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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Purnama Muharram, Arief Puteri Haryono, Hollyana Haji Juma, Abassi Puspasari, Ira Priya Utama, Nugraha School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Computer Engineering Universitas Dinamika Surabaya Indonesia

ISBN: (纸本)9798350381382

Reading and interpreting chest X-ray images is one of the most radiologist's routines. However, it still can be challenging, even for the most experienced ones. Therefore, we proposed a multi-modal deep learning-based automated chest X-ray report generator system designed to assist radiologists in their work. The basic idea of the proposed system is by utilizing multi binary-classification models for detecting multi abnormalities, with each model responsible for detecting one abnormality, in a single image. In this study, we limited the radiology abnormalities detection to only cardiomegaly, lung effusion, and consolidation. The system generates a radiology report by performing the following three steps: image preprocessing, utilizing deep learning models to detect abnormalities, and producing a report. The aim of the image pre-processing step is to standardize the input by scaling it to 128x128 pixels and slicing it into three segments, which covers the upper, lower, and middle parts of the lung. After pre-processing, each corresponding model classifies the image, resulting in a 0 (zero) for no abnormality detected and a 1 (one) for the presence of an abnormality. The prediction outputs of each model are then concatenated to form a 'result code'. The 'result code' is used to construct a report by selecting the appropriate pre-determined sentence for each detected abnormality in the report generation step. The proposed system is expected to reduce the workload of radiologists and increase the accuracy of chest X-ray diagnosis. copy;2023 IEEE. © 2023 IEEE.

关键词： Report generators

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Sentiment Analysis of Israeli-Palestinian Conflict on Indonesian Tweets Using Machine Learning 4

Sentiment Analysis of Israeli-Palestinian Conflict on Indone...

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Rahardi, Majid Arsya, Muhammad Rezzha Riamrizal Aminuddin, Afrig Abdulloh, Ferian Fauzi Miah, Mohammad Badrul Alam Pratikno, Heri Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia Mawlana Bhashani Science and Technology University Faculty of Engineering Tangail Bangladesh Universitas Dinamika Faculty of Technology and Informatics Surabaya Indonesia

ISBN: (纸本)9798350368253

The social media of X has become a focal point for discussions on sensitive issues, including the Israeli-Palestinian conflict. This research analyzes sentiment toward tweets expressing support for Palestine on X, comparing the Support Vector Machine (SVM) and Naïve Bayes methods. Given the global attention to the conflict, sentiment analysis on this topic is crucial. The results reveal that the SVM model outperforms Naïve Bayes, achieving accuracy, precision, recall, and F1-score of 85%, 87%, 85%, and 85% for Naïve Bayes, and 91%, 92%, 90%, and 90% for SVM, respectively. It demonstrates the SVM model's capability to distinguish between positive and negative sentiments. Comparison with previous research indicates that this study surpasses the sentiment analysis results of previous SVM models, while Naïve Bayes excels in specific research. With superior performance, this research provides a new contribution to recognizing and classifying sentiments on a particular dataset related to the Israeli-Palestinian conflict on X social media. © 2024 IEEE.

关键词： Tweets

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Automated Chest X-Ray Report Generator Using Multi-Model Deep Learning Approach

arXiv

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arXiv 2023年

作者： Muharram, Arief Purnama Haryono, Hollyana Puteri Juma, Abassi Haji Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Computer Engineering Universitas Dinamika Surabaya Indonesia

Reading and interpreting chest X-ray images is one of the most radiologist’s routines. However, it still can be challenging, even for the most experienced ones. Therefore, we proposed a multi-model deep learning-based automated chest X-ray report generator system designed to assist radiologists in their work. The basic idea of the proposed system is by utilizing multi binary-classification models for detecting multi abnormalities, with each model responsible for detecting one abnormality, in a single image. In this study, we limited the radiology abnormalities detection to only cardiomegaly, lung effusion, and consolidation. The system generates a radiology report by performing the following three steps: image pre-processing, utilizing deep learning models to detect abnormalities, and producing a report. The aim of the image pre-processing step is to standardize the input by scaling it to 128x128 pixels and slicing it into three segments, which covers the upper, lower, and middle parts of the lung. After pre-processing, each corresponding model classifies the image, resulting in a 0 (zero) for no abnormality detected and a 1 (one) for the presence of an abnormality. The prediction outputs of each model are then concatenated to form a’result code’. The’result code’ is used to construct a report by selecting the appropriate pre-determined sentence for each detected abnormality in the report generation step. The proposed system is expected to reduce the workload of radiologists and increase the accuracy of chest X-ray diagnosis. © 2023, CC BY-SA.

关键词： Report generators

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Automated Chest X-Ray Report Generator Using Multi-Model Deep Learning Approach

Automated Chest X-Ray Report Generator Using Multi-Model Dee...

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International Conference on Data and Software engineering (ICODSE)

作者： Arief Purnama Muharram Hollyana Puteri Haryono Abassi Haji Juma Ira Puspasari Nugraha Priya Utama School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Computer Engineering Universitas Dinamika Surabaya Indonesia

Reading and interpreting chest X-ray images is one of the most radiologist's routines. However, it still can be challenging, even for the most experienced ones. Therefore, we proposed a multi-modal deep learning-based automated chest X-ray report generator system designed to assist radiologists in their work. The basic idea of the proposed system is by utilizing multi binary-classification models for detecting multi abnormalities, with each model responsible for detecting one abnormality, in a single image. In this study, we limited the radiology abnormalities detection to only cardiomegaly, lung effusion, and consolidation. The system generates a radiology report by performing the following three steps: image preprocessing, utilizing deep learning models to detect abnormalities, and producing a report. The aim of the image pre-processing step is to standardize the input by scaling it to 128x128 pixels and slicing it into three segments, which covers the upper, lower, and middle parts of the lung. After pre-processing, each corresponding model classifies the image, resulting in a 0 (zero) for no abnormality detected and a 1 (one) for the presence of an abnormality. The prediction outputs of each model are then concatenated to form a ‘result code‘. The ‘result code’ is used to construct a report by selecting the appropriate pre-determined sentence for each detected abnormality in the report generation step. The proposed system is expected to reduce the workload of radiologists and increase the accuracy of chest X-ray diagnosis.

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Sentiment Analysis of Israeli-Palestinian Conflict on Indonesian Tweets Using Machine Learning

Sentiment Analysis of Israeli-Palestinian Conflict on Indone...

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International Conference on Electrical engineering and computer Science (ICECOS)

作者： Majid Rahardi Muhammad Rezzha Riamrizal Arsya Afrig Aminuddin Ferian Fauzi Abdulloh Mohammad Badrul Alam Miah Heri Pratikno Faculty of Computer Science Universitas Amikom Yogyakarta Sleman Indonesia Faculty of Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh Faculty of Technology and Informatics Universitas Dinamika Surabaya Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

关键词： Support vector machines Electrical engineering Sentiment analysis Analytical models Accuracy Social networking (online) Machine learning Vectors Bayes methods Reliability

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Feature Selection using Chi Square to Improve Attack Detection Classification in IoT Network: Work in Progress 9

Feature Selection using Chi Square to Improve Attack Detecti...

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9th International Conference on Electrical engineering, computer Science and Informatics, EECSI 2022

作者： Saputra Elsi, Zulhipni Reno Stiawan, Deris Oklilas, Ahmad Fali Susanto Kurniabudi Kunang, Yesi Novaria Idris, Mohd. Yazid Budiarto, Rahmat Universitas Muhammadiyah Palembang Universitas Sriwijaya Faculty of Engineering Faculty of Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Computer Engineering Department Palembang Indonesia Universitas Bina Insan/Universitas Sriwijaya Faculty of Computer Faculty Engineering Lubuklinggau Indonesia Universitas Dinamika Bangsa Computer Engineering Department Jambi Indonesia Universitas Bina Darma Faculty of Computer Science Palembang Indonesia School of Computing Universiti Teknologi Malaysia Faculty of Engineering Johor Malaysia College of Computer Science and IT AlBaha University Albaha Saudi Arabia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

To maintain network security, Intrusion Detection System (IDS) is needed to detect anomaly and attack. Designing proper IDS requires accurate model. This paper proposes a model, which consists of statistical extraction, feature selection, dataset clustering, classification, and performance measurement. Experiments on MQTT-IOT-IDS2020 dataset which contains Normal, scan A, scans U, Sparta and mqttbruteforce are conducted. The dataset is statistically extracted using Bidirectional-based features packet header feature with 37 features. Chi square algorithm is selected for performing feature extraction process. 10 relevant and best features are selected and ranked into 5-subsets and 10-subset feature. Three dataset splitting into testing data and training data of 90%:10%, 70%:30% and 50%:50% are created. Binary classification using k-Nearest Neighbor (KNN) and Adaboost algorithms are performed. The experimental results show accuracy level above 99% for all scenarios, with Adaboost algorithm outperforms k-Nearest Neighbor algorithm. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： Adaboost Binary Classification Chi Square Intrusion Detection System k-Nearest Neighbor

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RSSI Data Preparation for Machine Learning 2

RSSI Data Preparation for Machine Learning

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2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020

作者： Abidin, Dodo Zaenal Nurmaini, Siti Firsandava Malik, Reza Erwin Rasywir, Errissya Pratama, Yovi Universitas Sriwijaya Palembang Lecturer of Computer Faculty of Universitas Dinamika Bangsa Jambi Indonesia Leader of Intelligence System Research Group Universitas Sriwijaya Palembang Indonesia Leader Team of Communication Networks Research Universitas Sriwijaya Palembang Indonesia Informatics Engineering Departement of Sriwijaya University Jambi Indonesia Informatics Engineering Computer Departement of Dinamika Bangsa University Jambi Indonesia

ISBN: (纸本)9781728191676

In this study, we prepared unstructured raw RSSI (Received Signal Strength Indication) data to prepare it for use in machine learning predictive modeling. RSSI data preparation is very important in predictive modeling machine learning projects, such as classification and regression, so that the algorithm can model it based on the right conditions, because the data has a more specific part but does not necessarily affect the algorithm performance. RSSI data were collected from the dinamika Bangsa University Campus building, namely 225 reference points. The two main processes involved in data compilation, namely construction data and data cleaning. From the process of identifying a column containing a single RSSI value, several unique values were found in each column in the RSSI data table. Since no column has a Single Value, no columns are deleted. Then in the process of deleting columns that have low RSSI variance, the input is 5969 rows and 18 columns. The output is 5969 rows and 3 columns. However, the number of columns that passed the Low Variance selection was 5969 rows and 18 columns, meaning that all columns in the dataset have high variance. Additionally, no duplicate RSSI data were found. It can be said that the result of the overall preparation of this data set is the RSSI data used in good condition and can be used for machine learning needs. © 2020 IEEE.

关键词： Machine learning

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Image Segmentation of Women’s Salivary Ferning Patterns Using Harmony Frangi Filter 1

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Pratikno, Heri Ibrahim, Mohd Zamri Faculty of Technology and Information Department of Computer Engineering Universitas Dinamika Surabaya Indonesia Faculty of Electrical and Electronic Engineering Universiti Malaysia Pahang Kuantan Malaysia

ISBN: (数字)9789811523175

ISBN: (纸本)9789811523168

Medical research proves that entering the fertile period, especially during ovulation, all-female body fluids contain ferning patterns in the form of crystallization of salt shaped like a fern tree. Until now, not many research topics have been carried out related to the segmentation process in the salivary ferning pattern, this is due to several problems including first, the unavailability of a database of image salivary ferning pattern online. Second, the salivary ferning pattern has several hidden layers and uneven intensity. The purpose of this study was to detect and determine the line shape of the salivary ferning crystal pattern using the Harmony Frangi Filter method based on the Hessian matrix operation. The results of the segmentation process from this study are a crucial basis in determining the level of accuracy and precision at the next stage of research, namely: the prediction process of a woman’s ovulation in each menstrual cycle. The measurement of segmentation results has an average value of MSE 2.25, PSNR 44.86 dB, FSIM 0.954, accuracy 99.88%, sensitivity 99.98% and specificity 99.88%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Body fluids

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Factors influencing SaaS adoption by MSMEs 7

Factors influencing SaaS adoption by MSMEs

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7th International Conference on Software engineering and computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Ayuningtyas, Ayuningtyas Azmi, Zafril Rizal M Abdullah Arshah, Ruzaini Ahmad, Muhammad Aliif Khubrani, Mousa Faculty of Technology and Informatics Universitas Dinamika Surabaya East Java Indonesia Faculty of Computing Universiti Malaysia Pahang Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Malaysia Faculty of Computer Science Information Technology Jazan University Saudi Arabia

ISBN: (纸本)9781665414074

Cloud computing has been around for quite long, yet some organizations have never used it, especially MSMEs in Indonesia. Several factors hinder its implementation by MSMEs, including unequal infrastructure for using the internet in Indonesia. This paper assesses the literature on cloud computing, SaaS, e-Commerce, and e-wallet to determine the initial factors that affect technology implementation. The results showed that the frameworks widely used to obtain and assess variables include the DOI, TAM, and TOE. Furthermore, compatibility and the pressure from competitors to adopt technology are the highly considered variables. Among the factors, perceived usefulness and ease of use is also deemed by MSMEs in implementing e-commerce and e-wallet. © 2021 IEEE.

关键词： Software as a service (SaaS)

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Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm

Improving the Anomaly Detection by Combining PSO Search Meth...

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International Conference on Electrical engineering, computer Science and Informatics (EECSI)

作者： Kurniabudi Abdul Harris Albertus Edward Mintaria Darmawijoyo Deris Stiawan Mohd Yazid bin Idris Rahmat Budiarto Universitas Dinamika Bangsa & Faculty of Engineering Universitas Sriwijaya Indonesia line 3: Faculty of Computer Sciences Universitas Dinamika Bangsa Indonesia Faculty of Computer Sciences Universitas Dinamika Bangsa Indonesia Faculty of Mathematics and Natural Sciences Universitas Sriwijaya Indonesia Faculty of Computer Sciences Universitas Sriwijaya Indonesia Universiti Teknologi Malaysia Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (数字)9786020737621

ISBN: (纸本)9781728188461

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.

关键词： Search methods Feature extraction Classification algorithms Security Particle swarm optimization Anomaly detection Testing

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