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7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

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

ISBN: (纸本)9798350360752

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. © 2023 IEEE.

关键词： Python

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Text-based File Security with Signcryption Using AAβand DSA Algorithms 1

Text-based File Security with Signcryption Using AAβand DSA...

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1st IEEE International Conference on Cryptography, informatics, and Cybersecurity, ICoCICs 2023

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

ISBN: (纸本)9798350339437

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 AAβ 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 AAβ 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. © 2023 IEEE.

关键词： Authentication

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Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms for Securing Digital Documents 8

Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms ...

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8th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2024

作者： Hulu, Delisman Lydia, Maya Silvi Budiman, Mohammad Andri Universitas Sumatera Utara Master of Informatics Program Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

Symmetric algorithms offer speed but have weaknesses in key distribution, while asymmetric algorithms are secure but less efficient for encrypting and decrypting large text messages. This research aims to secure data and digital documents through the implementation of a hybrid cryptosystem that combines the Cramer-Shoup algorithm and Spritz. This research also analyzes and evaluates the effectiveness and speed of encryption and decryption, as well as the efficiency of the system in protecting various types of data, including docx and pdf document files. The methodology of this research includes a literature review, analysis of the encryption-decryption process, and performance testing of the system using the Python programming language. The speed test results show that this hybrid cryptosystem provides fast encryption-decryption times, with an encryption time of 0.002607 milliseconds for 400 characters and a decryption time of 0.001537 milliseconds, while for digital documents of the pdf file type, the encryption time is 0.424478 milliseconds and the decryption time is 0.404343 milliseconds. In addition, the use of the SHA-3 hash function has proven effective in maintaining data integrity. In conclusion, this hybrid cryptosystem offers an efficient and secure solution for protecting various types of digital data, including text and documents. © 2024 IEEE.

关键词： Cramer-Rao bounds

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E-Portofolio in Improving Critical Thinking and Self-Management through Lesson Study: A Study on Writing Pedagogy in Higher Education 10

E-Portofolio in Improving Critical Thinking and Self-Managem...

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10th International Conference on Education and Technology, ICET 2024

作者： Fawaid, Achmad Handayani, Putri Abdillah, Yahya Aulia Islamic Education Universitas Nurul Jadid Probolinggo Indonesia Master of Informatics Distance Program Universitas Amikom Yogyakarta Indonesia

ISBN: (纸本)9798331522483

The purpose of this study was to investigate the effectiveness of integrating e-portfolios with the Lesson Study approach in enhancing critical thinking and self-management skills among higher education students in writing pedagogy. The research was conducted in a quasi-experimental design, involving two classes with a total of 53 students. One class employed traditional portfolios, while the other used e-portfolios, both within the context of Lesson Study cycles. Data were collected through pretests and posttests assessing critical thinking and self-management skills, supported by qualitative observations from the Lesson Study. The results indicated that the use of e-portfolios, when combined with Lesson Study, significantly improved both critical thinking and self-management skills compared to traditional portfolios. Based on the ANACOVA test results, F-value of critical thinking and self-management skill is 9.841 and 10.84 with significance level of 0.002. It indicates that there is a statistically significant effect of the method (e-portfolio vs. traditional portfolio) on critical thinking and self-management skill after controlling for the pretest scores. Even though Lesson Study based e-portfolio has many advantages, this study still has limitations, namely its short duration and single institution, it still provides opportunities for future research on a wider scale. © 2024 IEEE.

关键词： Systems thinking

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study 3

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

作者： Muharram, Arief Purnama Taufikulhakim, Farhan Hilmi Purwarianti, Ayu Universitas Indonesia Faculty of Medicine Jakarta Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Master Program of Informatics Bandung Indonesia

ISBN: (纸本)9798350302424

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development. © 2023 IEEE.

关键词： COVID-19

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Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naïve Bayes Classifier

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Journal of Computing Science and Engineering 2022年第3期16卷 153-164页

作者： Yudhana, Anton Umar, Rusydi Saputra, Sabarudin Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Master Program of Informatics Universitas Ahmad Dahlan Yogyakarta Indonesia

Fish is one of the food sources that should be examined for freshness before being consumed. The consumption of rotten fish can cause various diseases. The rotten fish have changed color on the gills, skin, flesh, and eyes and have a pungent odour. Fish freshness can be assessed using a variety of conventional methods, but these methods have limitations, such as requiring relatively expensive equipment, trained personnel and being destructive. The machine learning method is used because it is non-destructive, reduces costs, and is easy to use. This study aims to identify the freshness of fish using k-nearest neighbor (k-NN) and Naïve Bayes (NB) classification methods based on the fish-eye image. The features used in the classification process are RGB and GLCM. The research stages consist of the fish collection process, image acquisition and class division, preprocessing and ROI detection, feature extraction and dataset split, and the classification process. Based on these results, it can be stated that the k-NN method has better performance than NB with average accuracy, precision, recall, specificity, and AUC of 0.97, 0.97, 0.97, 0.97, and 0.97. © 2022. The Korean Institute of Information Scientists and Engineers

关键词： Fish

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ECLAT – Based Analysis Of Heart Disease Symptom Patterns

ECLAT – Based Analysis Of Heart Disease Symptom Patterns

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Putri Athirah Thaibur Syahril Efendi Maya Silvi Lydia Master of Informatics Program Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Heart disease remains a leading global health concern, with millions of people affected worldwide. The World Health Organization has highlighted the alarming increase in heart disease, even among non-smokers. Data mining techniques, such as association rule mining, offer a promising approach to identify patterns and relationships within large datasets. In this study, the authors propose using the Eclat algorithm to efficiently discover frequent itemsets and association rules in heart disease data. By analyzing these patterns, we can gain valuable insights into the factors contributing to heart disease. The results of this analysis suggest that men are at a higher risk of heart disease compared to women, and high cholesterol levels and chest pain are common symptoms associated with the condition. This research demonstrates the potential of data mining techniques to uncover hidden patterns and improve our understanding of heart disease. By identifying key risk factors and symptoms, we can develop more effective prevention and treatment strategies.

关键词： Heart Seminars Pain Itemsets Prevention and mitigation Organizations Association rule learning Intelligent systems Information technology Diseases

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Performance Analysis of GloVe Word Embedding for Batak – English Translation

Performance Analysis of GloVe Word Embedding for Batak – En...

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International Conference on informatics and Computing (ICIC)

作者： Andika Syahputra Erna Budhiarti Nababan Herman Mawengkang Master of Informatics Program Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This paper presents a performance analysis of GloVe word embeddings in Batak language – English, utilizing Long Short-Term Memory (LSTM) models. The Batak language, with its rich language features and relatively low resource availability, poses significant challenges for machine translation tasks. By integrating GloVe embeddings into LSTM networks, we aim to enhance the quality and accuracy of translation outputs. The study compares the effectiveness of GloVe embeddings against Word2Vec embeddings. Our experiments involve training and evaluating LSTM models with both embedding techniques, followed by an analysis of translation performance metrics. The result demonstrate the relative strengths and weakness of each embedding approach, providing valuable insight into their suitability for low-resource language translation tasks.

关键词： Training Analytical models Translation Computational modeling Predictive models Performance analysis Machine translation informatics Long short term memory Context modeling

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory 10

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Sugiarto, Dedy Utami, Ema Yaqin, Ainul Raharjo, Suwanto Universitas Trisakti Faculty of Industrial Technology DKI Jakarta Indonesia Universitas Amikom Yogyakarta Master Program of Informatics Engineering Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350329919

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as 'mafia', 'mahal', 'langka', 'tangkap', 'bongkar', and 'mendag' could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions. © 2023 IEEE.

关键词： Sentiment analysis

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Robot and Human Interaction using Natural Language Understanding in Indonesian based on Mental Image Based Understanding 11

Robot and Human Interaction using Natural Language Understan...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Abrari, Muhammad Giriarda Hindersah, Hilwadi Rusmin, Pranoto Hidaya Bandung Institute of Technology Master Program in Electrical Engineering Bandung Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350321982

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans. © 2023 IEEE.

关键词： Semantics

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