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

作者： Chrysler, A. Warnars, H.L.H.S. Utomo, W.H. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients. © Published under licence by IOP Publishing Ltd.

关键词： Mobile computing

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The Utilization of Mobile Communication on Marketing: A Systematic Review

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Procedia computer science 2023年 227卷 101-109页

作者： Adora Vania Permana Agung Purnomo Haryadi Sarjono Fairuz Iqbal Maulana Etsa Astridya Setyani Entrepreneurship Department BINUS Business School Undergraduated Program Bina Nusantara Univerisity Jakarta 11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

The advancement of mobile communication technology with entrepreneurial minds is advancing, and its use is increasingly playing an important role in business, marketing, and life. However, there are not many studies that focus on the role of mobile communication in the business world, especially those that focus on marketing. The purpose of writing this paper is to determine the status of research and the relationship between mobile communication in the field of marketing. The method used in this research is systematic literature review using the PRISMA protocol, involving 29 articles. Through quantitative analysis, we found that many years of article publication occurred mostly in 2020 due to the development of 5G technology. Australia is the largest article producing country in the world because many researchers are trying to examine public acceptance of mobile communication technology. The use of mobile communication is also mostly used in the marketing and segmentation processes in multilevel firm level marketing with multipoint mobile communication. On the other hand, articles with a marketing perspective that discuss price issues have never been discussed.

关键词： Entrepreneurship marketing systematic review

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Low-Resolution Face Recognition for CCTV and Edge-Powered Smart Attendance Systems

Low-Resolution Face Recognition for CCTV and Edge-Powered Sm...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Nico Surantha Edward Yose Sani Muhamad Isa Department of Electrical Electronics and Communication Engineering Faculty of Science and Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Attendance systems have become more modern, and one of the biometric systems without physical contact is face recognition. However, many face-based attendance systems still carry out attendance individually and cannot detect multiple faces simultaneously. In addition, capturing facial data in real-time is still a challenge because the relatively large distance between the camera and the individual reduces the ability to recognize faces. The general solution is to use super-resolution to generate better-quality faces while maintaining the main facial recognition features. One technique still being researched is super-resolution generative adversarial networks (SRGAN). SRGAN can enlarge the resolution of captured images and maintain image quality sufficient for face recognition. The attendance system can be easily integrated into edge devices such as the Jetson Nano. This paper proposes automatic and effective attendance systems with the super-resolution technique to detect and recognize faces in low-resolution input. The experimental results show that using face data capture with a resolution of 40 × 40 pixels and a four-fold magnification results in a resolution of 160 × 160 pixels. Combining Face SRGAN with FaceNet architecture as the basis of face recognition can achieve an accuracy rate of 78.19% and an F1-Score of 81.13% with an average processing time of 1.61 seconds per frame on a PC and 14.55 seconds per frame on a Jetson Nano at an average of face recognition per frame of as many as up to 8 faces simultaneously.

关键词： Support vector machines Image quality Accuracy Face recognition Image edge detection Superresolution Generative adversarial networks

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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An innovative artificial intelligence-based visualization and rendering for e-commerce startup booth design

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Procedia computer science 2024年 245卷 147-154页

作者： Calista Kalleya Andi Pramono Chelsea Putri Angelina Wilbert Alvin Cuarista Riefky Prabowo Fairuz Iqbal Maulana Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Marketplace development is increasing, as is the number of people with active internet access. Unive is a startup that runs an e-commerce platform connecting Student Activity Units with consumers. Focuses on fostering student entrepreneurship through practical business experience. For its development, this startup needs an interior for business escalation. Designers need software integrated with artificial intelligence (AI) in interior design. This study explores the integration of AI-powered rendering tools in the design of e-commerce platforms, focusing on how these tools enhance the user experience and satisfaction. The research employs a mixed-method approach, combining qualitative and quantitative analyses. The author collected data through user surveys, expert interviews, and performance metrics of AI rendering tools. The author based the evaluation criteria on adherence to interior design principles and the quality of rendering results. Findings indicate that selecting furniture and supporting components that adhere to interior design principles results in optimal rendering outcomes. These outcomes are close to user expectations, significantly improving user engagement and satisfaction. Detailed analysis shows the effectiveness of AI rendering in various design scenarios. The discussion highlights the implications of integrating AI rendering tools in e-commerce platforms. It addresses the potential of these tools to revolutionize user interaction and provides insights into future developments and applications in the field. This study demonstrates the value of AI-powered rendering tools in enhancing e-commerce platform design. By adhering to interior design principles, these tools can achieve rendering results that meet user expectations, ultimately leading to improved user experiences.

关键词： Interior Design Artificial Intelligence Prototype Render Start-up

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Question Classification of University Admission using Named-Entity Recognition (NER)

Question Classification of University Admission using Named-...

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International Conference on Information Technology, computer and Electrical Engineering (ICITACEE)

作者： Emny Harna Yossy Derwin Suhartono Agung Trisetyarso Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users’ most frequent question type is “What” by 63.3%, and the answer category is “tuition fees” by 22.4%. It can be concluded that the most frequent question type asked by users is “What”, and the most frequent topic of interest is “tuition fees”. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees.

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Literature study of stunting supplementation in Indonesian utilizing text mining approach 7

Literature study of stunting supplementation in Indonesian u...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Sudigyo, Digdo Hidayat, Alam Ahmad Nirwantono, Rudi Rahutomo, Reza Trinugroho, Joko Pebrianto Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry

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Procedia computer science 2023年 227卷 307-315页

作者： Muhammad Asrol Sofyan Wahyudi Suharjito Christian Harito Ditdit N Utama Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence Sejong University Seoul 05006 South Korea

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively; and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively; and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset.

关键词： Training computer science Computational modeling Instruments Machine translation Task analysis Information technology

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Threat Modeling for Honeypot Deployment

Threat Modeling for Honeypot Deployment

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Information Technology International Seminar (ITIS)

作者： Kalpin Erlangga Silaen Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes.

关键词： Threat modeling Seminars Firewalls (computing) Filtering Prevention and mitigation Complexity theory Security Information technology Monitoring Cyberattack

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