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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 157 Documents
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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
Correlation between chord guitar and song year era using apriori algorithm Rifki Fahrial Zainal; Arif Arizal
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.126

Abstract

There are there are more and more varieties of music, especially if you made the music with guitar. There are also many and variety key combinations for the guitar chord. This is usually taken into consideration by beginners who are just learning to play the guitar to make their own music. Music also must made by feeling for the tone itself, and everyone has a different feel. Beginners usually see references from existing songs to made their own music. They usually make it in any key or chord that they want. But they also need inspiration or suggestions for the next key or chord to use from the key or chord they specified. In this study, we propose a way for beginners to find a combination of chords that can be used to make their own first music. From the results of this study, it was found that of the many songs in the database that were released in the 1990s to 2000s, most of them used three combinations of chords Am, Em and G. These three combinations were the combinations that most often appeared in songs. These three keys can become the user's favourites to be used as a basis for making songs or just to find inspiration from songs from the 1990s to 2000s.
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.
Solar-Powered smart irrigation and fertilization with loRa remote monitoring Santi Febri Arianti; Antonius Antonius; Daniel Simbolon; Edwinner Lamboris Sitorus; Erdianto Parluhutan Sitorus
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.145

Abstract

Efficient water management is a critical challenge in agriculture, particularly in rural areas where water resources may be scarce. To address this issue, this research introduces and assesses a solar-powered automatic drip irrigation system with a fertilization feature, specifically tailored for chili farming in the rural community of Banua Huta village. The system incorporates LoRa technology for remote monitoring, allowing farmers to efficiently manage water use and nutrient application. The study focused on evaluating the system's performance concerning water conservation, fertilizer application, and crop productivity. The results demonstrated a substantial improvement in irrigation efficiency, with water usage reduced by 33.5% compared to conventional methods. The fertilization feature of the system not only facilitated targeted nutrient delivery but also resulted in increased crop growth rates of up to 30% and improved leaf health by up to 35%. This innovative solar-powered automatic drip irrigation system showcases a practical and sustainable approach to water and nutrient management in rural agriculture, demonstrating its potential for widespread adoption in similar settings.
Application of SMART and TOPSIS in determining beneficiaries of latrine construction assistance Apni Rahmadani Tanjung; M.Fakhriza; Aninda Muliani Harahap; Nur Sakinah Tanjung
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.146

Abstract

In Bangun Raya Village there are still many people who defecate in the open (BABS) because they do not have latrines, resulting in an increase in disease transmission in Bangun Raya Village. To deal with this incident, the government of Bangun Raya Village provided assistance in building latrines to the less fortunate with predetermined criteria. However, the determination of beneficiaries for the construction of latrines is still based on manual calculations. The first thing the author did was to collect data from the poor family cards directly. To avoid mistakes in providing assistance for latrine construction, a decision support system is needed that can be used by the village apparatus in processing data. So that residents who receive assistance are residents who really need it and with the construction of a computerized decision support system, the decision making regarding the provision of latrine assistance can be more effective and efficient. By combining two methods, namely the SMART method as the stage for assessing the weight of the criteria data obtained and the stage for calculating the relative value of the assessment of weights and the TOPSIS method as the stage for normalizing the final result of calculating the relative value and the stage for ranking the results of normalization. The results of this study resulted in the Hilaluddin Harahap house in hamlet 2 being selected as the location for the construction of a 1st rank latrine with an accuracy value of 96% based on the desired criteria.
The application of particle swarm optimization (PSO) to improve the accuracy of the naive bayes algorithm in predicting floods in the city of Samarinda Faldi Faldi; Trisha NurHalisha; Wawan Joko Pranoto; Hendra Saputra; Asslia Johar Latipah; Sayekti Harits Suryawan; Naufal Azmi Verdikha
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.148

Abstract

This study focuses on the implementation of Particle Swarm Optimization (PSO) to enhance the accuracy of the Naive Bayes algorithm in predicting floods specifically in the city of Samarinda. The aim is to improve the efficiency and precision of flood prediction models in order to mitigate the impact of flooding in the area. The results of this research highlight the effectiveness of PSO in optimizing the Naive Bayes algorithm, showing promising potential for more accurate flood prediction and proactive measures in Samarinda. The accuracy value obtained from testing using the Naive Bayes method alone is 91.12%. However, there is an improvement in accuracy after conducting testing with the optimization technique based on Particle Swarm Optimization (PSO) and the Naive Bayes algorithm. The conducted testing achieved an accuracy value of 94.38%. This accuracy result is higher compared to testing without optimization.
Optimization of K-Means algorithm in grouping data using the statistical gap method Alfiansyah Hasibuan; Djubir R.E. Kembuan; Christine Takarina Meitty Manoppo; Medi Hermanto Tinambunan
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.149

Abstract

In this study, we study the core concepts of the K-Means algorithm, explore its algorithmic framework, computation steps, and practical applications. Using data that is used as a basic need to perform calculations from the k-means algorithm optimization method. Using data taken from the r studio dataset with the EuStockMarkets dataset. The purpose of this study is to optimize the k-means algorithm and cluster the clustering process from a dataset, minimizing the objective function that has been set in the clustering process. The tools used are R Studio. Based on the results of this study, profiling of each group formed can be carried out. Based on the grouping results that have been carried out, the grouping results are 75.7% the accuracy of the statistical Gap method in optimizing clusters from existing datasets and the results of 92.9% are obtained from the results of minimizing the object functions in the dataset from grouping with k-means. The smaller the percentage in this grouping process the better it is in optimizing the clusters from the dataset. The author applies the k-means clustering algorithm to minimize objects for grouping from the EuStockMarkets dataset which consists of 4 variables. And the author uses the Statistical Gap method to optimize the clusters from the dataset.
A systematic literature review of gray level co-occurence matrix on plants Anwar Sadad; Ema Utami; , Anggit Dwi Hartanto
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.153

Abstract

The Gray Level Co-Occurrence Matrix method includes contrast, correlation, energy and homogeneity then is processed using an artificial neural network method for its classification. This literature tries to learn about the process of the GLCM method. This is done to understand the methods that researchers use to collect data from various sources, process the data that has been collected, and classify the data so that it becomes information that is easier to understand. researchers collect, screen, and review the research found using a Systematic Literature Review approach. Researchers pooled research from ScienceDirect, Google Scholar, and Elsevier by selecting studies published from 2020 to 2023. The purpose of the researchers conducting this literature review was to understand the GLCM method in parks, gain an understanding of data collection techniques, methods, and study the results of the research. previously. This study collects and summarizes 12 studies. The study was conducted regarding the method of data collection, the methods used, and the results of the research.
Comparison of three fuzzy logic algorithm methods for cellular selection Gunawan Gunawan; Wresti Andriani; Sawaviyya Anandianskha
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : 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.v6i3.154

Abstract

Many cell phone types are on the market today, increasingly making users feel confused and confused about choosing the cell that suits their needs. As one of the most essential needs at this time, users must be able to match their cellular needs with their income. Many smartphone products are offered. To help users in this study using three methods from the Fuzzy Logic algorithm for Decision Support Systems in choosing cellular according to their needs and desires; from the research that has been done, it is found that using the Fuzzy Tsukamoto method the accuracy is better than Mamdani which is equal to 0.02135, Mamdani is as large as 0.0643, while Sugeno is 0.1007. The cellular chosen is the Samsung A73 brand.

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