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Comparative analysis of machine learning and survival analysis for breast cancer prediction 9th

Comparative analysis of machine learning and survival analys...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Emily, Madeline Meidioktaviana, Felicia Nabiilah, Ghinaa Zain Moniaga, Jurike V. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Breast cancer cases are an alarming problem for several countries and are among other female diseases which are in the top list of population ill-health. To overcome this obstacle, it is crucial to develop reliable predictive models with high accuracy to maximize impact on early detection of breast cancer. Since we want to contrast two different models in studying patient death from breast cancer, our goal is to evaluate these models for their accuracy. The Cox model outperforms the Survival Random Forest in terms of survival probability, with a C-index of 0.757 and an AUC of 0.8561 compared to the Survival Random Forest's C-index and AUC of 0.5837. Furthermore, the Cox model's superiority is proved by its feature selection score of 46.72174, whereas the Survival Random Forest has no similar metrics. Additionally, Random Forest shows superior performance with higher precision in identifying survival probabilities with perfect specificity (100%), precision (100%), and overall accuracy (96.8%), versus 91.3%, 71.4%, and 83.9% for Naive Bayes. Random Forest is a more reliable solution for accurately anticipating both positive and negative cases due of its adaptability and zero false positive rate. © 2024 The Authors.

关键词： Lung cancer

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Uncovering Fake Profiles Using Machine Learning Algorithms

Uncovering Fake Profiles Using Machine Learning Algorithms

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Amadea Hardjono, Ignatia Christabelle Tanumihardja, Raymond Willy Lucky, Henry Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368451

Fake user profiles has been one of the biggest security issues in all online social networks (OSN) platforms. Whether operated by bots or humans, their sole presence disrupts the online social environment by being either a direct or indirect form of threat to users and platforms alike. Thus, detection of fake profiles plays a crucial role to mitigate this sort of issue, and with the help of machine learning algorithms this can be done simply and automatically while still achieving reliable and high-quality outcomes. In this paper, the research that was done employs and introduces an Ensemble Learning of three algorithms, namely Support Vector Machine (SVM), Random Forest algorithm, and Logistic Regression - a combination that has been sparsely tested and trialed compared to their individual applications. The aim of this approach is to enhance the generalizability while maintaining robustness of the detection model. Synthetic Minority Oversampling Technique (SMOTE), a data preprocessing method was also applied before the training and testing to prevent risks from associated with imbalanced data. The model evaluation will be determined by obtaining the precision results of the conducted experiment, highlighting the proposed methodology's effectiveness in identifying fake accounts. The findings from the conducted study are expected to contribute to the improvement of automated methods, as well as to provide insights that may be beneficial in strengthening security and user trust across different OSN platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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Manor Mystery: The Evaluation of the Effectiveness of Serious Game Using Speech Recognition Control for Social Anxiety 11

Manor Mystery: The Evaluation of the Effectiveness of Seriou...

引用

11th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2024

作者： Akbar Pahlawan, Jouza Muhammad Ramdhan, Dimas School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389289

This study investigates the creation and efficacy of a Serious Game (SG) designed specifically for the treatment of Social Anxiety Disorder (SAD) using speech recognition technology. Previous research has not explored this approach, making it unique in its application. By integrating Cognitive Behavioral Therapy (CBT) concepts, particularly exposure therapy, into a gaming environment, this study aims to provide an engaging and innovative method of treatment. The game, developed as a point-and- click adventure, allows players to interact using speech commands, enhancing the realism and immersion of the therapeutic experience. Usability and effectiveness were evaluated using the Game Usability Questionnaire (GUQ) and the Igroup Presence Questionnaire (IPQ), with participants divided into control (mouse control) and experimental (speech control) groups. Results indicated no significant differences in usability scores between the two groups, with both achieving high scores, suggesting that the game is user-friendly regardless of the control method. However, the general presence scores revealed that the game did not fully provide the necessary feedback for a strong sense of presence, likely due to the testing environment. This study contributes to the development of accessible mental health interventions tailored to the challenges faced by individuals with social anxiety disorder. © 2024 IEEE.

关键词： Usability engineering

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Comparison of the Latest Version of Deep Learning YOLO Model in Automatically Detecting the Ripeness Level of Oil Palm Fruit in PTPN IV, North Sumatra, Indonesia

Comparison of the Latest Version of Deep Learning YOLO Model...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Purba, Samdo Jumar Ferdinandz Tandion, William Irwansyah, Edy School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350379839

This research explores the capability of the Deep Learning models YOLOv8 and YOLOv9 in detecting the ripeness level of oil palm fruit. The purpose of this research is to compare the accuracy results between YOLOv8 and YOLOv9 in detecting the ripeness level of oil palm fruit and to find out whether there is an improvement in the accuracy of YOLOv9 compared to the previous version, YOLOv8. This research uses datasets sourced from the oil palm plantation repository of PTPN IV Medan, North Sumatra, Indonesia. The dataset collected for this research consists of 180 images with a size of 640x640 and a camera resolution of 12 Mega Pixel. The dataset was uploaded into Roboflow and underwent pre-processing and augmentation. After obtaining the API from Roboflow and inputting it into Google Colab for YOLOv8 and YOLOv9 training, testing was carried out on the provided images. The results show that both YOLOv8 and YOLOv9 successfully detected ripe, rotten, and unripe oil palm fruit with an accuracy of 0.984 (98.4%) and 0.99 (99%). The results show that YOLOv9 has higher accuracy than YOLOv8 in detecting the ripeness level of oil palm fruit. The potential use of the latest version of the YOLO model in detecting the ripeness level of oil palm fruit in plantations can serve as a basis for developing more effective and efficient automatic detection systems in the future. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing small object detection: Development on car logo identification using the YOLO-NAS method for speed and accuracy improvements 9th

Enhancing small object detection: Development on car logo id...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Marvell, Yohanes Amadeo Gunawan, Alexander A.S. Iswanto, Irene Anindaputri Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Car logo detection could give a broader advantage outside of business and marketing by making it easier for security forces to search for vehicles by surveillance. There are challenges in detecting car logos by surveillance such as blurred images, various image angles, brightness and contrast, and of course the size of car logos itself which is very small. This requires a single shot object detection model which is compatible for detecting small objects as well as real-time applications. The baseline models DETR and YOLOv7, as well as the SOTA model YOLO-NAS, will be trained using a Roboflow car logo dataset which has been pre-processed and augmented. The performance of all models will be compared using mAP0.5, IoU loss, precision, recall, and accuracy as evaluation metrics. YOLONAS produced a 0.731 mAP0.5 value and a significantly higher recall value compared to the baseline models, caused by YOLO-NAS' backbone, pyramid-attention neck, quantization aware blocks, and pre-train strategies. © 2024 The Authors.

关键词： Convolutional neural networks

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Analysing the Impact of Rhythm Game in Enhancing Typing Performance 10

Analysing the Impact of Rhythm Game in Enhancing Typing Perf...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Lancaster, Lemuel Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The research was centered on adapting and assessing a new, unique game concept. The game onKeys presents a new approach and poses as an alternative method for improving typing skills. However, despite offering a promising avenue, the game still grapples with certain limitations. Utilizing the MDA Framework, the study seeks to integrate additional game components into onKeys and evaluate its effectiveness in improving typing skills. A paired T-test is used to assess the Pre-Test and Post-Test data. The System Usability Scale gathers subjective feedback about the game design's usability. This study found that the onKeys game has little impact on players' typing speed and accuracy, which may be attributed to a higher average typing speed, short period of playtime or a small sample size. However, upon reviewing the record and survey data, particularly the System Usability Score, it becomes evident that the game design still has potential. Further research can delve deeper into this game concept and design. © 2024 IEEE.

关键词： Game design

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Lung Cancer Classification Using Modified ResNet Architecture 8

Lung Cancer Classification Using Modified ResNet Architectur...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Tianto, Eric Chandaka Samudera, Axel Adeta, Franz Nadia, Nadia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368970

One of the most prevalent and deadly cancers in the world, lung cancer presents serious difficulties for both patients and medical professionals. Even with the quick progress in early diagnosis, differentiating benign from malignant tumors is still a difficult task for doctors. By modifying the architecture and improving hyperparameters, this study aims to increase the model's accuracy in detecting subtypes of lung cancer and distinguishing between benign, malignant, and normal lesions. A new method for classifying lung cancer is provided by modifying the ResNet 18 architecture. While utilizing the basic hierarchical features that ResNet learnt, the suggested model incorporates modifications unique to the complexity of lung cancer imaging data. To assess the performance of the suggested model, the outcomes of this study (Modified ResNet 18) are compared with ResNet 18 and ResNet 50. Modified ResNet 18 exhibits the highest accuracy of 97%, followed by ResNet 18 at 95% and ResNet 50 at 95%. © 2024 IEEE.

关键词： Lung cancer

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Web-Based Application for GUI programming Learning Through Reverse Engineering Method

Web-Based Application for GUI Programming Learning Through R...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Tunardi, Yovita Yulianto, Budi Layona, Rita Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331511470

GUI (Graphical User Interface) programming is the process of developing (coding) a graphical interface application. GUI-based application, which replaces TUI (Textual User Interface), allows users to interact with a program interactively. computer science students are required to master in creating GUI-based applications because of industry needs. Despite the importance of mastering GUI programming, students still have obstacles when learning it. Students who fail the GUI programming course will potentially experience difficulties in two things. First, it is difficult to finish the final project. Second, it is difficult to get a programmer job after graduation. This research aims to develop a web-based application for GUI programming learning using a reverse engineering method that can produce sourcecode based on GUI designed by users. The application development method used is the Waterfall Method with the stages of Requirement Collection, Analysis & Design, Development & Testing, Implementation, and Maintenance. The conclusion of this research is that the application helps users understand GUI programming learning through the reverse engineering method. © 2024 IEEE.

关键词： Students

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Training Critical Thinking and Collaboration for Young People with the Help of Video Games

Training Critical Thinking and Collaboration for Young Peopl...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Wongso, Jeremy Emmanuel Fajar, Muhamad School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350379839

This study explores the development and usability evaluation of a cooperative puzzle-genre video game aimed at enhancing critical thinking, problem-solving, and cooperative skills in young people. Despite the common perception of video games as detrimental, they can also be educational. Usability was assessed using the Keyboard and Mouse Game Usability Questionnaire (KM-GUQ), which evaluates Easiness, Learnability, Challenges, Efficiency, and Satisfaction. The Mann-Whitney U test results showed no statistically significant differences in usability perceptions between frequent and rare players of cooperative games, with both groups median scores ranging from 4.0 to 4.5, The results suggest that the interface is equally easy to control, understand, and adapt to, as well as similarly challenging and satisfying, for both groups. Overall, the findings suggest that the usability of the application interface is consistent across users with different levels of experience in playing cooperative video games. © 2024 IEEE.

关键词： Mammals

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Optimizing K-NN Algorithm for Breast Cancer Diagnosis: A Focus on Chebyshev and Minkowski Metrics 2

Optimizing K-NN Algorithm for Breast Cancer Diagnosis: A Foc...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Prasetyo, Simeon Yuda Wicaksono, Pandu Izdihar, Zahra Nabila Arisaputra, Panji School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389241

Breast cancer represents a significant global health concern, being the most common cancer in women and a leading cause of cancer-related deaths. Early detection is crucial for improving patient outcomes, underscoring the need for effective diagnostic tools. Machine learning techniques offer promising avenues for enhancing breast cancer detection, with the K-Nearest Neighbors (KNN) algorithm showing remarkable performance. This study investigates the efficacy of two distance metrics, Chebyshev and Minkowski, within the KNN algorithm for breast cancer diagnosis. Using a dataset sourced from Kaggle, comprising various tumor characteristics, the study evaluates KNN models with different numbers of neighbors (1-15) using accuracy, precision, recall, and F1 score metrics. The results demonstrate that the Minkowski distance consistently outperforms the Chebyshev distance across all neighbor settings, with an average accuracy of approximately 95.39%. The best-performing model is achieved using the Minkowski distance metric with 3 neighbors, exhibiting an average accuracy of approximately 96.49%. This research not only contributes to the advancement of machine learning applications in medical diagnostics but also underscores the pivotal role of algorithmic optimization in improving healthcare outcomes. © 2024 IEEE.

关键词： Lung cancer

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