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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Ruys, Joseph Hartono, Lenno Aubert Setiawan, Francesco Emmanuel Qomariyah, Nunung Nurul Bahana, Raymond School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

This research explores a new approach to building recommendation systems in the telecommunications industry, focusing on revenue generation rather than traditional service ratings. By analyzing data from a well-known local telecommunications company, this research proposes two different strategies: one targeting customers who generate minimal revenue and another for customers who have higher and consistent revenue. Utilizing collaborative filtering with item-based techniques, the system uses Cosine and Jaccard Similarity algorithms to improve its recommendation results. The Jaccard Similarity algorithm, in particular, showed a high accuracy rate of 93%. This method, which is based solely on income data and customer segmentation based on average daily income, offers new insights into the development of recommendation systems in the absence of explicit service ratings. © 2024 IEEE.

关键词： Telecommunication industry

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Harnessing Deep Learning for Ocular Disease Diagnosis

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Procedia computer science 2024年 245卷 914-923页

作者： Jessica Ryan Dave Andrew Nathaniel Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Vision impairment, often caused by preventable ocular diseases can be challenging to diagnose accurately and prone to human error. Automation using technology, particularly deep learning, offers a promising solution to aid in accurate and efficient disease detection. This study explores the use of different CNN models specifically VGG-16, VGG-19, ResNet-50, and ResNet-152v2, for detecting ocular diseases. Simple fine-tuning is applied to these models, and their performance is compared to identify the most effective model. The purpose is to show how different models contribute to establishing reliable illness detection systems. The results reveal that most of these models perform well with even minimal fine-tuning. Among the models, ResNet-152v2 achieved the highest training accuracy of 90.36% demonstrating its capacity to learn from the training data. In contrast, ResNet-50 offered a more balanced performance with marginally lower accuracy, making it a robust choice for general application.

关键词： ocular diseases deep learning classification

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Development of Advance Augmented Reality Based Application on Traditional Music Instrument Book: Gamelan Enhancing Indonesian Culture 18

Development of Advance Augmented Reality Based Application o...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Permana, Frihandhika Rifah, Lailatul Dirgantara, Satrya School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Digital Language Learning Visual Communication Design Bina Nusantara University Jakarta11480 Indonesia Visual Communication Design Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350331011

The applications of augmented reality (AR) in the realm of musical instruments represent a subset of the broader field of AR, presenting numerous opportunities for the advancement of novel entertainment and educational models. The integration of augmented reality technology has the potential to enhance the conventional gamelan performance, rendering it a more captivating contemporary rendition, given the prevailing technological landscape. Using the system Development Life Cycle (SDLC), an Augmented Reality Gamelan Book for Children can be developed to facilitate students learning about the traditional music instruments, also to develop their ability in learning English. Within this augmented reality (AR) experience, the researchers have integrated interactive components, notably visual representations that youngsters may engage with by showing the 3D Object. The photos are altered markers designed to accommodate the innate inquisitiveness of toddlers and their proclivity for novel experiences, particularly when presented in shades of grey. To optimize the performance of the application, it was noted that specific distances and heights yielded a 100% success rate. These distances include 45 cm, while the corresponding heights were 30 cm. Additionally, distances of 50 cm, 55 cm, 60 cm, and 65 cm were found to be successful when paired with heights of 25 cm and 30 cm. These specified specifications provide the reliable detection of markers by smartphones, hence ensuring a high level of accuracy in the augmented reality (AR) experience. © 2024 IEEE.

关键词： Augmented reality

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Analyzing the Impact of Customer Service Chatbots on User Satisfaction 15

Analyzing the Impact of Customer Service Chatbots on User Sa...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Antonio, Elkana Rahadian Putra Fadhilah, Muhammad Farras Faiq, Fikri Fredyan, Renaldy Pranoto, Hady School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

In this current digital era, chatbot apps are widely used in customer service with great automation, round-the-clock customer service, and constant customer support. The main problems with chatbots are that they are unable to provide the intended end solution and fail to reproduce the typical customer experience with human interaction. This research aims to see the impact of customer service chatbots on user satisfaction. This paper uses a quantitative review method that results from the translation of the impact given by the Customer service chatbot. The results of this study show that customer service chatbots for most users increase their satisfaction compared to conventional customer service, with chatbots providing customer comfort and efficiency, reducing customer service time, improving information, increasing productivity, and giving customers tailored experiences. The findings in this research benefit the industry by allowing it to evaluate its chatbot applications because customer satisfaction becomes a fundamental issue in this research. Customers may choose a different application from competitors if a company fails to address this issue. © 2023 IEEE.

关键词： Customer satisfaction

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LDA Topic Modeling for Bioinformatics Terms in arXiv Documents 9th

LDA Topic Modeling for Bioinformatics Terms in arXiv Documen...

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

作者： Karnyoto, Andrea Stevens Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "computer science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology;thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein. © 2024 The Authors.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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Trend analysis in competitive programming using topic modeling

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Procedia computer science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.

关键词： competitive programming topic modeling trend analysis

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Semi-automated meningioma segmentation with bounding boxes 9th

Semi-automated meningioma segmentation with bounding boxes

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

作者： Heryanto, Nur Adhianti Isnan, Mahmud Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores. © 2024 The Authors.

关键词： Magnetic resonance imaging

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Machine Learning Approach for Single Nucleotide Polymorphism Selection in Genetic Testing Results 8

Machine Learning Approach for Single Nucleotide Polymorphism...

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

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Isnan, Mahmud Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 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. © 2023 The Authors. Published by Elsevier B.V.

关键词： feature selection genetic test lactose intolerance SNP

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Establishing a Standard Operating Procedure (SOP) for Palm Oil Plantation FFB Image Capture: Utilizing YOLOv8 for Counting and Ripeness Classification 6

Establishing a Standard Operating Procedure (SOP) for Palm O...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Hidayat, Muhammad Rizky Sulistyo, Pavel Azarya Oktario, Hansen Gunawan, Alexander A.S. Mimboro, Prasetyo Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Plant Division Regional 2 PT. Perkebunan Nusantara IV Medan11480 Indonesia

ISBN: (纸本)9798331539603

Almost all oil palm harvest management is still done manually today, especially in the monitoring stage where oil palm farmers assess the ripeness of oil palm fresh fruit bunches (FFB) in the plantation. The monitoring stage, which is a crucial step, requires re-checking the ripeness of fresh fruit bunches (FFB) at the collection point (TPH) to ensure that the bunches brought to the factory are ripe. This research focuses on creating an SOP to be implemented in the plantation so that the AI in the automated system, also created in this research, can be used to optimally detect, calculate, and classify the ripeness of the FFB. By using the YOLO8 algorithm based on deep learning and CNN, the dataset used consists of 4257 images taken directly from PTPN IV plantations. The results of this study showed good model performance, with an accuracy of 96.56%. The application of this model is expected to increase the efficiency and accuracy of FFB quality monitoring, reduce losses, and support better data-based decision-making. © 2024 IEEE.

关键词： Efficiency

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