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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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Application of machine learning for election data classification in Tegal city based on political party support Andriani, Wresti; Gunawan, Gunawan; Naja, Naella Nabila Putri Wahyuning; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers
Development of optimization formula for Neural Network-Based Automatic Control System in manufacturing industry Handayani, Yuni
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.271

Abstract

The manufacturing industry faces great challenges in improving the efficiency and effectiveness of automated control systems. This research aims to develop a neural network-based optimization formula that can overcome the limitations of conventional control methods. The method used in this research is gradient descent optimization applied to an objective function with certain constraints. The results show that this optimization method is effective in achieving the optimal value of ? that is close to the target with high precision, while the control variable ? remains stable throughout the iterations. The implication of this research is the improvement of the reliability and stability of automatic control systems in the manufacturing industry, which has the potential to significantly increase productivity and operational efficiency. Thus, this research makes an important contribution to the field of control system optimization and opens up opportunities for further development with the integration of more sophisticated optimization techniques.
Evaluation of ARIMA model performance in projecting future sales: case study on electronic products Simanungkalit, Erwinsyah
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.274

Abstract

Sales of electronic products are highly influenced by various internal and external factors, which require accurate prediction models to support strategic decision making. This study aims to evaluate the performance of ARIMA models in projecting future sales with a case study of electronic products, using monthly sales data collected from company reports and industry databases. The methods used include checking the stationarity of the data using the Augmented Dickey-Fuller (ADF) test, applying differentiation if necessary, and selecting ARIMA parameters based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis. The results of the analysis show that ARIMA models successfully capture seasonal and trend patterns, with performance evaluated using accuracy metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The implications of this study suggest the importance of considering external factors in modeling to improve prediction accuracy, as well as exploring other modeling approaches that can be more responsive to changing market dynamics.
Development of a web-based citizen contribution management system to optimize the financial management of community associations Rizal, Fathur; Fani, Ubed Dwi; Sonda, Bima Tsaqif Syahhama; Hakam, Syaif; Urip, Muhammad; Fuadz Hasyim
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.279

Abstract

This research aims to develop a web-based citizen contribution management system at D'Tar Residence to improve financial efficiency and transparency. Manual management of citizen contributions often causes transparency problems and delays in payment recapitulation. This research method uses the Waterfall system development model which includes the stages of needs analysis, system design, implementation, testing, distribution, and maintenance. Data collection was carried out through interviews, observations, and literature studies involving housing association administrators. This application was developed using the PHP programming language with the CodeIgniter framework and MySQL database. System testing was carried out using the Black Box Testing method to ensure that the application functions according to residents' needs. The results of the study indicate that the developed application can improve the efficiency of citizen contribution management and provide more transparent access to financial information. However, there are three features that require further refinement. Overall, this application is expected to encourage citizen participation in decision-making related to housing finances and increase satisfaction with the financial management of the association.
Analysis of student sentiment towards the quality of final project guidance using the Support Vector Machine Algorithm Wijaya, Andi; Anan, M. Rifqi; Maulidi, Minan Fikri; Maulana, M. Farhan; Purnomo, Hadi; Arifin, Zainal
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.280

Abstract

This research aims to analyze student sentiment towards thesis guidance services at Nurul Jadid University (UNUJA) using sentiment analysis methods with the Support Vector Machine algorithm. Thesis guidance services play a crucial role in shaping high-quality and competitive human resources within the university environment. However, students' sentiment assessments of these services are often complex and may differ from the perspectives of their advisors. The research approach used is quantitative analysis by collecting student feedback data through questionnaires and interviews. The text data from student responses is then processed to clean and format the data before being implemented with the Support Vector Machine algorithm. This algorithm will classify the sentiment into positive, negative, or neutral groups based on the information contained in the text responses. Based on the results of the conducted study, using the Support Vector Machine (SVM) method for sentiment analysis of thesis guidance quality at Nurul Jadid University, this study achieved an accuracy of 87%, precision of 88%, recall of 87%, and an F1 score of 86%.
Design analysis of monitoring and inventory management at Public Elementary School Kalikajar Wetan Paiton Bambang, Bambang; Shudiq, Wali Ja'far; Sholehah, Aisyatus; Fawaid, Izzah Diyanah; Maghfirah, Lidya Erika
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.281

Abstract

This study aims to analyze and design an effective monitoring and inventory management system at Public Elementary School Kalikajar Wetan, Paiton. A well-structured inventory management system is crucial to ensure efficient asset management, reduce the risk of loss, and improve operational effectiveness. The research methods employed include direct observation, in-depth interviews with school staff, and a literature review on inventory management systems. The analysis of the existing system revealed several shortcomings in the manual recording method, such as data inaccuracies, potential duplication of information, and delays in inventory reporting. As a solution, this study proposes the design of a digital system featuring automated recording, real-time data updates, and organized data access. This system design is expected to enhance efficiency, accuracy, and transparency in the school's inventory management. The findings indicate that implementing the proposed system supports faster decision-making, minimizes errors, and provides convenience in monitoring overall assets. Thus, the design holds significant potential as a model for inventory management in other educational institutions.
Classification of Corn Seed and Cob Quality Based on Texture and RGB Color Features Using Backpropagation Method Pawening, Ratri Enggar; Zaskiya, Karina Desy; Hasanah, Syarifatul
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.282

Abstract

The quality of agricultural products, such as corn, is greatly influenced by various factors, both environmental factors and agricultural engineering factors. This post-harvest quality affects product performance and is in line with consumer satisfaction, so it will greatly affect its selling price. Manual corn quality grouping requires a lot of time and effort. This study aims to develop a method for classifying corn kernel and cob quality based on digital image processing, using RGB color and texture features. The dataset consists of 150 corn kernel images divided into two quality categories, namely "good" and "bad". The research process involves preprocessing stages, color feature extraction using RGB color space, and texture features using the Gray Level Co-occurrence Matrix (GLCM) method. The classification model is built using the Backpropagation artificial neural network algorithm. The test results show that this method is able to achieve classification accuracy of up to 75%. The implementation of this method is expected to increase the efficiency of the corn quality selection process, reduce dependence on manual assessment, and provide significant benefits to the agricultural sector, especially farmers and the corn industry. These findings provide an important contribution to the development of digital-based post-harvest technology in Indonesia.
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.

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