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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,127 Documents
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Impact of Parallel Processing on LightGBM Implementation a Comparative Analysis CPU on Iris Plants Dataset Sulaiman, Sulaiman Muhammed; Zeebaree, Sobhi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3813

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.
Distributed Systems for Machine Learning in Cloud Computing: A Review of Scalable and Efficient Training and Inference Sadiq, Shereen; R. M. Zeebaree, Subhi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3814

Abstract

Traditional computer systems have been pushed to their limits as a result of the exponential rise of data and the rising complexity of machine learning (ML) models. As a result of its on-demand scalability and resource agility, cloud computing has emerged as the platform of choice for training and deploying large-scale machine learning models. However, in order to make good use of cloud resources for machine learning, it is necessary to make use of distributed systems. These systems are responsible for coordinating computations over several nodes in order to manage the demanding workloads. The purpose of this paper is to investigate the realm of distributed systems for machine learning in cloud computing, with a particular emphasis on training and inference that is both scalable and efficient. During the discussion on the need of distributed systems in machine learning, it was made clear why conventional single-machine techniques are not enough for the requirements of current machine learning and how distributed systems might help solve these difficulties. Scalability and Efficiency Considerations were reviewed in relation to the primary elements that contribute to the effectiveness of a distributed system for machine learning. These elements include task partitioning, communication overhead, fault tolerance, and resource optimization that were discussed. In the context of cloud computing, the purpose of this review research is to provide a complete overview of the fascinating topic of distributed systems for machine learning. In order to successfully traverse the intricate and ever-changing world of cloud-based machine learning, it provides vital insights and information.
Harnessing the Power of Distributed Systems for Scalable Cloud Computing A Review of Advances and Challenges Taher, Hanan; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3815

Abstract

In the realm of cloud computing, the literature defines scalability as the inherent ability of a system, application, or infrastructure to adapt and accommodate varying workloads or demands efficiently. It encompasses the system's capability to handle increased or decreased usage with compromising performance, responsiveness, or stability. In this paper, a comprehensive review is presented regarding the scalability in the cloud computing network. In addition, the research community define the scalability as a dynamic attribute, emphasizing its ability to facilitate both horizontal and vertical scaling. Horizontal scalability involves adding or removing instances or nodes to distribute workloads across multiple resources, while vertical scalability focuses on enhancing the capacity of existing resources within a single entity. They established a global frameworks to evaluate scalability, often emphasizing response time, throughput, resource utilization, and cost-efficiency as critical metrics. These metrics serve as benchmarks to assess the system's ability to scale effectively without compromising performance or incurring unnecessary costs [1]. The literature underscores scalability's interconnectedness with elasticity, highlighting the need for on-demand resource provisioning and de-provisioning to maintain an agile and adaptable infrastructure. Overall, in academic papers, cloud scalability is portrayed as a fundamental attribute crucial for modern computing infrastructures, enabling systems to flexibly and efficiently adapt to dynamic computing needs.
Analisis Topic Modelling Pariwisata Yogyakarta Menggunakan Latent Dirichlet Allocation (LDA) Uray Nur Khadijah; Nuri Cahyono
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3816

Abstract

Pariwisata Yogyakarta sebagai destinasi yang kaya akan budaya dan sejarah, sering menjadi fokus diskusi di media sosial. Tujuan dari Penelitian ini adalah menelaah topik pariwisata Yogyakarta dari Twitter. Dataset yang diperoleh dalam penelitian ini dari crawling data menggunakan API key Twitter. Penelitian ini menggunakan tahapan dari pengumpulan data, text preprocessing, dan menerapkan metode Topic Modelling, khususnya Latent Dirichlet Allocation (LDA). Hasil penelitian ini pengujian kinerja pemodelan topik dengan metode LDA dapat dilihat dari nilai coherence score, semakin tinggi nilai coherence suatu topik, semakin mudah diinterprestasikan oleh manusia dan Perplexity merupakan salah satu standar pengukuran yang dapat digunakan untuk menilai kinerja model yang baik dari model tersebut ditunjukkan dengan nilai perplexity yang lebih rendah. Nilai coherence score yang ditunjukkan pada num topic ke-1 sebesar 0.331047, untuk nilai perplexity ditunjukkan dengan nilai yang tinggi terletak pada num topic ke-3 sebesar -8.830172565520245. diharapkan dapat memberikan wawasan mendalam tentang topik-topik yang sering dibahas dan berkonsentrasi pada penerapan sistem pemodelan topik untuk membangun sistem keputusan topik berita yang menggunakan metode Latent Dirichlet Allocation (LDA). Pada Penelitian ini efektif dalam menggunakan metode LDA untuk menentukan topik berita yang mencakup tiga kategori topik yang sering dibicarakan pada masing-masing kelas.
Supervised Machine Learning Model untuk Prediksi Penyakit Hepatitis Putra, Andriyan Dwi; Nurani, Dwi; Dewi, Melany Mustika; Rahmi, Alfie Nur; Supriatin
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3817

Abstract

Hepatitis menjadi salah satu penyakit mematikan yang diakibatkan karena peradangan yang terjadi pada organ hati manusia. Hepatitis seringkali disebabkan karena infeksi virus dan gaya hidup yang tidak sehat. Hepatitis bahkan bisa menular apabila dikaitkan dengan infeksi dari adanya virus tertentu. Hepatitis perlu dideteksi secara dini dan diantisipasi sedini mungkin sehingga tidak mengakibatkan adanya penyakit komplikasi yang lebih serius yang bahkan dapat mengakibatkan terjadinya kematian. Perkembangan teknologi informasi dan komunikasi yang terus berkembang hingga saat ini memungkinkan penyakit hepatitis untuk dapat dikenali dan diprediksi. Salah satunya menggunakan teknologi pembelajaran mesin. Pada penelitian ini, metode supervised learning yang menerapkan algoritma Naïve Bayes dan KNearest Neighbor digunakan untuk memprediksi adanya penyakit hepatitis. Dengan menggunakan dataset yang diunduh secara langsung dari halaman website UCI Machine Learning Repository, Naïve Bayes menghasilkan nilai akurasi sebesar 91.67% dengan nilai presisi dan recall mencapai 95%, Sedangkan penggunaan K-Nearest Neighbor menghasilkan nilai akurasi sebesar 95.8%, dengan adanya perbedaan nilai presisi dan recall sebesar 1%, menunjukkan bahwa penggunaan pervised machine learning model berdasarkan algoritma Naïve Bayes dan K-Nearest Neighbor memiliki potensi untuk digunakan dalam pengembangan berbagai sistem terutama untuk prediksi penyakit hepatitis.
A Review of Blockchain-Rooted Energy Administration in Networking Wijesekara, Patikiri Arachchige Don Shehan Nilmantha
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3818

Abstract

Energy Administration (EA) in networking involves improving energy efficiency by managing energy. The blockchain framework involves a chain of associated blocks that obviously protects the genuineness, upholds accountability, and upholds 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 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 provide automated EA tasks operating smart contracts. Detailed exploration shows that from blockchain-rooted EA, 32.5% operate blockchain to store and exchange data for an 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 to the conception of blockchain-rooted EA and then present guidance to vanquish them.
Distributed Systems for Data-Intensive Computing in Cloud Environments: A Review of Big Data Analytics and Data Management Zeravan Arif; Subhi R. M. Zeebaree
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3819

Abstract

Because of the increasing increase of data, which is frequently referred to as "big data," many different businesses have been severely impacted in recent years, necessitating the implementation of sophisticated data management and analytics solutions. By virtue of the fact that it provides scalable resources for applications that are data-intensive, cloud computing has emerged as an indispensable platform for the management of these enormous databases. The evolving landscape of distributed systems in cloud settings is the primary emphasis of this study, which is situated within the framework of big data analytics and data management. With the purpose of providing a comprehensive overview of distributed systems that are used in cloud settings for data-intensive computing, the review article seeks to offer. Furthermore, it evaluates the many ideas, techniques, and technical improvements that have been established in order to properly manage, store, and analyse large amounts of data. A comprehensive literature evaluation of recently published scientific references was successfully completed by our team. The analysis takes into account the theoretical foundations, as well as the research that has already been conducted on distributed computing systems, cloud-based data management, and enormous data analytics. The study places an emphasis on the significant role that distributed computing plays in ensuring the success of big data analytics. The interplay between distributed systems and cloud computing paradigms has resulted in the development of solutions that are robust, scalable, and economical for activities that need a significant amount of data. It is still a huge problem to ensure that data security, privacy, and interoperability are maintained across the many cloud services.
Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications Abd Alnabe, Nisreen; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3821

Abstract

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.
Perancangan Sistem Informasi Rekam Medis Berbasis Web Aditama Farhan Mudhoffar, Rama Reiswa; Widayat, Widi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3822

Abstract

Klinik Pratama Pintan Sari merupakan klinik berbasis rumah bersalin dan juga balai pengobatan di Kabupaten Sukoharjo. Kegiatan operasional yang dijalankan memiliki keterbatasan seperti pendataan data rekam medis pasien yang masih manual, yang mengakibatkan proses pengelolaan data menjadi kurang efisien. Tujuan dari penelitian adalah untuk membuat sistem informasi rekam medis, yang bisa mempermudah proses penginputan data rekam medis, serta efisien dalam pencarian data. Metode yang digunakan dalam penelitian adalah metode waterfall. Dan untuk proses pembuatan sistem menggunakan bahasa pemrograman PHP serta framework Laravel. Hasil dari penelitian berupa sistem informasi rekam medis dengan fitur seperti login multiuser, data pasien, rekam medis, data obat, serta fitur untuk cetak laporan rekam medis dan nota pembayaran. Pengujian sistem menggunakan metode Black Box dan System Usability Scale yang memperoleh nilai rata rata 73. Artinya sistem yang dibangun termasuk dalam kategori Good dengan grade scale C. Sehingga dapat disimpulkan bahwa sistem informasi ini dapat diterima oleh petugas medis Klinik Pintan Sari.
Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques Ali, Nabeel N.; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3823

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.

Page 48 of 113 | Total Record : 1127

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