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6th International Conference on Informatics and Computing, ICIC 2021

作者： Prayitno, Ragiel Hadi Sudiro, Sunny Arief Madenda, Sarifuddin Gunadarma University Doctoral Program in Information Technology Jakarta Indonesia STMIK Jakarta STIK Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9781665421553

This article describes the AES encryption and decryption process without using lookup tables in the MixColumns transformation. The encryption process consists of transforming subbytes, shiftrows, mixcolumns and addroundkey. The process was carried out for 10 rounds, but in round 10 the mixcolumns transformation was not carried out. The decryption process consists of inverse mixcolumns, inverse shiftrows, inverse subbytes and addroundkey. In this study, the AES encryption and decryption process was carried out using two methods, namely based on the lookup table and without using the lookup table on the MC/IMC transformation. The method in this article is applied to Matlab software. The experimental results show that the encryption and decryption process using a lookup table is slower than the method without a lookup table. The encryption process without a lookup table on the MC transformation takes 0.091 seconds while using a lookup table takes 0.399 seconds. The decryption process without a lookup table on the IMC transformation takes 0.149 seconds while using a lookup table takes 0.206 seconds. © 2021 IEEE.

关键词： Cryptography

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Navigating Consumer Skepticism: Conceptual Model of Distrust Detection and Recognition technology Adoption Among Online Shop Owners in Indonesia

Navigating Consumer Skepticism: Conceptual Model of Distrust...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Muhamad Rachmat Gunawan Febriliyan Samopa Ahmad Mukhlason Doctoral Program in Technology Management School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Background: Consumer skepticism affects trust between online shoppers and shop owners in digital commerce, necessitating effective distrust detection mechanisms. This study presents a conceptual model aimed at promoting the adoption of distrust detection and recognition technology among online shop owners, focusing on technologies powered by Large Language Models (LLMs). The model builds on Taylor's Decomposed Theory of Planned Behavior (DTPB) and incorporates factors relevant to online shop owners' perceptions of consumer distrust. Method: A quantitative research approach was used to survey 100 online shop owners to validate the model empirically. Results: The results indicate a significant effect of Perceived Usefulness (PU) and Social Influence (SI) on intention to adopt the technology. For small businesses such as online shops, PU (an internal factor) and SI (an external factor) can predict owners' intention to adopt distrust detection technology. Implications: This study highlights a simplified method for measuring small business owners' intention to adopt advanced information technologies and suggests business opportunities for e-commerce platforms to strategically position online shop owners.

关键词： Surveys Navigation Large language models Focusing Position measurement Behavioral sciences Electronic commerce information technology Business

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Vision Transformer Algorithm for Plant Disease Detection: A Systematic Literature Review

Vision Transformer Algorithm for Plant Disease Detection: A ...

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International Conference on Informatics and Computing (ICIC)

作者： Reza Al Husna Lintang Yuniar Banowosari Tubagus Maulana Kusuma Tri Handhika Doctoral program Information Technology Universitas Gunadarma Jakarta Indonesia Department of Informatics Program Universitas Gunadarma Jakarta Indonesia Department of Technology and Engineering Universitas Gunadarma Jakarta Indonesia Department of Computational Mathematics Studies Universitas Gunadarma Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Advances in deep learning have significantly impacted various sectors, including agriculture, by improving efficiency and sustainability. Computer vision-based plant disease detection that utilizes deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) has replaced manual methods that require specialized skills. Vision Transformers (ViTs) with self-attention mechanisms excel at recognizing complex patterns in images of diseased plants, enabling early detection and more effective management. This study conducted a systematic literature review to evaluate the use of feature fusion-based transformer algorithms in plant disease detection and to identify current research trends, challenges, and future opportunities. The results show that ViTs can improve accuracy and efficiency in plant disease diagnosis, particularly under diverse environmental conditions. However, the adoption of this technology faces obstacles such as the need for large annotated datasets, significant computational costs, and variability in field conditions that can affect model performance. This research highlights the need for further innovation to address these challenges and expand the accessibility and reliability of deep learning-based plant disease detection for the global agricultural sector.

关键词： Deep learning Plant diseases Computer vision Technological innovation Accuracy Computational modeling Transformers Feature extraction Computational efficiency Systematic literature review

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Modifying the Revised NIOSH Lifting Equation using Anthropometric Variables to Calculate Horizontal and Vertical Multipliers 4

Modifying the Revised NIOSH Lifting Equation using Anthropom...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Rochmat, Moehamad Adi Madenda, Sarifuddin Handhika, Tri Ernastuti, Ernastuti Gunadarma University Department of Industrial Engineering Depok Indonesia Gunadarma University Doctoral Program in Information Technology Depok Indonesia Gunadarma University Centre for Computational Mathematics Studies Depok Indonesia Gunadarma University Faculty of Industrial Technology Depok Indonesia

ISBN: (纸本)9781665453950

The Revised NIOSH Lifting Equation is a work system assessment tool which often used in industrial world. The calculation result in the form of Recommended Weight Limit (RWL) and the Lifting Index (LI) become the basis for improving the work system carried out. RNLE is an application developed by NIOSH and uses American anthropometry as a constant in its calculations. Some researchers, especially in Asia, consider that the application is less suitable for Asia people who have anthropometric differences with Americans. The assessment carried out by the RNLE application is based on work activities carried out without considering the worker's body posture. In particular, the calculate of HM and VM in this application uses a constant related to anthropometry. RNLE uses the constants based on the average anthropometry of workers in calculation. The constant anthropometry used in the equation led to the results of the risk calculation that does not correspond to physical aspects of workers. It takes an equation that consider anthropometry workers to get the calculation of the value of the appropriate work risk. The study aims to propose equations that consider anthropometry factors to calculate the risk factor. The development of the equation with Python programming language allows further development in the field of information and communication technology. The results of the study shows that the average results of the RWL and LI calculation uses anthropometry data not equal to the RNLE, thus required new equation in order to resulting more objective risk value. © 2022 IEEE.

关键词： Anthropometry

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New Architecture Quantum Perceptron using Quantum Circuit

New Architecture Quantum Perceptron using Quantum Circuit

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2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics, DATABIA 2021

作者： Solikhun, Solikhun Efendi, Syahril Zarlis, Muhammad Sihombing, Poltak Universitas Sumatera Utara Doctoral Program Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9781665426800

the learning algorithm that is not optimal is due to the incomplete use of quantum in the perceptron quantum learning algorithm which is the background of this research. Previous research has shown that the proposed architectural form still needs to increase its probability value to be more optimal. The previous probability was 90.7%. The proposed model is a quantum circuit architecture in a quantum perceptron algorithm consisting of a quantum bit gate. The researchers conducted training and testing of the proposed quantum circuit architecture using quantum computer of the IBM Quantum Experience. This research produces a perceptron quantum architecture model using a quantum circuit that can solve problems regarding data classification. After measuring the proposed quantum circuit architecture, it shows a 100% probability. After training and testing, the probability is 100%, the same as that at the time of measurement. Then it produces the same output of |111100000000000000〉. The resulting probability shows a significant increase from the previous 90, 7%. © 2021 IEEE.

关键词： Probability

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Parallelizing Cipher Processes with Finite State Machine in MATLAB for Substitution and Transposition

Parallelizing Cipher Processes with Finite State Machine in ...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology (AGERS)

作者： Fauziah Sunny Arief Sudiro Bayu Kumoro Yakti Ragiel Hadi Prayitno Sarifuddin Madenda Doctoral Program of Information Technology Gunadarma University Jakarta Indonesia Magister Teknologi Informasi STMIK Jakarta STI&K Jakarta Indonesia Industrial Technology Electrical Engineering Gunadarma University Jakarta Indonesia

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

This article introduces a novel methodology for enhancing the efficiency of cipher processes by employing Finite State Machines (FSMs) within the MATLAB environment. The primary focus of this research is to improve the speed of algorithms used for both substitution and transposition ciphers, which are essential components of cryptographic practices. Recognizing the critical importance of speed in cryptographic operations, this research emphasizes the optimization of these algorithms as a key objective. Traditional encryption methods often struggle with performance limitations; thus, this paper proposes a solution by integrating parallelism into both the encryption and decryption stages through FSMs. This strategy capitalizes on the computational capabilities of modern multi-core processors, allowing for more efficient execution of cryptographic tasks. The research delineates two main phases of the proposed methodology. The first phase centers on transposition ciphers, while the second phase focuses on substitution ciphers. Data encryption is carried out via a meticulously defined FSM-based procedure that alters the input data by substituting and transposing values in accordance with a specified cryptographic key. The concurrent execution of both phases results in a substantial decrease in processing time. Empirical findings indicate that the processing duration is under 1 second for data sizes ranging from 8 to 129 bits, while encrypting and decrypting 1 billion bits of data takes approximately 162 seconds. The improvements in processing speed and the adaptability of these methods highlight their potential as effective solutions for secure data processing applications, which encompass secure communication and the advancement of sophisticated cryptographic protocols.

关键词： Ciphers Multicore processing Automata Parallel processing Software Hardware Encryption MATLAB Remote sensing Optimization

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Understanding Future Trends in Digital Banking Research Through Bibliometric Analysis

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Procedia Computer Science 2024年 234卷 764-771页

作者： Jimmy Carter Sudianjaya Heri Kuswanto Reny Nadlifatin Doctoral Program in Management of Technology School of Interdisciplinary Management and Technology Institut Teknologi Sepuluh Nopember Surabaya 6011 Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Information System Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

To generate a research overview of digital banking, we did a bibliometric analysis. In our bibliometric analysis, we identified 2475 papers about digital banking that were published in the previous 10 years (2013–2023) from 1492 different sources under the direction of 6777 academics. The bibliographic information was gathered using the Scopus database. The dataset was examined using the bibliometric R program Biblioshiny. The number of digital banking research publications published each year, the most relevant author, the most cited articles, thematic maps, and trending topics are all listed. A keyword analysis showed that “blockchain”, “security”, “digital banking”, and “digital storage” appeared most often. The research results will be significant to scholars, researchers, and policymakers in banking, as the study showed the roadmap and pathways to scientifically understanding current and future research trends.

关键词： digital banking bibliometric biblioshiny

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Convolutional Neural Network (CNN) Algorithm Development for Quality Measurement in Tire Product Classification: A Systematic Literature Review

Convolutional Neural Network (CNN) Algorithm Development for...

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International Conference on Informatics and Computing (ICIC)

作者： Armando Tirta Dwilaga Lintang Yuniar Banowosari Sudaryanto Aini Suri Talita Department of Information Technology Doctoral Program Gunadarma Information Technology University West Java Indonesia Department of Informatics Gunadarma University West Java Indonesia Department of Industrial Engineering and Management Gunadarma University West Java Indonesia Department of Information System Management Gunadarma University West Java Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Convolutional Neural Networks (CNN) have shown potential in tire defect classification, but challenges like image quality variations and model limitations persist. This Systematic Literature Review (SLR) employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and a qualitative approach. It uses the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework to analyze 30 articles from Google Scholar, published between 2018 and 2024 in Q1-Q4 journals. The review examines article distribution by publisher, evaluates the impact of key pre-training parameters (such as data split ratio, dropout rate, optimization algorithm, learning rate, and pre-trained models on CNN performance), and identifies challenges in defect classification. MDPI (Multidisciplinary Digital Publishing Institute) is the most frequent publisher, with the highest number of publications occurring in 2021. AlexNet and VGG16 are the most common models, and an 80:20 data split ratio, 0.5 dropout rate, and a 0.001 learning rate are frequently used. The Adam (Adaptive Momentum) optimization algorithm is widely adopted, but challenges remain in detecting small defects. Further improvements in CNN models, lighting techniques, and larger datasets are needed to enhance defect detection. This review highlights current trends and future research opportunities in tire quality measurement.

关键词： Image quality Adaptation models Current measurement Lighting Tires Data models Classification algorithms Convolutional neural networks Optimization Systematic literature review

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Comparison of Machine Learning Text Classification for Intent Sentiment Analysis

Comparison of Machine Learning Text Classification for Inten...

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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Abulwafa Muhammad Sarjon Defit Gunadi Widi Nur Cahyo Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%.

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