Indonesian Journal of Computer Science最新文献

{"title":"Impact of Parallel Processing on LightGBM Implementation a Comparative Analysis CPU on Iris Plants Dataset","authors":"Sulaiman Muhammed Sulaiman, Sobhi Zeebaree","doi":"10.33022/ijcs.v13i2.3813","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3813","url":null,"abstract":"Parallel processing has emerged as base for machine learning to address the computational requirements of complex models and expanded datasets. In additions, Parallel functions give the ability to algorithms exploiting the full potential of available accounting resources. This mechanism enhances parallel processing capabilities, as calculations are distributed through a multiple processor. This research explores the impact of the parallel processing of the central processing unit on the performance of LightGBM. The gradient-based learning in LightGBM enables efficient feature split decisions during tree construction. framework for scaling up, using the IRIS plant dataset. The study aims at comparing accuracy measures and training time for trained models with or without parallel processing units. The methodology includes advance data processing steps and the formation of environmentally sound management models with or without parallel processing units. The results reveal marked differences in accuracy and training time between the model and parallel processing of the central processing unit and its counterpart without it. Research contributes to understanding the role of parallel processing in the optimal functioning of the automated learning model.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"58 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140784308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques","authors":"Nabeel N. Ali, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3823","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3823","url":null,"abstract":"This review paper provides an in-depth examination of distributed resource management in cloud computing, focusing on the critical elements of allocation, scheduling, and provisioning. Cloud computing, characterized by its dynamic and scalable nature, necessitates efficient resource management techniques to optimize performance, cost, and service. The study encompasses a comprehensive analysis of various strategies in resource allocation, scheduling methodologies, and provisioning techniques within the cloud computing paradigm. Through comparative analysis, this paper aims to highlight the synergies and trade-offs inherent in these methods, offering a holistic view of distributed resource management. It contributes to the field by bridging the gap in existing literature, presenting a critical, comparative analysis of current strategies and their interplay in distributed cloud environments.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"8 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140764117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges","authors":"J. Fadhil, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3794","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3794","url":null,"abstract":"The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"43 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140771642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Sharing at Rooftop Solar PV System Based Community Microgrid Using Helioscope Software","authors":"Thiri Zin, Dr. Wunna Swe","doi":"10.33022/ijcs.v13i2.3826","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3826","url":null,"abstract":"Due to increase in use of rooftop solar PV system and increase of electricity price, the power sharing among the prosumers of community microgrid become an interesting research area. This study proposes a power sharing mechanism that captures the interaction within a community microgrid. The efficient management of energy sharing is crucial for the efficient operation of community microgrids. To design rooftop solar PV systems, Helioscope software is employed. One of the key features of Helioscope is its ability to efficiently arrange arrays and blocks of solar panels based on the designated location within the software. The power sharing is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a load demand and PV generation mechanism is designed to deal with the uncertainty of PV energy and load consumption.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"73 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing","authors":"Nashwan Hussein, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3802","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3802","url":null,"abstract":"This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"128 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140767755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel Processing Impact on Random Forest Classifier Performance: A CIFAR-10 Dataset Study","authors":"Bareen Haval Sadiq, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3803","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3803","url":null,"abstract":"Using the CIFAR-10 dataset, this research investigates how parallel processing affects the Random Forest method's machine learning performance. Accuracy and training time are highlighted in the study as critical performance indicators. Two cases were studied, one with and one without parallel processing. The results show the strong prediction powers of the Random Forest algorithm, which continues to analyze data in parallel while retaining a high accuracy of 97.50%. In addition, training times are notably shortened by parallelization, going from 0.6187 to 0.4753 seconds. The noted increase in time efficiency highlights the importance of parallelization in carrying out activities simultaneously, which enhances the training process's computational efficiency. These results provide important new information about how to optimize machine learning algorithms using parallel processing approaches.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"36 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140770044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools","authors":"Khalid Ibrahim Khalaf Jajan, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3805","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3805","url":null,"abstract":"This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"357 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embracing Distributed Systems for Efficient Cloud Resource Management: A Review of Techniques and Methodologies","authors":"Abdo Abdi, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3806","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3806","url":null,"abstract":"The development of parallel computing, distributed computing, and grid computing has introduced a new computing model, combining elements of grid, public computing, and SaaS. Cloud computing, a key component of this model, assigns computing to distributed computers rather than local computers or remote servers. Research papers from 2017 to 2023 provide an overview of the advancements and challenges in cloud computing and distributed systems, focusing on resource management and the integration of advanced technologies like machine learning, AI-centric strategies, and fuzzy meta-heuristics. These studies aim to improve operational efficiency, scalability, and adaptability in cloud environments, focusing on energy efficiency and cost reduction. However, these advancements also present challenges, such as implementation complexity, adaptability in diverse environments, and the rapid pace of technological advancements. These issues necessitate practical, efficient, and forward-thinking solutions in real-world settings. The research conducted between 2017 and 2023 highlights the dynamic and rapidly evolving field of cloud computing and distributed systems, providing valuable guidance for ongoing and future research. This body of work serves as a crucial reference point for advancing the field and emphasizing the need for practical, efficient, and forward-thinking solutions in the ever-evolving landscape of cloud computing and distributed systems.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"140 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140796586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crosslingual Transfer Learning for Arabic Story Ending Generation","authors":"A. Alhussain, Aqil M. Azmi","doi":"10.33022/ijcs.v13i2.3831","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3831","url":null,"abstract":"In the field of natural language processing, the task of generating story endings (SEG) requires not only a deep understanding of the narrative context but also the ability to formulate coherent conclusions. This study delves into the use of crosslingual transfer learning to address the challenges posed by the scarcity of Arabic data in SEG, proposing the utilization of extensive English story corpora as a solution. We evaluated the efficacy of multilingual models, such as mBART, mT5, and mT0, in generating Arabic story endings, assessing their performance in both zero-shot and few-shot scenarios. Despite the linguistic complexities of Arabic and the inherent challenges of crosslingual transfer, our findings demonstrate the potential of these multilingual models to transcend linguistic barriers, significantly contributing to the domain of natural language processing across different languages. This research has significant implications for generating creative text and improving multilingual natural language processing in resource-limited language contexts","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"65 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Blockchain-Rooted Energy Administration in Networking","authors":"Patikiri Arachchige Don Shehan Nilmantha Wijesekara","doi":"10.33022/ijcs.v13i2.3818","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3818","url":null,"abstract":"Energy Administration (EA) in networking involves in improving energy efficiency by managing energy. Blockchain framework involves of a chain of associated blocks that obviously protects the genuineness, upholding the accountability, and uphold disguised-anonymity of its transactions/entries with the help of peer-to-peer consensus techniques and cryptographic mechanisms. Driven by the fact that existing surveys do not focus on the EA in the broad scope of networking, we review on diverse blockchain-rooted EA solutions, where we recognize 7 roles of blockchain in EA and explore them in detail with regard to EA techniques, EA approaches, blockchain linked factors, network linked factors, and such. We assembled a first-stage sample of 80 document citations by appraising the articles for qualification criteria hunted from E-libraries operating a detailed and prolonged process. Considering the review, in blockchain-rooted EA, blockchain can facilitate storing and exchanging data in a trustworthy manner, operate energy efficient consensus approaches, act as an energy manager, provide authentication and access control for EA, facilitate secure offloading for EA, and automated EA task operating smart contracts. Detailed exploration shows that from blockchain-rooted EA, 32.5% operate blockchain to store and exchange data for EA task, 95% operate uniform blockchain, 30% operate PoW consensus, 82.5% operate fully decentralized EA, 57.5% operate cross layer EA, and 10% operate in IoT networks. Finally, we debate the possibilities and barriers of the conception of blockchain-rooted EA and then presenting guidance to vanquish them.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"46 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140771006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}