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TechRxiv 2024年

作者： Muharram, Arief Purnama Sajid, Fahmi Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies. © 2024, CC BY.

关键词： Self-supervised learning

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset) 4

Finding the Best Techniques for Predicting Term Deposit Subs...

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4th International Conference on Sustainable Engineering and Creative Computing, ICSEC 2022

作者： Setiyani, Lila Indahsari, Ayu Rosalina Wansen, Tjong President University Master of Science Information Technology Study Program Bekasi Indonesia President University Informatics Study Program Bekasi Indonesia

ISBN: (纸本)9798350322002

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%). © 2022 IEEE.

关键词： Forecasting

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Digital File Signcryption using Benaloh and ElGamal Signature Scheme

Digital File Signcryption using Benaloh and ElGamal Signatur...

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Software Engineering and Information Technology (ICoSEIT), International Conference on

作者： Nahdah Salsabiil Damanik Mohammad Andri Budiman Muhammad Zarlis Master of Informatics Program University of Sumatera Utara Medan Indonesia Master of Infromation Systems Bina Nusantara University Jakarta Indonesia

The rapid development of technology makes it easier for everyone to send files digitally, so there are also many cases of forgery and duplication of files in the general public, for example is on e-voting process. To prevent this from happening, it can be done by applying a cryptographic system in the data transmission process that occurs, one of which is by doing signcryption. E-voting itself focuses on the aspects of authentication, data integrity, and confidentiality. Therefore, research was carried out on the Benaloh algorithm and the ElGamal Signature Scheme in the signcryption method which is expected to overcome the problems mentioned earlier and also can provide integrity, confidentiality and authentication of data transmission which required in the e-voting process. Benaloh and ElGamal Signature Scheme also using the modular exponential method, which produces better performance compared to the RSA factorization. The final result obtained from this research is the successful signcryption process with the results of the execution time signcryption of files of 6 KB d is 8.694 seconds.

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Hybrid Cryptosystems MRC-4 Algorithm and RSA Dual Modulus to Secure Data

Hybrid Cryptosystems MRC-4 Algorithm and RSA Dual Modulus to...

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International Conference of Computer Science and Information Technology

作者： Benny Hutagaol Mohammad Andri Budiman Herman Mawengkang Master of Informatics Program Universitas Sumatera Utara Medan Indonesia

Cryptography is very important when we talk about the security of data. Cryptography is a science that studies techniques, both mathematical and intuitive, related to aspects of data security. Along with the development of cryptography, technological developments are also developing very rapidly. There is a lot of data theft and lack of efficiency in maintaining data security, in this case it is very important to maintain the security of data and information. This research is designed to provide a solution to this problem by using Hybrid cryptography. Hybrid in this research combines the MRC4 and RSA Dual Modulus algorithms. The MRC4 algorithm is a stream cipher algorithm whose system security depends on generating a stream of key bits (keystream), while the RSA Dual Modulus algorithm is an asymmetric algorithm variant of RSA whose security lies in the difficulty of factoring large numbers into prime factors but this algorithm takes a long time in the encryption process when compared to the MRC4 algorithm. In the research process, the MRC4 algorithm is used to encrypt the sender's message and the RSA Dual Modulus algorithm will encrypt the key from MRC4, then the results of the message and key encryption process will be sent to the message recipient. After the recipient gets the plaintext and key, the key is first decrypted using the RSA Dual Modulus algorithm to produce a key that can decrypt the message using the MRC4 algorithm.

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Text-based File Security with Signcryption Using AAβ and DSA Algorithms

Text-based File Security with Signcryption Using AAβ and DS...

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

作者： Lathifah Tsamratul Ain Mohammad Andri Budiman Maya Silvi Lydia Master of Informatics Program University of Sumatera Utara Medan Indonesia

The swift development of the current digital era is directly proportional to the emergence of several security issues in digital data transmission, especially in data confidentiality and authentication. The modernisation of this way of communication method must be given more attention to the security system because of its increasing popularity. In this case, users can overcome it by using various encryption method approaches, one of many ways that can be done is signcryption. Signcryption is a scheme that combines encryption and digital signatures. Where the encryption process provides confidentiality while the signature offers authenticity. Signcryption itself is utilized in providing solutions to the security problems mentioned earlier by combining a relatively new public key algorithm called $AA_{\beta}$ with DSA, which is the standard in digital signatures, in the hope that this combination of algorithms could provide integrity, confidentiality, and authentication. The final result of this research is that signcryption with $AA_{\beta}$ and DSA algorithms has been successfully carried out by producing an execution time of 1.189 seconds on the signcryption process of the smallest file size of 1 KB and resistant to brute force attacks.

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Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis

arXiv

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arXiv 2023年

作者： Putra, Danendra Athallariq Harya Muharram, Arief Purnama Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling;TensorFlow for deep learning handling;and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard. © 2023, CC BY.

关键词： Sentiment analysis

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Use of an Expert System to Diagnose and Provide Solutions for Pests and Diseases in Hydroponic Mustard Plants Using Certainty Factor and Forward Chaining Methods 4

Use of an Expert System to Diagnose and Provide Solutions fo...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Baihaqi, Mahesa Bayu Widodo, Aris Puji Jie, Ferry Diponegoro University Master of Information System Study Program Central Java Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Central Java Indonesia Edith Cowan University School of Business and Law Australia

ISBN: (纸本)9798350376111

Mustard greens are a vegetable that is very popular with people, especially hydroponic mustard greens, which appear fresher. However, like other vegetables, mustard greens grown hydroponically are also susceptible to pests and diseases, which can cause the vegetables to lose their freshness and may even result in crop failure. This condition can cause losses for mustard greens farmers in running their businesses. This research proposes an information system that represents the expertise of hydroponic mustard greens farmers in diagnosing pests and diseases in mustard plants as well as solutions to overcome them. This is to make it easier for novice growers and anyone interested in learning about diseases or pests that impact hydroponic mustard greens. The expert system developed is web-based and functions as a user consultation medium in relation to mustard pests and diseases using the forward chaining and certainy factor method to explain the decision tree procedure for mustard plant disease symptoms. With this expert system, farmers do not need to look for people who can explain the problems they face. We found that there were 10 types of pests and diseases and 29 symptoms in the hydroponic mustard plants reviewed in this research. © 2024 IEEE.

关键词： Expert systems

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Supervised Machine Learning Approach for Predicting Cardiova...

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International Conference on Electrical Engineering and informatics, ICEEI

作者： Arief Purnama Muharram Fahmi Sajid Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XG- Boost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F -1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies.

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Information Security Challenges in the Digitalisation of the Austrian Food Industry: Assessment of Food Suppliers and Implications 29

Information Security Challenges in the Digitalisation of the...

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29th International Conference on Engineering, Technology, and Innovation, ICE 2023

作者： Hetzenauer, Claudia Dobler, Martin Simma, Armin Vorarlberg University of Applied Sciences Master Program Business Administration: Bpm Dornbirn Austria Vorarlberg University of Applied Sciences Research Centre Business Informatics Dornbirn Austria Vorarlberg University of Applied Sciences Competence Area Informatics Dornbirn Austria

ISBN: (纸本)9798350315172

In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper. (Abstract) © 2023 IEEE.

关键词： Supply chain management

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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model 8

Exploring the Impact of Word Embedding Dimensions on Depress...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Lestandy, Merinda Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach. © 2023 The Authors. Published by Elsevier B.V.

关键词： BiLSTM Depression Social Media Word Embedding

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