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Machine Learning Approach for Single Nucleotide Polymorphism Selection in Genetic Testing Results

Procedia computer science 2023年 227卷 46-54页

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Mahmud Isnan Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Lactose intolerance is a type of digestive problem that may threaten the population because milk and dairy products compose of nutrients that are essential for human body. Genetic tests possess a great potential to detect lactose intolerance as it can be used in children and even infants. However, a new approach to analyze the genetic test results is needed to elucidate the Single Nucleotide Polymorphisms (SNPs) that are related to lactose intolerance. In this work, we utilized the machine learning based feature selection to select the SNPs associated with lactose tolerance trait from genotyping samples of direct-to-customer (DTCG genetic tests, obtained from the public database. Recursive Feature Elimination (RFE) with XGBoost model was used to perform feature selection. We also compared three different models, such as XGBoost, support vector machine (SVM), and random forest (RF) for training the selected features. Our findings revealed that 20 SNPs (out of 3501) were chosen, with rs4394668 as the most important variables (F-score 0.009). Furthermore, when compared to the RF and SVM models, the XGBoost model had the highest accuracy (0.87). Further studies should be undertaken to elucidate how the selected SNPs may lead to the lactose intolerance trait.

关键词： lactose intolerance SNP genetic test feature selection

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Development of a prototype tool to automate the counting of SNAP function points of the graphical interface 5

Development of a prototype tool to automate the counting of ...

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5th Congreso Internacional en Inteligencia Ambiental, Ingenieria de Software y Salud ElectroWnica y Movil, AmITIC 2022 - 5th International Congress on Ambient Intelligence, Software Engineering and Electronic and Mobile Health, AmITIC 2022

作者： Sánchez, Merlin Jenkins, Marcelo Quesada-López, Christian Martínez, Alexandra University of Costa Rica Graduate Program in Computer Science San Pedro Costa Rica University of Costa Rica Computer Science Department San Pedro Costa Rica

ISBN: (数字)9781665485869

ISBN: (纸本)9781665485869

In this work we present the results of the creation and evaluation of a tool prototype that automatically calculates the size of the non-functional requirements (NFR) of the User Interface 2.1 subcategory of the SNAP (Software Non-functional Requirements) method. Assessment Process). We evaluated the accuracy of the tool compared to manual counting and results show an accuracy level of 100% in SNAP point counting for category 2.1 graphical interface, although with some differences in the correct detection of user interface properties. This work demonstrates the feasibility of a software tool to automate the counting of SNAP points. © 2022 IEEE.

关键词： User interfaces

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor

A Systematic Literature Review of CNN Approaches in Classify...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Aedentrisa Yasmanda Paulindino Bens Pardamean Gregorius Natanael Elwirehardja Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data.

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Sentiment Analysis of COVID-19 Public Activity Restriction (PPKM) Impact using BERT Method

arXiv

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arXiv 2022年

作者： Fransiscus Girsang, Abba Suganda Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Covid-19 has grown rapidly in all parts of the world and is considered an international disaster because of its wide-reaching impact. The impact of Covid-19 has spread to Indonesia, especially in the slowdown in economic growth. This was influenced by the implementation of Community Activity Restrictions (PPKM) which limited community economic activities. This study analyzes the mapping of public sentiment towards PPKM policies in Indonesia during the pandemic based on Twitter data. Knowing the mapping of public sentiment regarding PPKM is expected to help stakeholders in the policy evaluation process for each region. The method used is BERT with IndoBERT specific model. The results showed the evaluation value of the IndoBERT f-1 score reached 84%, precision 86%, and recall 84%. Meanwhile, f-1 scores 70%, 72% precision, and 70% recall for evaluating the use of SVM. Multinominal Naïve Bayes evaluation shows an f-1 score of 83%, precision of 78%, and recall of 80%. In conclusion, the BERT method with the IndoBERT model is proven to be higher than classical methods such as SVM and Multinominal Naïve Bayes. © 2022, CC BY-NC-ND.

关键词： Sentiment analysis

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Instagram Augmented Reality Marketing in Boosting Brand Awareness and e-WOM in the Indonesian Bottled Tea Industry

Instagram Augmented Reality Marketing in Boosting Brand Awar...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Banu Rinaldi Maria Grace Herlina Karto Iskandar Department of BINUS Business School Undergraduate Program Business Creation Program Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

This study explores the efficacy of Instagram's augmented reality (AR) features in enhancing brand awareness and electronic word-of-mouth (e-WOM) within Indonesia's bottled tea industry. The research uses a cross-sectional survey and partial least square structural equation modeling (PLS-SEM) to focus on how AR can transform consumer engagement and interaction on social media platforms. Findings from a sample of 260 bottled tea consumers in Greater Jakarta indicate that AR significantly enhances brand awareness, substantially influencing e-WOM. The integration of AR into Instagram marketing not only captivates and engages users but also fosters stronger consumer-brand relationships and promotes brand loyalty through interactive and immersive digital experiences. The study’s implications underscore AR's role in revolutionising marketing strategies by providing immersive experiences that are shown to increase brand visibility and influence purchasing decisions. Limitations and future research directions are discussed, highlighting the potential for broader applications of AR in different market segments and its impact on various consumer demographics.

关键词： Industries Social networking (online) Urban areas Transforms Boosting Mathematical models Web sites Multimedia communication Augmented reality Consumer electronics

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Boosting Team Spirit: The Impact of Ambidextrous LMS Usage on Indonesian Academics

Boosting Team Spirit: The Impact of Ambidextrous LMS Usage o...

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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Maria Grace Herlina Karto Iskandar Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

The study explores the role of Learning Management Systems (LMS) in enhancing academic teamwork and ambidextrous learning in Indonesian higher education. It examines how different uses of LMS, both explorative and exploitative, affect academics' morale and teamwork. The study employs Structural Equation Modeling to analyse questionnaire data from academics in Greater Jakarta, Indonesia. Key findings reveal significant correlations between the exploration and exploitation of LMS, ambidextrous learning, and the spirit of teamwork. The research contributes to understanding how LMS fosters collaborative learning and innovation in academic settings, highlighting its importance in modern education.

关键词： Learning management systems Technological innovation Analytical models Correlation Federated learning Education Boosting

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier 8

Proposing a visualized comparative review analysis model on ...

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

作者： Sano, Albert Verasius Dian Stefanus, Adriel Anderson Madyatmadja, Evaristus Didik Nindito, Hendro Purnomo, Agung Sianipar, Corinthias P.M. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Kyoto University Kyoto606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis. © 2023 The Authors. Published by Elsevier B.V.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression

Systematic Literature Review of Transformer Model Implementa...

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International Conference of computer and Informatics Engineering (IC2IE)

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

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities.

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A Comparison of Lexicon-based and Transformer-based Sentiment Analysis on Code-mixed of Low-Resource Languages 1

A Comparison of Lexicon-based and Transformer-based Sentimen...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Tho, Cuk Heryadi, Yaya Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University BINUS Graduates Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Sentiment analysis from code-mixed texts has been gaining wide attention in the past decade from researchers and practicians from various communities motivated, among others, by the increasing popularity of social media resulted in a huge volume of code-mixed texts. Sentiment analysis is an interesting problem in Natural Language Processing with wide potential applications, among others, to understand public concerns or aspirations toward some issues. This paper presents experimentation results aim to compare performance of lexicon-based and Sentence-BERT as sentiment analysis models from code-mixed of low-resources texts as input. In this study, some code-mixed texts of Bahasa Indonesia and Javanese language are used as sample of low-resource code-mixed languages. The input dataset are first translated to English using Google Machine Translation. The Sentiwordnet and VADER are two English lexicon label datasets used in this study as basis for predicting sentiment category using lexicon-based sentiment analysis method. In addition, a pretrained Sentence-BERT model is used as classification model from the translated input text to English. In this study, the dataset is categorized into positives and negative categories. The model performance was measured using accuracy, precision, recall, and F1 score. The experimentation found that the combined Google machine translator and Sentence-BERT model achieved 83 % average accuracy, 90 % average precision, 76 % average recall, and 83 % average F1 Score. © 2021 IEEE.

关键词： Sentiment analysis

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