and hardware testing. The testing revealed that the application could perform well under various testing conditions. Through our AR solution, we aim to raise awareness of the importance of Indonesian culture and provide an educational experience that can inspire users to take ownership of their cultural heritage. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality (AR) Cultural appropriation identity markers traditional

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Design and Development a Virtual Planetarium Learning Media Using Augmented Reality 8

Design and Development a Virtual Planetarium Learning Media ...

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

作者： Latif, Jazlyn Jan Keyla Triputra, Augustinus Adrian Kesuma, Michael Awarsa Maulana, Fairuz Iqbal Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The solar system has always been a mystery to many. If not for the advanced technologies, most humans would not have the opportunity to gain knowledge about the planets. Although Earth is a part of the solar system, the solar system is simply too dangerous and expensive for humans to explore casually. Humans do not interact with the Sun or planets actively. This is especially concerning for children who often require visual aids in studying. An Augmented Reality (AR) based application can solve that problem. Through Virtual Planetarium, children may interact with the Sun or the planets and gain information. This will help aid children's guardians in studying the solar system. The application is made by Systems Development Life Cycle (SDLC) method. Through the making of this application, it is expected that children will have a better understanding of the solar system. © 2023 The Authors. Published by Elsevier B.V.

关键词： Application Augmented Reality Planetarium Virtual Virtual Planetarium

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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

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

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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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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Forecasting Rice Production in Indonesia using Regression Techniques: A Comparative Analysis of Support Vector Machine, Linear Regression, and XGBoost Regression 10

Forecasting Rice Production in Indonesia using Regression Te...

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10th International Conference on ICT for Smart Society, ICISS 2023

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

ISBN: (纸本)9798350339543

Population growth is straining the agricultural sector, posing a significant threat to global food security. In Indonesia, rice is a staple diet, with annual demand growth of 1.16 percent outpacing production growth of 0.81 percent. Given Indonesia's projected population growth, meeting this demand is crucial. Indonesia is the fourth most populous nation in the world. In order to evaluate government policies on food sustainability, such as rice imports and agricultural programs, it is necessary to forecast rice production levels. Using Support Vector Machine, Linear Regression, and XGBoost Regression, the objective of this study was to forecast the paddy production in Indonesia. The study employed a dataset spanning the years 2018 to 2022, and the efficacy of each model was evaluated using standard metrics including MAPE, MAE, RMSE, and R2. With an RMSE of 0.015, an R2 of 0.997, and an MAE of 0.008, XGBoost Regression outperformed all other models across all metrics. Support Vector Regression had the highest MAPE, the lowest RMSE, the highest R2, and the lowest MAE, whereas Linear Regression had the highest MAPE, the lowest RMSE, the highest R2, and the lowest MAE. © 2023 IEEE.

关键词： Linear regression

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