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Jurnal Ilmiah Computer Science
Published by PT. SNN Media Tech Press
ISSN : -     EISSN : 30267145     DOI : https://doi.org/10.58602/jics
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Jurnal Ilmiah Computer Science (JICS) is a periodical scientific journal that contains research results in the field of informatics and computer science from all aspects of theory, practice and application. Papers can be in the form of technical papers or surveys of recent developments research (state-of-the-art). Topics cover the following areas (but are not limited to): Artificial Intelligence Decision Support Systems Intelligent Systems Business Intelligence Machine Learning Data mining Network and Computer Security Optimization Soft Computing Software Engineering Pattern Recognition Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 36 Documents
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Sistem Pendukung Keputusan Penentuan Kinerja Sales Terbaik Menggunakan Kombinasi Grey Relational Analysis dan Pembobotan Rank Sum Citra, Puspa; Sriyasa, I Wayan; Santoso, Heri Bambang
Jurnal Ilmiah Computer Science Vol. 2 No. 2 (2024): Volume 2 Number 2 January 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v2i2.26

Abstract

The best sales performance is one of the key elements in the business world that not only reflects the ability of individuals or sales teams to achieve sales targets, but also becomes a key pillar in the growth and success of the company. The problem in choosing the best sales performance is that there is no decision support system model in choosing the best sales performance. The purpose of this study is to determine the best sales performance by applying the GRA method and rank sum weighting in the assessment of existing sales performance, so that the results of the sales performance appraisal will be a recommendation for companies in determining the best sales performance. The ranking results showed the highest value of 0.1309 obtained by sales Hadi for rank 1, the next highest value of 0.0941 obtained by sales Arini for rank 2, the next highest value of 0.0777 obtained by sales Cindy for rank 3.
Surveying the Landscape: A Comprehensive Review of Object Detection Algorithms and Advancements Majeed Zangana, Hewa; Mustafa, Firas Mahmood
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.29

Abstract

This review paper gives a comprehensive investigation of the energetic scene of object detection, an essential field inside computer vision. Leveraging experiences from an assorted cluster of thinks about, the paper navigates through the chronicled advancement, techniques, challenges, later headways, applications, and future bearings in object detection. The comparative examination dives into the complexities of conventional strategies versus profound learning approaches, the trade-offs between exactness and speed, and the vigor of models against ill-disposed assaults. Highlighting key measurements such as cross-modal location, ceaseless learning, and moral contemplations, the paper divulges the multifaceted nature of object detection techniques. Applications of question discovery over spaces, counting independent vehicles, healthcare imaging, and keen cities, emphasize its transformative effect on different businesses. The talk amplifies to long term, envisioning challenges and openings in ranges such as ill-disposed vigor, cross-modal discovery, and moral contemplations. As a comprehensive direct for analysts, professionals, and devotees, this paper not as it were capturing the current state of object detection but too serves as a compass for exploring the strange domains that lie ahead. The survey typifies the essence of protest detection's advancement and its significant suggestions, empowering proceeded investigation and advancement within the domain of computer vision.
Advancements and Applications of Convolutional Neural Networks in Image Analysis: A Comprehensive Review Majeed Zangana, Hewa; Mohammed, Ayaz Khalid; Mustafa, Firas Mahmood
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.30

Abstract

Convolutional Neural Networks (CNNs) have revolutionized image analysis, extracting meaningful features from raw pixel data for accurate predictions. This paper reviews CNN fundamentals, architectures, training methods, applications, challenges, and future directions. It introduces CNN basics, including convolutional and pooling layers, and discusses diverse architectures like LeNet, AlexNet, ResNet, and DenseNet. Training strategies such as data preprocessing, initialization, optimization, and regularization are explored for improved performance and stability. CNN applications span healthcare, agriculture, ecology, remote sensing, and security, enabling tasks like object detection, classification, and segmentation. However, challenges like interpretability, data bias, and adversarial attacks persist. Future research aims to enhance CNN robustness, scalability, and ethical deployment. In conclusion, CNNs drive transformative advancements in image analysis, with ongoing efforts to address challenges and shape the future of AI-enabled technologies.
BI-RADS Category Prediction from Mammography Images and Mammography Radiology Reports Using Deep Learning: A Systematic Review Shiwlani, Ashish; Ahmad, Ahsan; Umar, Muhammad; Dharejo, Nasrullah; Tahir, Anoosha; Shiwlani, Sheena
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.31

Abstract

Women's health and mortality are significantly threatened by breast cancer, underscoring the importance of timely detection and treatment. Mammograms are an extremely useful and trustworthy diagnostic tool for early detection and screening of breast cancer. Mammograms based CADe systems have helped doctors in predicting BI-RADS categories and make better decisions and have somewhat reduced diagnostic errors. As deep learning algorithms advance, deep learning-based CADe systems become a practical means of resolving these problems and greatly improving the accuracy. The purpose of this review is to discuss the current techniques that have been developed for BI-RADS category classification in the fields of deep learning and convolutional neural networks. Additionally, the paper demonstrates the progression of models introduced in the past ten years. It also discusses the shortcomings of models proposed in the literature for the prediction of BI-RADS categories from mammography radiology reports and mammography images, in addition to summarizing the current challenges. Lastly, it proposes a novel multi-modal approach to predict the BI-RADS categories from radiology reports and mammography images.
Manajemen Proyek Sistem Informasi Pembayaran SPP Pada MAS YMI Sinaksak Berbasis Web Rangkuti, Faiz Wahyu Perdana; Yahfizham
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.34

Abstract

Di era globalisasi ini, kemajuan teknologi dan penyebaran informasi yang cepat memerlukan pemanfaatan teknologi canggih untuk menyelesaikan berbagai masalah. MAS YMI Sinaksak merupakan sebuah lembaga pendidikan di Sumatera Utara, mengalami kesulitan dalam proses pembayaran SPP yang masih dilakukan secara manual, menyebabkan pelayanan lambat, rawan kesalahan, dan kesulitan dalam pembuatan laporan. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Pembayaran SPP Berbasis Web guna mengatasi masalah tersebut. Metode penelitian mencakup observasi, wawancara, dan studi pustaka. Pengembangan sistem menggunakan metode Waterfall yang meliputi tahap inisiasi, perencanaan, eksekusi, dan penutupan. Sistem yang diusulkan memungkinkan pengelolaan data pembayaran SPP secara efisien melalui fitur-fitur seperti halaman login, beranda, data admin, data siswa, data transaksi, dan data laporan. Pengujian sistem dilakukan menggunakan metode black box untuk memastikan fungsionalitasnya. Hasil penelitian menunjukkan bahwa sistem informasi ini dapat meningkatkan efisiensi, akurasi, dan keamanan dalam proses administrasi pembayaran SPP di MAS YMI Sinaksak, serta memudahkan petugas dalam mengelola dan mengakses data.
From Classical to Deep Learning: A Systematic Review of Image Denoising Techniques Majeed Zangana, Hewa; Mustafa, Firas Mahmood
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.36

Abstract

Image denoising is essential in image processing and computer vision, aimed at removing noise while preserving critical features. This review compares classical methods like Gaussian filtering and wavelet transforms with modern deep learning techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). We conducted a systematic literature review from [start year] to [end year], analyzing studies from IEEE Xplore, PubMed, and Google Scholar. Classical methods are effective for simple noise models but struggle with fine detail preservation. In contrast, deep learning excels in both noise reduction and detail retention, supported by metrics like PSNR and SSIM. Hybrid approaches combining classical and deep learning show promise for balancing performance and computational efficiency. Overall, deep learning leads in handling complex noise patterns and preserving high-detail images. Future research should focus on optimizing deep learning models, exploring unsupervised learning, and extending denoising applications to real-time and large-scale image processing.
The Human Factor in Cybersecurity: Addressing the Risks of Insider Threats Zangana, Hewa Majeed; Sallow, Zina Bibo; Omar, Marwan
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.37

Abstract

In the rapidly evolving landscape of cybersecurity, the human element remains one of the most critical and complex factors to manage. Insider threats, whether originating from malicious intent or inadvertent actions, pose significant risks to organizational security. This paper explores the multifaceted nature of insider threats, examining the motivations and behaviors that drive individuals to compromise systems. By analyzing case studies and current research, we identify key vulnerabilities and the role of organizational culture in mitigating these risks. Furthermore, we propose comprehensive strategies for detecting, preventing, and responding to insider threats, emphasizing the importance of continuous education, robust access controls, and advanced monitoring technologies. This paper aims to provide a holistic understanding of the human factor in cybersecurity and offers practical solutions to address the pervasive challenge of insider threats.
Advances in Adaptive Resonance Theory for Object Identification and Recognition in Image Processing Zangana, Hewa; Mustafa , Firas Mahmood; Omar , Marwan
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.41

Abstract

Adaptive Resonance Theory (ART) has emerged as a significant framework in the realm of image processing, particularly in object identification and recognition. This review paper examines the application and effectiveness of ART in these domains. By analyzing a wide range of studies, we highlight ART's high accuracy, precision, and robustness in recognizing objects under varying conditions. The methodology involves data collection, preprocessing, and the configuration and training of ART networks. Our results demonstrate ART's superior performance compared to traditional neural networks, particularly in handling noisy data and real-time learning. Furthermore, we discuss the integration of ART with other technologies, such as memristor-based neuromorphic systems and fuzzy logic, to enhance its capabilities. The study underscores the versatility of ART, suggesting its applicability in diverse fields including robotics and cybersecurity. The results of our analysis demonstrate that ART achieves an average accuracy of 92% on the CIFAR-10 dataset and 89% on ImageNet, with a precision of 91% and a recall of 88%. These findings confirm ART's superior performance in recognizing objects under varying conditions, particularly in handling noisy data and real-time learning. Future research directions include improving feature extraction methods, dynamic parameter adjustment, and exploring hybrid models. This paper confirms ART's potential as a powerful tool in advancing image processing technologies.
Hepatocellular Carcinoma Prediction in HCV Patients using Machine Learning and Deep Learning Techniques Saeed, Fiza; Shiwlani, Ashish; Umar, Muhammad; Jahangir, Zeib; Tahir, Anoosha; Shiwlani, Sheena
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.48

Abstract

Hepatitis C virus is the root cause of 78% of hepato-cellular carcinoma cases. Hepatocellular carcinoma (HCC) represents one of the primary causes of liver cancer mortality and incidence. Clinical prediction of HCC in patients suffering with hepatitis C virus infection (HCV) is challenging due to the diagnostic gold standard, liver biopsy, which is an invasive technique with several limitations. Artificial intelligence (AI) technology is being used in clinical research at a larger rate in recent years, and the field of HCC diagnosis is no exception. Several advanced and light-weight machine learning algorithms combined with less invasive blood tests have promising diagnostic potential to diagnose HCC from HCV. Deep learning algorithms are regarded as best methods for handling and processing complex, unstructured and raw data from various modalities, ranging from routine clinical variables i.e., from EMRs and laboratories to high-resolution medical images. This paper offers a thorough analysis of the most current research that has used machine learning and deep learning to diagnose, prognosticate, treat, and predict HCC risk in patients suffering with HCV.
Evaluasi Kinerja Divisi Logistik Berbasis Sistem Pendukung Keputusan dengan Pendekatan OWH-TOPSIS Ariany, Fenty; Kurniawan, Deny
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.49

Abstract

The performance of the Logistics Division is one of the important indicators in ensuring the smooth flow of goods, information, and resources in an organization. The optimal performance of the logistics division can be seen from the ability to meet delivery time targets, operational cost efficiency, order fulfillment accuracy, and adaptability to changes in market demand. One of the main problems is that the assessment criteria are not clear or relevant, so the assessment results do not reflect the actual abilities and contributions of employees. In addition, there is a lack of measurable quantitative data to identify operational performance. The solution to this problem involves the application of structured, objective, and data-driven evaluation methods, as well as the development of systems that support transparency in the assessment process. This study aims to evaluate the performance of the Logistics Division objectively and comprehensively using the decision support system approach based on OWH-TOPSIS, so as to provide a transparent, accurate, and relevant performance evaluation system to support strategic decision-making related to improving the performance of the Logistics Division. The results of the ranking of the performance evaluation of the logistics division, Team D showed the best performance with the highest score, which was 0.882. In second place, Team A has a score of 0.8341, followed by Team B with a score of 0.8255. Meanwhile, Team C occupies the last position with the lowest score of 0.6831. This difference in scores indicates that there is a variation in performance between teams, with Team D significantly superior to other teams.

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