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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 6 Documents
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Search results for , issue "Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS)" : 6 Documents clear
The design of flood disaster monitoring dashboard for DKI Jakarta Via Angelika; Tony Tony; Manatap Dolok Lauro
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.129

Abstract

Disasters are events that threaten and disrupt the lives of living things caused by natural or non-natural factors or human actions resulting in human casualties, environmental damage, property losses, and psychological impacts. One of the natural disasters is flooding. This includes the disasters that often occur in the community in any area regardless of location and time. Flooding means excessive waterlogging, particularly those that often occur during the rainy season. The puddle arises due to increased water flow above the ground surface, either due to high rainfall or overflow of river water. Information on flood incidence is recorded daily in detail. This information allows flood incidence data to be made into a visualization. This dashboard is designed to display a dashboard that can show visualization of flood incident data. The data employed data on DKI Jakarta flood incidence from 2018 to 2021. The dashboard design used the prototyping method. Visualizing the data aims to facilitate users in accessing information about flood incidents in DKI Jakarta. The results obtained by the dashboard will display reports of an annual increase or decrease in flood incidents.
Marketing information systems in the digital era (a literature review study) Hadi Kurniawanto; Hafidz Hanafiah
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.134

Abstract

The marketing information system is part of a management information system focusing on marketing. This information system is essential, especially in the digital era that depends on technology. In this research, we will focus on the trend of how marketing information systems are an option for some companies and the customer's perspective. This study uses qualitative studies from various recent journals. The study results show that most marketing information systems are still used to support the company's business with web-based information systems, with e-commerce as an option compared to Android-based ones. The other variables are still talking about customer satisfaction and loyalty. Use this information system used by several business fields, cooperatives, banking, property, entrepreneurship, tourism, agriculture, and sales as a decision by considering efficiency and effectiveness as well as long-term risks and effects.
Robust mathematical model for supply chain optimization: A comprehensive study Lise Pujiastuti; Mochamad Wahyudi; Barreto Jose da Conceição; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.137

Abstract

This research provides a comprehensive review of existing literature and research on supply chain optimization, aiming to capture the advances made in the field and identify emerging perspectives. Supply chain optimization plays a vital role in improving operational efficiency, reducing costs, and enhancing customer satisfaction. By analyzing a wide range of studies, this review examines various approaches, models, and techniques used in supply chain optimization, including mathematical programming, stochastic programming, simulation, and metaheuristic algorithms. The review also encompasses key aspects such as demand forecasting, inventory management, production planning, transportation, and distribution network design. Furthermore, the study investigates recent trends, such as incorporating sustainability considerations, addressing uncertainties and risks, and utilizing real-time data and decision support systems. By identifying the gaps and limitations in the existing research, this review sets the stage for future investigations and provides valuable insights for researchers and practitioners seeking to advance supply chain optimization efforts. The findings of this review contribute to enhancing the understanding of supply chain optimization and provide a roadmap for future research directions in this dynamic and critical field
Improving longitudinal health data analysis with stochastic models for predicting disease trajectories and optimizing treatment strategies Nur Hasanah; Nanarita Tarigan; Siskawati Amri; Siti Saodah; Sapnita Sapnita
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.141

Abstract

Longitudinal health data analysis helps diagnose and treat disease. Traditional deterministic models fail to represent longitudinal data's unpredictability and uncertainty, limiting their forecast accuracy and decision-making capacities. This research improves Longitudinal Health Data Analysis by adding stochastic models for disease trajectories and therapy optimization. The research begins with a stochastic model that accounts for the complicated dynamics of illness progression and therapy responses. This model captures individual variability and probability outcomes using patient-specific factors, features, and treatment information. Numerical examples demonstrate the model's practicality. The numerical example shows that the stochastic model may forecast illness trajectories and optimize treatment choices. The model predicts illness development probabilistically, helping understand disease dynamics and identify high-risk patients. Simulating and probabilistically estimating therapeutic interventions optimizes treatment options. Personalized therapy decision-making improves patient outcomes. Longitudinal Health Data Analysis should use stochastic models, the study suggests. These models improve disease prediction, therapy optimization, and personalized healthcare decision-making by capturing variability and uncertainty. Advanced modeling methodologies and real-world data validation are next. The research could change illness management and clinical care
Optimizing maternal and child health services with operations research techniques approach Fitri Andriani; Setia Sihombing; Sapnita Sapnita; Tri Suci Dewiwati
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.144

Abstract

Operations research is used to optimize mother and child health services through appointment scheduling and resource allocation. Public health is reflected in maternal and child health. Maternal and infant death rates remain a global issue despite medical advances. These issues stem from mother and child health service inefficiencies and poor care. This study uses operations research to improve healthcare delivery and patient outcomes.The study begins by identifying maternal and child health service issues such high wait times, insufficient resource allocation, and poor appointment scheduling. It then creates a mathematical formulation model that encompasses healthcare system intricacies including patient flow, resource use, and appointment scheduling. Linear programming, simulation, queuing theory, and data analytics enhance patient scheduling for varying medical urgency levels and time needs. A numerical illustration illustrates the mathematical formulation model. Patient wait times, resource allocation, and service efficiency improved significantly. Early time slots favor patients with higher medical urgency, ensuring timely healthcare treatments. Optimized resource use prevents overcrowding and ensures appointment equity. Stakeholder engagement and collaboration with healthcare practitioners, administrators, policymakers, and others are stressed throughout the study process. Key stakeholders can adjust proposed solutions to mother and child health service requirements and obstacles, improving acceptance and feasibility. This research advances operations research-based mother and child health service optimization. Data-driven decision-making and creative approaches aim to improve mother and child health service delivery, resource usage, and patient outcomes. Global mother and child health initiatives and sustainable development goals might benefit from evidence-based policy decisions and healthcare management solutions.
Naïve bayes on diagnostic expert system for menstrual disorders Adie Wahyudi Oktavia Gama; I Nyoman Gde Artadana Mahaputra Wardhiana
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : 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.v6i2.130

Abstract

Menstrual disorders often occur in women in their active reproductive period. This disorder is caused by various factors such as hormonal, ovarian, hypothalamus, and other factors. Thus, it can be stated that the causes of menstrual disorders are very broad and varied. Lack of public knowledge and awareness about women's reproductive health can have serious consequences for sufferers, such as difficulty getting pregnant, infertility, tumors, and even cancer. To be able to help people with menstrual disorders quickly and efficiently, an expert system is needed to make an initial diagnosis of menstrual disorders. In addition to helping the community, expert systems can assist experts or medical personnel in determining the initial diagnosis/anamnesis so that the evaluation of abnormal uterine bleeding can result in appropriate treatment. In this study, researchers built an expert system with the Naïve Bayes web-based method to get an initial diagnosis in the form of a percentage of possible diseases suffered by users based on the selected symptoms. By testing the system, it can be concluded that the system built by applying the Naïve Bayes method can accurately diagnose types of menstrual disorders with a percentage of 84% based on data and symptoms experienced by patients. Based on other tests, the system functions as it should, and the community considers the system acceptable, good, and proper.

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