cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Desi Puspitasari
Contact Email
jicssnnmedia@gmail.com
Phone
+6288269134230
Journal Mail Official
jicssnnmedia@gmail.com
Editorial Address
Jalan Palem VII 18B Beringin Raya, Kec. Kemiling, Kota Bandar Lampung
Location
Kota bandar lampung,
Lampung
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025" : 5 Documents clear
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.
Implementation of the Geometric Mean Multi-Attribute Utility Theory (G-MAUT) in Determining the Best Honorary Employees Setiawansyah, Setiawansyah; Rahmanto, Yuri; Ulum, Faruk; Triyanto, Dedi
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.v3i2.50

Abstract

Determining the best honorary employees is a strategic step to appreciate performance, increase motivation, and encourage productivity in the work environment. This process is carried out by evaluating employees based on certain criteria. The main problem in determining the best honorary employees is the lack of objectivity and transparency in the assessment process, which often leads to dissatisfaction among employees. Judgments that rely solely on subjective perceptions without considering measurable quantitative data can result in unfair decisions. The purpose of applying the Geometric Mean Multi-Attribute Utility Theory (G-MAUT) method in determining the best honorary employees is to provide a more objective, transparent, and accurate evaluation framework in decision-making. This method not only supports a fairer selection process, but also encourages increased motivation and performance among honorary employees. The results of the calculation of the final utility value carried out using the G-MAUT method, the results of the evaluation of eight honorary employees showed their performance ratings comprehensively. Honorary Employee F has the highest utility value of 0.6399, making it the best honorarium employee among all available alternatives. Followed by Honorary Employee A who was ranked second with a utility value of 0.4685, and Honorary Employee D in third place with a value of 0.3947. These results provide a clear picture of the order of employees based on their performance in various criteria that have been assessed.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2025): Volume 4 Number 1 July 2025 Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025 Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024 Vol. 2 No. 2 (2024): Volume 2 Number 2 January 2024 Vol. 2 No. 1 (2023): Volume 2 Number 1 July 2023 Vol. 1 No. 2 (2023): Volume 1 Number 2 January 2023 Vol. 1 No. 1 (2022): Volume 1 Number 1 July 2022