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Analysis of ensemble majority voting approach for acute lymphoblastic leukemia detection using svm trained on white blood cell abnormalities in images 9th

Analysis of ensemble majority voting approach for acute lymp...

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

作者： Januardo, Bryan Putradinata, Harley Moniaga, Jurike V. Nabiilah, Ghinaa Zain Computer Science Department Binus Graduate Program Jl. Raya Kb. Jeruk No.27 RT.1/RW.9 Kemanggisan Kec. Palmerah Kota Jakarta Barat Daerah Khusus Ibukota Jakarta11530 Indonesia

Leukemia is a cancer that attacks and infects white blood cells which can hinder the capability for someone with leukemia to fight infections, which may cause severe complications or even death. While Acute Lymphoblastic Leukemia is a certain type of leukemia that is the most prevalent childhood cancer. Detecting this disease is a repetitive activity that can take a lot of time and resources, meanwhile Acute Lymphoblastic Leukemia has a fast growth rate. In this study, we will try to classify leukemia cancer using machine learning based on the images of white blood cells provided. This method could provide early diagnosis and reduce the burden on hematologist-oncologist by optimizing the resources that are available. This research will use the ensemble classifier concept by combining several SVM models like linear, polynomial, and RBF. That have been trained, then combined into one singular ensemble model. By combining these models, we hope to improve the classification performance by minimizing the drawbacks of using certain SVM kernels. The results of this classification obtained an accuracy performance of 70.01%. © 2024 The Authors.

关键词： Lung cancer

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging

A Systematic Literature Review of Deep Learning Application ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Philips Bens Pardamean Gregorius Natanael Elwirehardja Mahmud Isnan Computer Science Department BINUS Graduate Program - Master of Compuer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance.

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

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Procedia computer science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors

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Procedia computer science 2023年 216卷 284-292页

作者： Faisal Asadi Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia.

关键词： typhoid fever salmonella typhi endemic disease meteorological factors

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Digital Twin (DT) Smart City for Air Quality Management

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Procedia computer science 2023年 227卷 524-533页

作者： Dedy Ariansyah Mahmud Isnan Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale.

关键词： Digital Twin Smart City Mixed Reality Augmented Reality

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Hotel Information System Management Audit Using COBIT 2019 - APO12

Hotel Information System Management Audit Using COBIT 2019 -...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Joy Nashar Utamajaya Suhono Harso Supangat Ford Lumban Gaol Benny Ranti BINUS Graduate Program School of Information System BINUS University Jakarta Indonesia School of Electrical Engineering and Informatic Bandung Institute of Technology Bandung Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal.

关键词： Technological innovation Companies Risk management Information technology Interviews Information systems

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Topic Segmentation using Transformer Model for Indonesian Text

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Procedia computer science 2023年 227卷 159-167页

作者： Ilvico Sonata Yaya Heryadi Cuk Tho Computer Science Department Binus Graduate Program Doctor of Computer Science BINUS University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Computer Science Department School of Computer Science BINUS University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia

The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right articles as needed through topic segmentation applications will helps this process in terms of speed and accuracy. Several neural network models used in segmentation topic applications include Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The Transformer model since its introduction in 2017 for Natural Language Processing (NLP) has a better level of accuracy compared to the RNN, CNN and LSTM models. Transformer model research for segmentation topic applications, especially articles in Indonesian language, is still very limited. This paper will discuss the use of the Transformer model in segmentation topic applications for Indonesian-language articles. The experimental results found that the accuracy produced by the Transformer model was higher than previous LSTM model with the WindowDiff value generated by the model proposed using Transformer is 0.249 and the LSTM baseline model is 0.363, while the while the Pk value generated by the proposed model is 0.279 and the LSTM baseline model is 0.394.

关键词： CNN LSTM RNN Topic segmentation Transformer

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The Pivotal Role of Red Teaming in Cybersecurity Drill Tests in Enhancing Indonesia's Cyber Defenses

SSRN

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SSRN 2024年

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

This study examines the pivotal role of red teaming within the context of cybersecurity drill tests, focusing on its contribution to enhancing Indonesia's cyber defenses. Through a detailed analysis, this research examines Red Teaming's capacity to elevate organizational readiness against cyber threats, identify latent vulnerabilities, and ensure compliance with Indonesia's rigorous cybersecurity regulations. Key findings revealed the profound effect of red teaming on fortifying cybersecurity protocols, showing its capability to discover and effectively counteract security vulnerabilities. This study positions Red Teaming as an essential strategy in the array of cybersecurity practices, offering valuable insights into its application and impact on meeting regulatory standards. Ultimately, this investigation underscores the indispensable role of red teaming in advancing Indonesia's cybersecurity posture amid the constantly evolving digital threat landscape. © 2024, The Authors. All rights reserved.

关键词： Regulatory compliance

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

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International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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ARtopia: A Mobile Application based Digital Crowdfunding Platform 2

ARtopia: A Mobile Application based Digital Crowdfunding Pla...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Daventa, Sherleen Setiawan, Andika Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Crowdfunding has become a popular term these days, and people usually do crowdfunding to raise a certain amount of money for a specific cause. There are already many popular crowdfunding portals in Indonesia where people can send support to anyone with money. When talking about crowdfunding, most people probably think of providing support to others in need, for example, those who are sick and poor. However, crowdfunding is not always like that. Crowdfunding can be applied to any field, especially in the creative industries, as the people in this report will discuss. If you do not know it beforehand, the creative industry sometimes struggles with copyright and online piracy issues. These problems will soon be very detrimental to the artists as creators. Another problem often sought is the artists themselves, especially those still amateurs. They are often hesitant to share their works and promote themselves because they do not know-how. In this case, crowdfunding application development will be necessary to facilitate all artists and content creators (such as content creators, vloggers, or even game developers) to gain support from their fans and promote themselves to become more popular. © 2022 IEEE.

关键词： Crowdsourcing

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