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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Andreas, Raynaldo Maria, Sarah Satyadhana, Alexander Khrisna Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Muyeba, Maybin K. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Indonesia Computer Science Bina Nusantara University Jakarta11480 Indonesia Telkom University Bandung40257 Indonesia MIST Consulting ManchesterM410SA United Kingdom

ISBN: (纸本)9798350398496

Sign language is the most commonly used communication medium for people who are deaf and mute. By using body movements, they can convey meaning through visual cues, not vocal cues. This type of language is learned almost exclusively by people who are deaf or dumb and those who interact with them on a daily basis. Most people do not learn it because sign language is not part of basic education, and the lack of need to use it, especially from a young age. Most of the spoken languages in the world usually have sign language variants according to the language of their respective countries. In this study, a mobile application is developed for children to learn Indonesian sign language where the process model is designed using use case diagrams, and the database design is modeled with class diagrams. As for the implementation, the Android Studio software is used for the application, and the MySQLdatabase is for database storage. © 2023 IEEE.

关键词： Mobile computing

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Implementation of the EfficientNet Model for Identification of Wild Edible Plants

Implementation of the EfficientNet Model for Identification ...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Maria Susan Anggreainy Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Wild plants are plants that are not cultivated but wild plants can be easily found in the neighborhood. However, not all wild plants can be utilized as food crops by the community. Therefore, it is necessary to know what wild plants can be consumed so that people do not have to worry about food availability. The purpose of this study is to determine whether the EfficientNet model can be used to identify wild plants that can be consumed. Experimental results show that the EfficientNet model has an accuracy of 85% in identifying wild plants that can be consumed.

关键词： Training Accuracy Transfer learning Neural networks Crops Data models Information technology Tuning Overfitting Image classification

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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Utilizing Sentiment Insights for Software Evalution with Aspect-Based Sentiment Analysis in App Reviews Using IndoBERT-Lite

Utilizing Sentiment Insights for Software Evalution with Asp...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Nikita Ananda Putri Masaling Haryono Soeparno Yulyani Arifin Ford Lumban Gaol Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

With mobile applications increasingly used daily, understanding user feedback is vital for software improvement. Manual analysis of user reviews on platforms is time-consuming and lacks scalability. This study utilizes aspect-based sentiment analysis with the IndoBERT-Lite model to automate the categorization and sentiment detection of feedback. Despite advancements, precise aspect classification and sentiment detection in Indonesian language reviews remain challenging due to limited resources. Using 5,000 Tokopedia reviews, labeled across five aspects, namely functionality, service, performance, usability, and design, the model achieved optimal results with batch size 32, showing 0.85 accuracy and 0.866 F1 score for aspect classification, and 0.998 across all metrics for sentiment classification.

关键词： Measurement Seminars Sentiment analysis Analytical models Accuracy Reviews Scalability Software Mobile applications Usability

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Mobile Application as Community Social Media 6

Mobile Application as Community Social Media

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pahlevi, Teuku Ryan Haryanto, Johannes Adha, Rahmat Alfian, Bayu Warnars, Harco Leslie Hendric Spits Widiono, Rahayu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350398496

Today's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle of today's people, which of course almost half of their daily life is spent in front of gadgets. On the one hand, this is undoubtedly very good and supports humans in carrying out their daily life activities, but on the other hand, this raises new problems where humans start to lack time to communicate and socialize with each other and often making them stressed and anxious because they are already tired and do not know how to socialize anymore. This paper begins with a model and implementation of a very simple mobile application that behaves as a social media inspired by *** as a social media for academics. The design of this mobile application is done by using use case diagrams to describe existing processes and class diagrams to describe class relationships. © 2023 IEEE.

关键词： Social networking (online)

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Performance Analysis of Heart Disease Prediction using Kernels in Support Vector Machine Model

Performance Analysis of Heart Disease Prediction using Kerne...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Maria Susan Anggreainy Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Linear data is a collection of data that is linearly separated by a straight line and nonlinear data is a collection of data that is not separated by a line. Usually, non-linear data is separated by irregular curves. However, for non-linear data. Support Vector Machine (SVM) is difficult to classify data for non-linear data. The solution is to use Kernel Trick. Kernel Trick is a simple method used to map low-dimensional nonlinear data and transform it into a higher-dimensional space. The goal is to make it easier to classify data by finding a hyperplane that can separate the dataset linearly well. This research is to create a program that can predict heart disease using the Support Vector Machine (SVM) model with Kernel Tricks. Heart disease prediction is very important because the heart is one of the most important organs in the human body. The results indicated that the SVM model with the Radial Basis Function (RBF) Kernel achieved the highest accuracy compared to the Linear Kernel and Polynomial Kernel.

关键词： Support vector machines Heart Accuracy Training data Transforms Predictive models Polynomials Kernel Diseases Testing

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Enhancing Incremental Dataflow Analysis in an IDE 24

Enhancing Incremental Dataflow Analysis in an IDE

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Harianto, Koko Wang, Feng-Jian Abouzeid, Mahmoud M. National Yang Ming Chiao Tung University Eecs International Graduate Program Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350365658

Incremental dataflow analysis is a conventional technique adopted in syntax-directed editors, popularly used in Integrated Development Environments (IDEs). However, dataflow anomaly detection during program editing in IDEs remains a challenge due to the interaction with newer functionalities, such as AI-powered plugins like Copilot, which might insert and modify code lines directly. These interactions impact both the efficiency and effectiveness of dataflow anomaly detection processes. This paper introduces an enhanced incremental dataflow analysis approach in IDEs, focused on the analysis of dataflow within program structures. Our approach introduces a modified version of SP-tree, named SP-graph. This modification, involving the addition of supporting edges, facilitates data storage and enhances dataflow analysis incrementally. We also present a mechanism that streamlines dataflow anomaly detection within IDEs by identifying modifications that may impact dataflow, updating relevant data, and conducting immediate analysis. In addition, incremental concurrent anomaly detection in SP-graph shall be studied further. We demonstrate the effectiveness of our approach through a case study. © 2024 IEEE.

关键词： Data flow graphs

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ApiPot: A Novelty API Honeypot for Exhaustive Attack Feature Detection in HTTP Protocol

ApiPot: A Novelty API Honeypot for Exhaustive Attack Feature...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Kalpin Erlangga Silaen Benfano Soewito Maria Susan Anggreainy Aditya Kurniawan Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

This paper introduces a novel honeypot called Apipot, specifically designed to emulate API services over the HTTP protocol, with the main purpose of capturing the feature set of each HTTP request more than what is typically recorded in standard web server logs. Web server limitations in logging HTTP requests to API-based applications from visitors mean that critical data required for comprehensive threat analysis and attacker profiling is lost. Apipot overcomes this problem by simulating API services and capturing all the features of HTTP requests. We expected that capturing more features in HTTP requests would enable deeper insight into attacker methods, thereby significantly improving sophisticated cyber attack detection and the ability to more accurately profile attackers, thereby contributing to identifying attackers behind cyber attacks. Our experiment result shows that Apipot can capture 119 unique HTTP headers and 491 unique queries, along with IP source, body, URL, host, and other HTTP request features, from 196,206 requests toward Apipot in 61 days. This result proves that Apipot can capture more features from an HTTP request compared to a web server. As far as we know, Apipot is the first honeypot that mimics an API to capture HTTP request features.

关键词： Uniform resource locators Feature detection Feature extraction HTTP Web servers Vectors IP networks Cyberattack Standards Monitoring

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Suitability of Latest Version of YOLOv11 in Drone Development Studies

Suitability of Latest Version of YOLOv11 in Drone Developmen...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Mohammad Faisal Riftiarrasyid Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department Binus Graduate Program – Computer Science Bina Nunsantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Although drones have become increasingly advanced and sophisticated, they still face challenges such as limited computing power and high-power consumption. Optimizing their features is a key strategy to address these limitations. One critical feature that can be further improved is the algorithm for specialized tasks like obstacle detection. Recent studies have primarily relied on earlier versions of YOLO for this purpose. However, with the release of the latest YOLO versions, YOLOv10 and YOLOv11, there is an opportunity to assess their suitability for drone applications. In this study, we trained YOLOv10-nano and YOLOv11-nano using a drone-specific dataset and compared their performance with similar studies. The results indicate that YOLOv11-nano outperforms YOLOv10-nano in bounding-box prediction, achieving a higher mAP of 0.957 with a 0.22 difference, as well as faster inference times with a 74.1 ms improvement, resulting in a frame rate gain of 0.0421 FPS. However, when compared to earlier studies, YOLOv11-nano demonstrates superior precision but lags in real-time processing speed. These findings highlight the trade-off between accuracy and inference time, highlighting the suitability of YOLOv11-nano for applications in drone development where accuracy takes precedence over processing speed.

关键词： YOLO Training Seminars Accuracy Cameras Prediction algorithms Real-time systems Intelligent systems Information technology Drones

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