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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Setiawan, Tri Agus Heryadi, Yaya Lukas Wulandari, Lili Ayu Sonata, Ilvico Binus Graduate Program -Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350368970

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Transformer and CNN Comparison for Time Series Classification Model 15

Transformer and CNN Comparison for Time Series Classificatio...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Sonata, Ilvico Heryadi, Yaya Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350383829

In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be reduced or added so that the main goal of the series of activities is achieved. Apart from that, there are also time series events that occur naturally, such as rainy and hot conditions in a certain area. The classification process of time series activities is very important to see the possibility of anomalies occurring. The significant development of machine learning models in recent years has made the process of classifying time series data increasingly researched. Several previous studies stated that deep learning models were more accurate in classifying time series data. In this paper, we will compare Convolutional Neural Network (CNN) and Transformer deep learning models in classifying time series data. Experimental results using the same public datasets for CNN and Transformer model show that the CNN model is more accurate than the Transformer model. The results of measuring accuracy using confusion matrix show that CNN has an accuracy of 92% and Transformer has an accuracy of 80%. © 2023 IEEE.

关键词： Time series

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Improved Model for Identifying the Cyberbullying Based on Tweets of Twitter

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Informatica (Slovenia) 2023年第6期47卷 159-164页

作者： Samalo, Darwin Martin, Rizky Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The surge of cyberbullying on social media platforms is a major concern in today's digital age, with its prevalence escalating alongside advancements in technology. Thus, devising methods to detect and eliminate cyberbullying has become a crucial task. This research meticulously presents a refined model for identifying instances of cyberbullying, building on previous methodologies. The process of devising the model involved a thorough literature review, object-oriented design, and decision tree methodologies to shape the labelling procedure and build the classifier. Data pre-processing was executed using RapidMiner, considering six intrinsic components. The final model successfully classified Indonesian-language tweets into five distinct categories: animal, psychology and stupidity, disabled person, attitude, and general bullying, with an accuracy rate of 99.56%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution 5

Rice Commodity Supply Chain Issues in Indonesia: Exploring B...

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5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Taufiq, Rohmat Warnars, Harco Leslie Hendric Spits Soeparno, Haryono Oktavia, Tanty Computer Science Department - BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System Management Department BINUS Graduate Program Master of IS Management Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350362602

—Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently. ©2024 IEEE.

关键词： Supply chains

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Developing Smart Precision Farming Using Big Data and Cloud-Based Intelligent Decision Support System 10

Developing Smart Precision Farming Using Big Data and Cloud-...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nurcahyo, Aldian Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-Time monitoring and predictive analytics to increase agricultural production and sustainability. © 2023 IEEE.

关键词： Decision support systems

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COCOMO II Analysis of Developing Multi-Account Partner Software for Crypto Exchange 15

COCOMO II Analysis of Developing Multi-Account Partner Softw...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Darmawan, Oscar Adam Gaol, Ford Lumban Soeparno, Haryono Arifin, Yulyani Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

The technique of calculating the work required for software development is known as software development estimation. The objective cost estimating technique for organizing and carrying out Multi Account Partner (MAP) software projects, COCOMO II, was employed in this study. The MAP software is a software architecture designed to support brokerage cryptocurrency exchanges using the order book and liquidity of established crypto exchanges. This research uses data sets from MAP project development at Indonesia Crypto Exchange Platform. It aims to create a software cost estimation model for MAP software using COCOMO II so that the resulting estimation model can be used as input or reference for estimates of subsequent MAP software development. The result estimated that MAP software finished in about four to five months, with a price range for software development of $7,441 to $8,780. Further research is needed with datasets from other crypto exchanges tested to increase cost estimation accuracy using COCOMO II. © 2023 IEEE.

关键词： Blockchain

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Group Decision Support Model for Tech-Based Startup Funding Using Multistage Fuzzy Logic

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Informatica (Slovenia) 2023年第6期47卷 131-144页

作者： Devanda, Muhammad Donny Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The startup business model has grown rapidly in the last few years. However, giving investment or funding to a startup, especially in its early stages, is difficult because the risk is higher than a conventional company. This paper proposes a group decision support model (GDSM) that can help both government venture capital (GVC) and private venture capital (PVC) make the right funding decision. The model was built using a simple mathematics method (SMM) and multistage fuzzy logic (MFL) to examine twenty-two parameters in fuzzy and nonfuzzy values. Two experts from GVC and PVC were interviewed to weigh all the parameters. The model is implemented and tested using three real-world data. Ultimately, the model can help decision-makers in GVC and PVC to decide the most optimum funding for startups. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Investments

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The Impact of Shift-Left Testing to Software Quality in Agile Methodology: A Case Study 8

The Impact of Shift-Left Testing to Software Quality in Agil...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Andriadi, Kus Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

The point of Agile Methodology is continuous improvement, delivering a small feature quickly without sacrificing the feature quality;every sprint must be better than the previous sprint, and better can be fewer bugs, faster development, and testing. We will present how we reduce production bugs by customizing our sprint iteration. As we know, bugs are unavoidable, there is no software engineer that can make software without a bug;however, we can reduce bugs in production if we can find bugs in lower environments as early as possible. The case study in this paper was taken from one of technology company in Indonesia, the activity was done by the Quality Engineer (QA) Team. We will show that shift-left testing can help us reduce bugs in production. Testing is part of agile methodology, and the main idea of shift-left testing is to move testing early and could be done by any team member, not only QA. We include shift-left testing in our agile methodology for one year in 2022 and compare the result in the previous year. © 2023 IEEE.

关键词： Software testing

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A Survey of Refactoring Techniques to Maximize Code Coverage Metric 15

A Survey of Refactoring Techniques to Maximize Code Coverage...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Simatupang, Johannes Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to examine refactoring techniques that can maximize Code Coverage Metric. Through the study, identification, evaluation, and summary of empirical evidence from various literature sources are carried out. The results of this study provide guidance on effective refactoring techniques to improve Code Coverage as well as other positive impacts for software development. There are ten refactoring techniques that can be used to improve Code Coverage Metrics in software testing. © 2023 IEEE.

关键词： Software testing

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Lip-Reading with Visual Form Classification using Residual Networks and Bidirectional Gated Recurrent Units

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第2期4卷 375-386页

作者： Anni Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (ResNets). This study primarily aimed to conduct a comparative analysis of several types of ResNets. This study additionally calculates metrics for several word structures included in the GRID dataset, encompassing verbs, colors, prepositions, letters, and numerals. This component has not been previously investigated in other studies. The proposed approach encompasses several stages, namely pre-processing, which involves face detection and mouth location, feature extraction, and classification. The architecture for feature extraction comprises a 3-dimensional convolutional neural network (3D-CNN) integrated with ResNets. The management of temporal sequences during the classification phase is accomplished through the utilization of the bidirectional gated recurrent units (Bi-GRU) model. The experimental results demonstrated a character error rate (CER) of 14.09% and a word error rate (WER) of 28.51%. The combination of 3D-CNN ResNet-34 and Bi-GRU yielded superior outcomes in comparison to ResNet-18 and ResNet-50. The correlation between increased network depth and enhanced performance in lip-reading models was not consistently observed. Nevertheless, the incorporation of additional trained parameters offers certain benefits. Moreover, it has demonstrated superior levels of precision in comparison to human professionals in the task of distinguishing diverse word structures. © Authors retain all copyrights.

关键词： Face recognition

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