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Cryptography, Informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

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Topic Segmentation using Transformer Model for Indonesian Text

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Procedia computer science 2023年 227卷 159-167页

作者： Ilvico Sonata Yaya Heryadi Cuk Tho Computer Science Department Binus Graduate Program Doctor of Computer Science BINUS University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Computer Science Department School of Computer Science BINUS University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia

The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right articles as needed through topic segmentation applications will helps this process in terms of speed and accuracy. Several neural network models used in segmentation topic applications include Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The Transformer model since its introduction in 2017 for Natural Language Processing (NLP) has a better level of accuracy compared to the RNN, CNN and LSTM models. Transformer model research for segmentation topic applications, especially articles in Indonesian language, is still very limited. This paper will discuss the use of the Transformer model in segmentation topic applications for Indonesian-language articles. The experimental results found that the accuracy produced by the Transformer model was higher than previous LSTM model with the WindowDiff value generated by the model proposed using Transformer is 0.249 and the LSTM baseline model is 0.363, while the while the Pk value generated by the proposed model is 0.279 and the LSTM baseline model is 0.394.

关键词： CNN LSTM RNN Topic segmentation Transformer

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The Pivotal Role of Red Teaming in Cybersecurity Drill Tests in Enhancing Indonesia's Cyber Defenses

SSRN

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SSRN 2024年

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

This study examines the pivotal role of red teaming within the context of cybersecurity drill tests, focusing on its contribution to enhancing Indonesia's cyber defenses. Through a detailed analysis, this research examines Red Teaming's capacity to elevate organizational readiness against cyber threats, identify latent vulnerabilities, and ensure compliance with Indonesia's rigorous cybersecurity regulations. Key findings revealed the profound effect of red teaming on fortifying cybersecurity protocols, showing its capability to discover and effectively counteract security vulnerabilities. This study positions Red Teaming as an essential strategy in the array of cybersecurity practices, offering valuable insights into its application and impact on meeting regulatory standards. Ultimately, this investigation underscores the indispensable role of red teaming in advancing Indonesia's cybersecurity posture amid the constantly evolving digital threat landscape. © 2024, The Authors. All rights reserved.

关键词： Regulatory compliance

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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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Monitoring Applications for Vehicle based on Internet of Things (IoT) using the MQTT Protocol 8

Monitoring Applications for Vehicle based on Internet of Thi...

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

作者： Nurudin Sukmandhani, Arief Agus Zarlis, Muhammad Computer Science Department BinusOnline Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Monitoring of vehicle conditions is needed as one of the securities in driving because one of the accident rates is negligence in checking the condition of the vehicle. This study aims to help reduce the level of accidents and increase human awareness for vehicle conditions, especially minibus type vehicles. The system works to provide information on the condition of vehicles sourced from the Engine Control Unit (ECU). ECU data cars are obtained by SNIP traffic broadcast message and ask for data directly at the ECU. The car can provide vehicle information using a Canbus communication protocol through the OBD-II socket. Vehicle information can be seen by users through the website in real time. Based on the evaluation from the user side, this system provides ease of monitoring vehicle conditions because it can be accessed via the internet using various kinds of devices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Blackbox ECU OBD-II Sstem Logging

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

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International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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ARtopia: A Mobile Application based Digital Crowdfunding Platform 2

ARtopia: A Mobile Application based Digital Crowdfunding Pla...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Daventa, Sherleen Setiawan, Andika Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Crowdfunding has become a popular term these days, and people usually do crowdfunding to raise a certain amount of money for a specific cause. There are already many popular crowdfunding portals in Indonesia where people can send support to anyone with money. When talking about crowdfunding, most people probably think of providing support to others in need, for example, those who are sick and poor. However, crowdfunding is not always like that. Crowdfunding can be applied to any field, especially in the creative industries, as the people in this report will discuss. If you do not know it beforehand, the creative industry sometimes struggles with copyright and online piracy issues. These problems will soon be very detrimental to the artists as creators. Another problem often sought is the artists themselves, especially those still amateurs. They are often hesitant to share their works and promote themselves because they do not know-how. In this case, crowdfunding application development will be necessary to facilitate all artists and content creators (such as content creators, vloggers, or even game developers) to gain support from their fans and promote themselves to become more popular. © 2022 IEEE.

关键词： Crowdsourcing

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Critical Factors of Disruptive Innovation: A Review

Critical Factors of Disruptive Innovation: A Review

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Yoga Prihastomo Harjanto Prabowo Agung Trisetyarso Haryono Soeparno Computer Science Department - BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department - Department of Mathematics Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

People acknowledge innovation as a critical and central factor in increasing economic output and productivity. The phrase disruptive innovation was first familiarized in 1995 and has evolved into a new phenomenon. It exemplifies how a new entrant in a field can eventually compete with the incumbent. Furthermore, many of them may replace them. The study strives to identify the critical factors underlying disruptive innovations. This study employs a systematic literature review strategy and PRISMA meta-analysis. It refers to various reputable data sources from 2013 to 2022. The study included 5,305 articles, analyzed 183 articles that encountered the criteria, and employed 45 appropriate articles. As a result, it encounters six critical factors to disruptive innovation: demand, technology readiness, regulation, labor, external technology, and capital. The proposed formula encapsulates a Success Score for assessing disruptive innovations, integrating various essential factors contributing to success.

关键词： Productivity Industrial electronics Economics Technological innovation Disruptive innovation Systematics Reviews

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Decision Theory and Risk Simulation Analysis for Optimizing Profit in PayLater Services

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Procedia computer science 2023年 219卷 60-67页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products.

关键词： PayLater decision-making Bayesian analysis fault tree analysis critical path method Monte Carlo simulation

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XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese

arXiv

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arXiv 2024年

作者： Arisaputra, Panji Handoyo, Alif Tri Zahra, Amalia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

This research paper focuses on the development and evaluation of Automatic Speech Recognition (ASR) technology using the XLS-R 300m model. The study aims to improve ASR performance in converting spoken language into written text, specifically for Indonesian, Javanese, and Sundanese languages. The paper discusses the testing procedures, datasets used, and methodology employed in training and evaluating the ASR systems. The results show that the XLS-R 300m model achieves competitive Word Error Rate (WER) measurements, with a slight compromise in performance for Javanese and Sundanese languages. The integration of a 5-gram KenLM language model significantly reduces WER and enhances ASR accuracy. The research contributes to the advancement of ASR technology by addressing linguistic diversity and improving performance across various languages. The findings provide insights into optimizing ASR accuracy and applicability for diverse linguistic contexts. © 2024, CC BY-SA.

关键词： Linguistics

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