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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ikrar Harvy Abba Suganda Girsang Melva Hermayanty Saragih Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods.

关键词： Training Convolution Social networking (online) Neural networks Blogs Training data Motion pictures

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Study of runway crosswind and tailwind potential for airport sustainability: A study of Soekarno Hatta airport, Cengkareng, Indonesia 2

Study of runway crosswind and tailwind potential for airport...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Sardjono, W. Zulkarnain Kusnoputranto, H. Soesilo, T.E.B. Utama, D.N. Sudirwan, J. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management BINA NUSANTARA University Jakarta11480 Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Faculty of Public Health University of Infonesia Jakarta Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

Airport runway designs must consider wind climatology to reduce the potential for crosswind and tailwind events that can cause accidents and aircraft delays. The large potential of crosswind and tailwind on a runway at the airport is very detrimental to aircraft passengers and airlines. Therefore, it is necessary to know the potential of crosswind and tailwind on the runway of Soekarno Hatta Airport and find the right parameters for the approximate direction and speed of the wind on the airport runway. Analysis was carried out using the direction and wind speed data from the Soekarno Hatta meteorological station in 2007-2017 to determine the potential for crosswind and tailwind on the runway of Soekarno Hatta airport and run a Weather Research and Forecasting (WRF) weather model to determine the parameterization of the right weather model for wind forecast at Soekarno Hatta airport. The highest maximum crosswind component in the 2007-2017 period occurred in August 30.53 knots, while the highest maximum tailwind of 25 knots occurred in January for a 250-degree runway and 24 knots occurred in August for a 070-degree runway. The potential for crosswind and tailwind is different every month. The scheme produces forecasts of wind direction and speed with a strong level of correlation with observations of wind direction and speed from the Soekarno Hatta meteorological station, the correlation value is 0.61. The most significant social impact of plane delays is time loss for passengers. While the economic impact on one of the crosswind events at Soekarno Hatta airport was an economic loss for one airline of US $ 43,392. © Published under licence by IOP Publishing Ltd.

关键词： Losses

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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International Conference on Data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

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Cancer Prediction Using Clinical and Genomic Data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono 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 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services Predictive models Data models Bioinformatics Cancer Systematic literature review

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The Country's Implementation and Adoption of Standardized Health Terminologies to Promote Interoperability: A Systematic Literature Review

The Country's Implementation and Adoption of Standardized He...

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International Conference on Information Management and Technology (ICIMTech)

作者： Taufiq Sitompul Harco Leslie Hendric Spits Warnars Meyliana Achmad Nizar Hidayanto Harjanto Prabowo Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Modern healthcare systems demand comprehensive information systems but face obstacles during adoption. Organizational and structural complexity, especially decentralized systems, challenges the integrated management and sharing of data across information systems. Interoperability is critical to address these issues effectively in heterogeneous and distributed environments. However, different medical terminology hinders interoperability within health information systems. To address this, a systematic literature review examined various medical terminology standards for national interoperability. From 790 articles, 12 were selected according to PRISMA guidelines with inclusion and exclusion criteria. Standard health terminology is critical to achieving interoperability, and many countries have adopted it, although some have required adaptation to accommodate local contexts. Data models, governance, and maintenance of standards are equally important in achieving national interoperability. In addition, standardized health terminology promotes consistency and uniformity in terminology, which is beneficial for decision-making by stakeholders in the health system.

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Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage & Trends

Semantic Literature Review on Non-Fungible Token: Expansion ...

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International Conference on Information Management and Technology (ICIMTech)

作者： Gede Indra Raditya Martha Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.

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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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Adopting Centroid and Bandwidth to Shape Security Line

Adopting Centroid and Bandwidth to Shape Security Line

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International Conference of computer science and Information Technology

作者： Heni Ispur Pratiwi Iman Herwidiana Kartowisastro Benfano Soewito Widodo Budiharto Doctorate Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctorate Program of Computer Science Bina Nusantara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient noise factors exist challenging issues of voice changes in speech recognition systems to be resolved. This paper presents experiment results of observing those issues, and its goal was obtaining the elements of voice features to secure the speaker's identity. Mel-frequency Cepstral Coefficient (MFCC) was used to conduct observations on spectral centroid and bandwidth, it has effectively performed capturing features of individual speech signal. The experiment was conducted with data split into three groups: under 13, range 13 to 19, and above 19 years old for both male and female. The results showed that both gender under 13 have centroid voices reached to 98%, age of 13 to 19 remain with centroid reached 98% of bandwidth for male and passing over 100% for female, then above 19 for both genders speak lower with centroid 50% of bandwidth, and male above age of 19 remains to keep centroid about 50% and female changes to 95%. MFCC fits to expose the speech spectrum for the purpose of an analysis frame on specific local spectral properties of voices, and the finding is to form a security line or an individual profiling adoption.

关键词： computer science Smart cards Shape Bandwidth Speech recognition Medical services Banking

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Enhancing Historical Learning Using Role-Playing Game on Mobile Platform

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Procedia computer science 2021年 179卷 886-893页

作者： Gede Putra Kusuma Louis Khrisna Putera Suryapranata Evan Kristia Wigati Yesun Utomo Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Learning history means to learn what caused the world around us to become the way it is right now. For decades, historical events were recorded and taught to younger generations to learn from them. However, most students found that learning history is boring because they could not feel or understand the moment of those historical events. This paper aims to investigate how a game-based learning approach influences the achievement and motivation of historical learning through a mobile learning environment. We propose game-based historical learning using the role-playing game on a mobile platform. The implementation is based on the history of the first army general in Indonesia, General Sudirman. The game was tested to 63 Junior High School students in Jakarta. Based on the evaluation, using the game can increase student learning motivation and learning achievement.

关键词： Game-Based Learning Historical Learning Role-Playing Game Mobile Platform Statistical Analysis

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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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