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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 4 (2025): December: Intelligent Decision Support System (IDSS)" : 5 Documents clear
Development of an IoT-Based Smart Hydroponic Monitoring System as a Modern Agricultural Solution in Remote Areas Laia, Firdaus; Laia, Finis Hermanto; Telaumbanua, Setia Murni; Sitorus, Halasan Pardamean; Hondro, Fernando Jofannikus
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.v8i4.318

Abstract

This research was motivated by the low agricultural productivity on Nias Island caused by limited land availability, inadequate infrastructure, restricted access to modern technology, and highly variable climatic conditions. This study aims to develop an Internet of Things (IoT)-based smart hydroponic monitoring system capable of automatically monitoring key parameters such as temperature, pH, water quality, and nutrient levels in real time, integrated with mobile applications and cloud platforms for remote accessibility. The research methodology consists of needs analysis, system design, prototype implementation, testing, and performance evaluation. The test results demonstrate that the developed prototype operates effectively, with sensor accuracy showing an average error of 3.74% for TDS, 1.28% for pH, and 0.91% for temperature measurements, while actuator testing confirmed 100% correct system response to nutrient and pH control scenarios. The system also maintained stable data transmission to mobile and cloud platforms without significant latency. These findings indicate that the proposed IoT-based monitoring system can significantly improve the efficiency and productivity of hydroponic agriculture, particularly in resource-limited regions such as Nias Island. The study provides empirical evidence of the system’s performance, reinforcing its potential as a practical solution for modernizing agriculture in remote areas.
Strategy for preventing human trafficking through verification of online job vacancies in Indonesia: English Passu Beta, Arga Husein; Rimbawa, H.A. Danang; Heikhmakhtiar, Aulia Khamas
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.v8i4.324

Abstract

This study addresses the rise of online job ads used to recruit victims of human trafficking (TPPO). We propose a practical screening approach that combines automated checks with human moderation. The goal is not to prove crimes, but to prioritize high-risk ads for fast review and referral. Using a public dataset of 500 job postings (fake_job_postings_500), we clean the text and basic metadata, extract simple text features (TF–IDF), and add light verification signals (e.g., contact and firm consistency). We then train two models in a leakage-safe pipeline: calibrated Logistic Regression (LR-cal) and Random Forest (RF). Performance is evaluated with standard accuracy measures ROC-AUC, PR-AUC, F1 plus calibration (how well risk scores match reality) and triage metrics that reflect real operations: precision for the highest-risk group, recall for all medium-and-above risk, and the share of ads moderators must review. Results show LR-cal is accurate and well-calibrated (5-fold means: ROC-AUC 0.993, PR-AUC 0.986, F1 0.934). In triage with thresholds T_high = 0.80 and T_med = 0.50, LR-cal yields Precision@High = 1.00 and Recall@≥Med=0.925 with ~34% of ads needing review. RF reaches near-ceiling accuracy (1.00/1.00 at ~35.3% workload) but requires careful calibration and leakage auditing. Practical contribution: AI-assisted, risk-based gatekeeping can reduce exposure to Human Trafficking or TPPO at the source. We recommend: (1) adopting calibrated models with adjustable thresholds; (2) standard operating procedures (SOPs) for cross-platform verification, including Know Your Customer (KYC) and Open-Source Intelligence (OSINT) checks; and (3) direct integration with official reporting channels to escalate flagged ads swiftly.
Generative AI and multi-source intelligence for automated security triage Herris, Fhatur Robby Tanzil; Saragih, Hondor; Anindito, Anindito
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.v8i4.326

Abstract

Security Operation Center (SOC) analysts encounter significant delays due to "Swivel Chair Analysis," a manual and fragmented process for triaging Indicators of Compromise (IoC). This study addresses this inefficiency by developing "CyberGuardianBot," an automated ChatOps assistant built using the Rapid Application Development (RAD) methodology and the Telegram Bot API. Applying Security Orchestration, Automation, and Response (SOAR) principles, the system asynchronously orchestrates multi-source intelligence from VirusTotal, AbuseIPDB, URLScan.io, AlienVault OTX, and MobSF. A key novelty is the integration of Google Gemini to perform cognitive synthesis, translating raw API data into actionable insights. Blackbox testing validated the system across 15 test cases, confirming the successful automation of URL, IP, and file triage. The bot generates natural language executive summaries and structured reports (.txt and .pdf), significantly enhancing the speed and accuracy of the triage process while reducing the cognitive load on analysts.
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.

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