cover
Contact Name
Marsono Marsel.
Contact Email
idss@iocspublisher.org
Phone
+6281381251442
Journal Mail Official
idss@iocspublisher.org
Editorial Address
Romeby Lestari Housing Complex Blok C Number C14, North Sumatra, Indonesia
Location
Unknown,
Unknown
INDONESIA
Journal of Intelligent Decision Support System (IDSS)
ISSN : 27215792     EISSN : 27215792     DOI : -
Core Subject : Science,
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.
Articles 162 Documents
Sales forecasting of pet food at oyen petshop using the fuzzy time series–markov chain method Pratama, Agung; Kurniawan, Rakhmat
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 4 (2025): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i4.327

Abstract

Oyen Petshop faces stock management inaccuracies because sales records are still kept manually, while demand patterns are highly fluctuating and difficult to predict, often leading to overstock or stockouts that harm the business. The purpose of this study is to develop a Fuzzy Time Series–Markov Chain (FTS-MC) model to forecast pet food sales at Oyen Petshop and implement it in the form of a website. The research method applies FTS-MC to construct fuzzy intervals, generate FLR/FLRG, calculate transition probabilities, and produce forecasts based on dry-food sales data from April 2024 to March 2025. The results show that the FTS-MC model achieves a MAPE of 8.92%, with forecasted values that follow actual fluctuations and indicate a stable demand trend of 206–224 units for the next seven periods. Black Box Testing confirms that all web-based system functions operate correctly and all test scenarios pass, ensuring the system is ready for operational use. The findings indicate that the system enables more precise stock estimation, supports the establishment of safe reorder limits, and allows procurement decisions to be made faster and more consistently without manual calculations.
Proxmox-Based virtualization for CBT moodle hosting: VM vs LXC performance evaluation Parulian, Parulian
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 4 (2025): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i4.328

Abstract

Server virtualization plays a critical role in managing e-learning infrastructure, particularly in Computer-Based Testing (CBT) systems that require high performance, stability, and efficient resource utilization. Proxmox Virtual Environment (Proxmox VE) provides two commonly used virtualization models: Virtual Machines (VM), which rely on full virtualization, and Linux Containers (LXC), which utilize lightweight container-based virtualization. This study aims to evaluate and compare the performance of Moodle as a CBT platform when hosted on VM and LXC environments in Proxmox VE. The evaluation focuses on several performance indicators including CPU utilization, memory consumption, I/O throughput, response time, and system behavior under concurrent user load. The methodology involves deploying Moodle in parallel on VM and LXC environments with identical hardware specifications, followed by load simulation using scenarios that reflect real examination conditions such as mass login, question navigation, and simultaneous submission. The results indicate that LXC provides higher efficiency, demonstrating lower resource consumption and faster response times compared to VM. However, VM maintains advantages in system isolation, compatibility with low-level configurations, and stability when running complex or highly customized services. Based on these findings, Proxmox VE using LXC can be considered a suitable deployment choice for high-performance Moodle CBT environments where resource efficiency and responsiveness are prioritized, while VM remains beneficial for cases requiring strict isolation, enhanced configurability, or support for legacy components. These insights are expected to support educational institutions and system administrators in selecting an effective virtualization architecture for reliable, scalable, and performance-driven computer-based examination ecosystems.

Filter by Year

2020 2025


Filter By Issues
All Issue Vol 8 No 4 (2025): December: Intelligent Decision Support System (IDSS) Vol 8 No 3 (2025): September: Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System Vol 7 No 4 (2024): December: Intelligent Decision Support System Vol 7 No 3 (2024): Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS) Vol 6 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 5 No 4 (2022): Desember: Intelligent Decision Support System (IDSS) Vol 5 No 3 (2022): September: Intelligent Decision Support System (IDSS) Vol 5 No 2 (2022): June: Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: Intelligent Decision Support System (IDSS) Vol 4 No 3 (2021): September: Intelligent Decision Support System (IDSS) Vol 4 No 2 (2021): June: Intelligent Decision Support System (IDSS) Vol 4 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS) Vol 3 No 3 (2020): September: Intelligent Decision Support System (IDSS) Vol 3 No 2 (2020): June: Intelligent Decision Support System (IDSS) Vol 3 No 1, Maret (2020): Exper System, Decision Support System, Datamining Vol 3 No 1 (2020): March: Intelligent Decision Support System (IDSS) More Issue