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Journal of Intelligent Decision Support System (IDSS)
Published by Institute of Computer Science
ISSN : 27215792     EISSN : 27215792     DOI : -
Core Subject : Science,
Computer Science & IT
An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS),intelligent marketing decision support systems and medical diagnosis systems. Ideally, an intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 8 No 1 (2025): March: Intelligent Decision Support System" : 5 Documents clear
Website security analysis using penetration testing method Anisah, Siti; Aslamiyah, Suwaebatul
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.284

Abstract

Website security is one of the main focuses in information system management, especially with the increasing cyber threats that can damage the integrity and confidentiality of data. One way to identify security gaps through penetration testing is widely used using automated tools to improve efficiency and accuracy. Identifying potential vulnerabilities such as SQL injection, Cross-Site Scripting (XSS), and configuration failures in This study involved implementing automated tools on several website tests, where the test results were then analyzed to determine potential security risks. The study found vulnerabilities in the form of Application Error Disclosure, Content Security Policy (CSP), hidden files found, servers leaking information via x-power-by, servers leaking version information via the server, x-content-type-options headers missing, and user agent fuzzier These findings contribute to efforts to improve the quality of automated security testing, as well as optimizing potential threat mitigation actions. Evaluate and disable components that are not needed in production, Disable or restrict closing the “X-Powered-By” and “Server” headers, Check for different responses based on User Agent, and use the HTTPS protocol throughout the application to improve its security
Product Sales Grouping Application Design Using K-Means Clustering Algorithm Sondang, Sondang
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.278

Abstract

This research aims to design and develop a product sales grouping application at Minimarket Diky using the K-Means Clustering algorithm. Product grouping based on sales patterns is one of the effective methods to improve marketing strategies, stock management, and more efficient business decision making. By using the K-Means algorithm, product sales data is processed to group products based on the initial number of items, the number sold, and the amount of stock. The designed application is able to identify sales patterns that are difficult to find manually, so as to provide deeper insights to minimarket management. This grouping process helps minimarkets in developing a more targeted product procurement strategy, managing stock more efficiently, and identifying products that have very good sales, good sales, and not good sales. The application development method used in this research is the web-based RAD (Rapid Application Development) method using the PHP programming language and MySQL database
Comparison of k-means clustering with hierarchical agglomerative clustering for the analysis of food security of rice sector in Indonesia Sinaga, Ryan Fahlepy; M Azhar Prabukusumo; Manurung, Jonson
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.290

Abstract

Indonesia's food security depends on the availability and distribution of rice as a staple food. To support data-driven policies, this study applies K-Means Clustering and Hierarchical Agglomerative Clustering (HAC) to cluster 38 provinces based on rice consumption and production patterns. Data is sourced from BPS with attributes: rice consumption per capita, rice production, rice price per kg, and population. These variables were chosen because they reflect the balance of demand, supply, affordability, and food needs. The optimal number of clusters was determined as three, based on Elbow Method and Silhouette Score for K-Means, and Dendrogram and Cophenetic Correlation Coefficient (CCC) for HAC. The clustering results identify regional characteristics related to food security and support the formulation of more targeted rice distribution policies. This study also compares the effectiveness of both methods in supporting equitable and sustainable food distribution strategies.
Mapping ownership of luxury goods and household assets in cities in Jawa Tengah using logistic regression Hanan, Rohman Ali; Firdaus, Eryan Ahmad; Manurung, Jonson
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.292

Abstract

Ownership of luxury goods and household assets is a crucial issue in the Indonesian economy, particularly in Jawa Tengah, as it reflects complex socio-economic dynamics. This study aims to map the distribution of luxury goods and household assets across regencies and cities in Jawa Tengah and analyze the factors influencing their ownership using logistic regression. Socio-economic disparities in asset ownership are driven by factors such as education, income, and access to information, which contribute to broader social inequality and regional economic development.Using data from the Jawa Tengah Statistics Agency, this study examines variations in asset ownership, including motorcycles, refrigerators, and land, across different regions. Findings indicate that regions with higher motor vehicle ownership tend to exhibit stronger economic welfare compared to those with lower asset ownership. Beyond economic factors, psychological and social aspects, including social status and religious influences, also shape decisions regarding luxury goods acquisition.This research contributes to the literature by addressing the underexplored local context of asset ownership in Indonesia. The findings provide insights for policymakers in designing more inclusive and responsive socio-economic policies, aiming to reduce disparities and promote equitable regional development.
The development of a data lakehouse system for the integration and management of cyber threat intelligence data in XYZ unit Chan, Ricky; Dhaifullah, Rendi Hanif; Saragih, Hondor; Lediwara, Nadiza; Adha, Rochedi Idul
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.293

Abstract

Cybersecurity systems are evolving to deal with increasingly complex digital threats. One of the main challenges in this field is integrating and managing Cyber Threat Intelligence (CTI) efficiently. This research aims to design and implement Data Lakehouse as a solution to manage CTI data in XYZ Unit. The system was built using Apache Spark, MinIO, Dremio, Nessie, and Apache Iceberg with a containerization approach using Docker to ensure flexibility and ease of implementation. The implementation results show that the system successfully integrates various CTI data sources and improves efficiency in data storage, processing, and analysis. MinIO is used as the primary storage, Apache Spark processes data at scale, Dremio enables real-time data analysis, and Nessie manages data version control to maintain its integrity. Blackbox testing proves that the system can work optimally, with results showing improved data integration and efficiency in managing cyber threat information. Thus, the developed Data Lakehouse can be an effective solution in supporting threat detection and strategic decision-making in XYZ Unit.

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