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检索条件"机构=Dept. of Informatics and Computer Engineering Education"

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Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models 6

Indonesian Hate Speech and Abusive Tweets Classification wit...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Yulianto Darmawan, Bagus Tri Irnawan, Bassamtiano Renaufalgi Suzuki, Yoshimi Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350345162

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa;second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy. © 2023 IEEE.

关键词： Abusive CNN Deep Learning Hate Speech IndoBERT IndoBERTweet Indonesian RoBERTa Indonesian Twitter

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Design and Implementation of Cloud Computing Using the NDLC Method Combined with Tunnel Link Split

Design and Implementation of Cloud Computing Using the NDLC ...

引用

2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Wibowo, Iqbal Tri Kurniawan, Asep Noviandri Sulaiman, Nur Fauzi Oktivasari, Prihatin Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350359633

Today's education system increasingly incorporates information technology, such as the use of computers, the internet, and mobile devices. Utilization of this information technology can facilitate the teaching and learning process and increase the effectiveness, efficiency and productivity of users, both teachers, students and school administrators. However, given the growing number of users and the amount of data processed, school infrastructure often cannot meet these needs. The system development method will use the NDLC (Network Development Life Cycle) method which will be combined with the Tunnel Link Split to become a system that can be used remotely (remoting). The results of this research are that the results of designing and implementing cloud computing with the NDLC method combined with tunneling can be successful and used. Implementation is also carried out with additional monitoring using observium software and for access to the cloud using additional software, namely owncloud by obtaining an extended processor observium value of 99.90% (idle), detailed observium memory 69% (% of ram), swap storage observium 218.48kb/ s, observium ping response 86.4m/s, and observium traffic port wlo15.63m. © 2023 IEEE.

关键词： Life cycle

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Analysis of *** Email Domain Security Status Using DMARC (Domain-Based Message Authentication, Reporting, and Conformance) 6

Analysis of *** Email Domain Security Status Using DMARC (Do...

引用

6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Arnaldy, Defiana Theyser, Regina Patricia Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350345162

The rapid development of technology not only provides benefits that facilitate all human activities. However, it also encourages more security loopholes due to a person's lack of awareness in maintaining data security. Phishing emails are one of the fastest growing security crimes and harm both businesses and individuals. This research aims to implement DMARC (Domain-based Message Authentication, Reporting, and Conformance) as an email authentication method that can monitor the security status of all types of messages that enter *** email domains. This research uses email phishing and spoofing as penetration testing techniques to analyze how good that email security method is. DMARC is implemented in three types of scenarios (none, quarantine, and reject) which manage to get different delivery rates and the success rate of DMARC work performance reaches 90% in monitoring and managing all email messages that go to the *** email domain. In the situation without policy enforcement, the DMARC system effectively observed a 100.0% external delivery rate during the phishing email delivery test. In the scenario where a quarantine policy was implemented, the DMARC system successfully monitored the external sending of phishing emails at a rate of 88.5%. Similarly, in the case of the reject policy scenario, DMARC effectively oversaw incoming phishing emails with an external delivery rate of 97.7%. © 2023 IEEE.

关键词： Apilogy.id DMARC Email Security Phishing Spoofing

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Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms 1

Breast Cancer Prediction Using Random Forest and Gaussian Na...

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1st International Conference on Information System and Information Technology, ICISIT 2022

作者： Sulistiani, Ita Wulandari, Windu Astuti, Fathia Dwi Widodo State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Breast cancer is the second deadliest cancer after lung cancer. In 2021, ASCO-American Society of Clinical Oncology states that female invasive breast cancer increased by half a percent from 2008 to 2017. Breast cancer is induced by a misspelling of a cell, which causes the cell to become uncontrollable. If the problem is not treated soon within a few months, a large number of cells containing the wrong instructions can be detected as cancer. Machine learning has been widely used for developing breast cancer prediction models. Unfortunately, the problem of imbalanced datasets tends to have little to no attention in previous research using machine learning. This research aimed to develop breast cancer prediction models using Random Forest and Gaussian Naïve Bayes Classifier. Borderline Synthetic Minority Oversampling Technique (BSM) is applied to handle the imbalanced dataset problem;meanwhile, machine learning algorithms such as Random Forest and Gaussian Naïve Bayes algorithms were used to build the prediction models. Using UCI Machine Learning Wisconsin Breast Cancer Dataset (WBCD), the combination of BSM and Random Forest algorithm showed the highest recall score, approximately around 99.8%. Meanwhile, the BSM and Gaussian Naïve Bayes Classifier combination provided the lowest recall score among generated models, 78.2%. © 2022 IEEE.

关键词： Decision trees

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Comparison of Bankruptcy Prediction Models Using Support Vector Machine and Artificial Neural Network 11

Comparison of Bankruptcy Prediction Models Using Support Vec...

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11th Electrical Power, Electronics, Communications, Control, and informatics Seminar, EECCIS 2022

作者： Sulistiani, Ita Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

In making financial decisions, many critical factors need to be considered, including the bankruptcy potential of the company. As a result, several bankruptcy prediction models, mainly using machine learning approaches, have been created. Based on relevant research, Support Vector Machine (SVM) and Artificial Neural Network (ANN) often provide the best performance in predicting companies' bankruptcy. On the other hand, both algorithms still have drawbacks, such as SVM is quite sensitive to feature selection, and ANN is prone to overfitting. In addition, many studies ignored the imbalance problems in the bankruptcy dataset, causing the prediction models to be prone to bias. Therefore, this research aimed to compare the performance of the SVM and ANN models in predicting companies' bankruptcy. To overcome the problem of overfitting and feature selection, we utilized the Principle Component Analysis (PCA) dimensionality reduction technique. In addition, for handling the imbalanced dataset problem, we used the Borderline Synthetic Minority Oversampling Technique (BSM). The data used in this study were gained from the annual financial statements of bankrupt and non-bankrupt public companies in Indonesia during the 2010 to 2019 years period. Based on the research, the ANN-BSM-PCA combination model with {PC =16} provided the best accuracy, precision, and recall scores of 0.96, 0.934, and 0.988, respectively. Therefore, the ANN-BSM-PCA with {PC =16} was the best model in the case of predicting companies' bankruptcy. © 2022 IEEE.

关键词： Support vector machines

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9 with Artificial Intelligence for Cancer Treatment 5

AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Kumar, D. Nitesh Chowdhary, D. Lavanya Pathuri, Trinadh Katta, Priyanka Arya, Leena Dept. of Computer Science & Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (纸本)9798350361155

The CRISPR-Cas9 genome editing system is a new technology that allows the modification of secret vital material using disruptions insertions or amendments of DNA sequence. It is an easy, widely accepted, and correct representation of gene modification. The potential for treating different genetic diseases is enormous. This paper discusses the latest approach to CRISPR-Cas9 integration with AI, aimed at improving efficacy, as well as the safety of cancer treatment. This paper highlights the cutting-edge integration of CRISPRCas9 with Artificial Intelligence (AI) to enhance the efficacy and safety of cancer treatment. Artificial intelligence is a simulation of human intelligence for computers, indicates the latest developments in the field, and mentions clinical trials' ability to help further the treatment of cancer and overcome current and upcoming challenges. Finally, the application of (AI) in the modification of the CRISPR/Cas9 system is highlighted. Taking case scenarios and futuristic approaches, this paper reveals excellent implementation initiatives by learning from the Clinical revolution together with human technology and aimed at a future in which new technologies improve patient outcomes without sacrificing ethics or putting patients first. © 2024 IEEE.

关键词： Diseases

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Lidar-based Human Detection for a Mobile Robot in Social Environment using Deep Learning 25

Lidar-based Human Detection for a Mobile Robot in Social Env...

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25th International Electronics Symposium, IES 2023

作者： Ramadhan, Alif Wicaksana Dewantara, Bima Sena Bayu Setiawardhana Akzada, Fursan Departmen of Electrical Engineering Surabaya Indonesia Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Nowadays, robotics implementation spans industries such as manufacturing, healthcare, and logistics, integrating AI-driven capabilities to enhance efficiency and collaboration in diverse operational environments. The ability to respond and interact while considering human presence is crucial for a mobile robot to operate effectively and be accepted in a human social environment. It is necessary to make a robot that can detect the presence of humans around it to make it capable of navigating while considering the social environment. Meanwhile, considering that the lidar sensor is almost always available in many robot navigation cases, it becomes an accessible tool for detecting the presence of humans in its surroundings. We brought the one-dimensional lidar data into two-dimensional image data, then involved some object selection based on weak characteristics, such as the acceptable range radius of the object and the acceptable distance between two legs. The result of the process is a group of human candidates. We use a deep learning model with a pretrained mobilenetv3 model as the feature extractor to ensure whether the candidates are humans and a fully connected layer of neural networks as the classifier. We defined several scenarios in an indoor environment to test the performance of the proposed algorithm. The experiment results show the system's performance with a precision score of 0.902, a recall score of 0.782, and an F1 score of 0.837. © 2023 IEEE.

关键词： Mobile robots

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Micro-Mobility in Indonesia: Examining the Legal and Policy Implications of Emerging Transportation Technologies 9

Micro-Mobility in Indonesia: Examining the Legal and Policy ...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Suci Meinarni, Ni Putu Vandika, Arnes Yuli Misnawati Institut Bisnis Dan Teknologi Indonesia Dept. Informatics Engineering Denpasar Indonesia Universitas Bandar Lampung Dept. Computer Science Bandarlampung Indonesia Universitas Palangka Raya Dept. Indonesian Language and Literature Education Palangka Raya Indonesia

ISBN: (纸本)9798350383010

This study delves into micro-mobility in Indonesia, focusing on legal, safety, and policy aspects of electric scooters, bicycles, and similar devices. The absence of specific regulations and safety concerns pose challenges, compounded by infrastructure deficiencies and public perception complexities. A literature review provides insights, identifying gaps and guiding research goals. Legal analysis reveals a regulatory void, emphasizing the need for clear guidelines and dedicated safety measures, amid infrastructure challenges and public perception issues. The study underscores the importance of aligning urban planning, efficient enforcement, and public engagement for sustainable micro-mobility. A collaborative and comprehensive framework, incorporating clear regulations, safety measures, infrastructure development, efficient enforcement, and positive public perception, is essential for the successful integration of micro-mobility in Indonesia's urban landscape. Strategic and informed approaches are crucial as Indonesia leads the way in embracing these innovative transportation technologies. © 2024 IEEE.

关键词： Urban transportation

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