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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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Web & android based CCTV maintenance application at PT. CCTV Palace Harefa, Brian Spencer; Dewi, Rofiqoh
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.206

Abstract

PT. Istana CCTV operates in the field of sales and services. Products sold include finger print, CCTV, access doors, hotel locks and so on. The services provided are product installation, maintenance and repairs. The problem that occurred was PT. Istana CCTV is unable to respond to customers' CCTV maintenance requests because the number of technicians is limited. On the other hand, PT. CCTV Palace often complains that CCTV maintenance requests are not immediately contacted and have to wait for the maintenance schedule. Therefore, an appropriate method is needed to increase sales turnover so that PT. CCTV Palace is making progress. The application of the FCFS method is very appropriate in Customer CCTV Maintenance services. Consumers who are the first to book CCTV Maintenance will be served first. The FCFS method is a scheduling algorithm with the characteristics of prioritizing processes that are submitted first, first come first served. So, the process that arrives first will be executed first.
Machine learning algorithm-based decision support system for prime bank stock trend prediction Gunawan, Gunawan; Budiono, Wahyu; Andriani, Wresti; Naja, Naella Nabila Putri Wahyuning
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.207

Abstract

In the complex landscape of financial markets, predicting bank stock trends is a critical aspect that supports more accurate investment decision-making. This study aims to develop and evaluate machine learning algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—for predicting the trends of major bank stocks in Indonesia using the IDX-PEFINDO dataset from January 1, 2020, to December 31, 2023. The adopted methodology includes collecting historical data, initial processing, feature selection, and training and validating models using evaluation metrics such as Accuracy, Precision, Recall, F1-Score, MAE, and RMSE. Results indicate that although no single algorithm is dominant, SVM and ANN perform better within the given data context. This research underscores the importance of a tailored approach to maximize the potential of machine learning algorithms in stock prediction, providing new insights into developing decision support systems for bank stock investments. This study implies that it recommends the integration of broader economic indicators and the exploration of advanced machine-learning techniques to enhance stock prediction accuracy in the future.
Application of fuzzy logic method to determine the level of damage to buildings in elementary schools Fahirah, Dwi Fina; Anandaianskha, Sawaviyya; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.208

Abstract

This study aims to provide information effectively and efficiently about the condition of damage to elementary school buildings to assist schools in determining decisions about buildings in schools that must be repaired immediately. In research, the application of fuzzy logic methods to assess the extent of damage to elementary school buildings will depend heavily on the research design, processes, and data used to develop and test fuzzy logic models. This approach involves reviewing, synthesizing, and evaluating a variety of literature sources, including scientific journals, books, conference papers, articles, and other written sources. It is important to consider that the value and novelty of a study can be assessed by the scientific community and practitioners in related fields, and the results will depend on the ability of that research to make a meaningful contribution. The value of applying fuzzy logic methods to determine the extent of damage to elementary school buildings will provide a more complete picture of the contribution of such research to science and practice in the field concerned.
Classification of fresh chicken meat and tainted chicken meat using naive bayes classifier algorithm Zain, Ahmad Muzakky; Ali Murtopo, Aang; Fadila, Nurul; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.212

Abstract

This research discusses the classification of fresh and tainted chicken meat using the Naive Bayes Classifier (NBC) algorithm based on Gray Level Co-occurrence Matrix (GLCM) feature extraction, with the aim of developing an efficient and accurate classification method. This research aims to utilize image processing and machine learning technologies to distinguish fresh chicken meat from tainted ones, which is crucial for the food industry. The research methodology involved the use of GLCM for texture feature extraction from chicken meat images, with the implementation of the NBC model through RapidMiner, offering an intuitive and efficient approach. The results showed the success of the model in achieving 80% accuracy, with an average precision of 81.25%, recall of 80%, and F1-score of 80.62%, confirming its ability in chicken meat classification. The integration of GLCM and RapidMiner in the application of NBC not only improves accuracy and objectivity in chicken meat classification but also provides a foundation for the wider application of machine learning techniques in ensuring food safety and consumer satisfaction
Application of nearest neighbor interpolation method and naïve bayes classifier for identification of bespectacled faces Setiawan, Dodi; Gunawan, Gunawan; Zaenul Arif
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.213

Abstract

Facial identification has become necessary in the era of advanced technology, especially in security and human-computer interaction. However, accessories such as glasses often complicate the identification process. This research aims to develop a facial identification system that can recognize bespectacled individuals with high accuracy, overcoming the limitations of conventional facial recognition technology. The method combines nearest neighbor interpolation to improve image quality and Naïve Bayes classification to distinguish between bespectacled and non-spectacled faces. The results showed that the developed model effectively identified bespectacled subjects with a high recall rate, although accuracy and precision still needed improvement. The implications of this research are significant for the field of biometric security and facial recognition, offering new solutions for more inclusive and adaptive facial recognition systems and opening up opportunities for further research in method optimization and dataset quality improvement.
Application of the viola-jones algorithm method to recognize faces of Stmik Tegal students Azmi, Muchamad Nauval; Nugroho, Bangkit Indarmawan; Septiana, Pingky; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.214

Abstract

This study examines the application of the modified Viola-Jones algorithm for student facial recognition at STMIK YMI Tegal, aiming to improve the efficiency and safety of the student attendance system. By adapting the algorithm to address the challenge of facial recognition accuracy from different angles and lighting conditions, a quasi-experimental quantitative design involved collecting data through photographic sessions with student subjects, followed by preprocessing to improve the quality of the analysis. The modification was evaluated for its ability to handle variations in facial and lighting conditions, showing significant improvements with 60% accuracy and precision, recall, and an F1-score of 71.43%. These findings demonstrate the effectiveness of the modification in improving facial recognition, potentially contributing significantly to attendance management and safety practices in educational settings. This research not only strengthens the existing literature.
Application of fuzzy expert system method for early detection of dengue fever Prayoga, Alan Eka; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: 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.v7i1.217

Abstract

The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.
Android based letter recognition application with augmented reality implementation Pramarta, Pandhu; Irfan, Irfan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): 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.v7i2.222

Abstract

Recognition of letters is an important basis in the process of learning to read in children. The use of Augmented Reality (AR) technology in education offers interactive methods that can increase interest and effectiveness of learning. This research aims to develop an Android-based application that uses AR to help children recognize letters. This application was developed using Unity 3D with the help of Vuforia SDK which allows effective implementation of AR. The methods used in this research include literature study, application design and development, as well as evaluation through technical testing and user testing. Testing is conducted to assess the functionality, user engagement, learning outcomes, and technical performance of the application. The results showed that the AR application was successful in improving letter recognition skills among children, with a high level of engagement and positive feedback from users. Although the application shows good performance on high-spec devices, technical challenges such as lag and frame rate drops on low-spec devices require further optimization. This research confirms the potential of AR as a valuable learning tool, especially in elementary education. The implications of this study suggest that further development and integration of AR technology in educational curricula can significantly improve the teaching and learning process, especially in facilitating distance learning and more immersive and interactive learning experiences. Further research is needed to explore possible applications of AR technology in broader educational contexts. Keywords: Augmented Reality, letter recognition, Unity 3D, Vuforia, children's education, learning applications.
Introduction to types of motorized vehicles based on shape and model using convolutional neural network based on digital images Hurairah, Wahu Abi; Mmurtopo, Aang Alim; Fadilah, Nurul
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): 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.v7i2.225

Abstract

The process of classifying images of different vehicles is an interestingchallenge for research. The process of classifying different vehiclesis widely used in various things such as electronic ticketing, e-parking andother fields. One method often used in the classification process is the Convolutional Neural Network (CNN) method. The CNN method is widely used toperform the classification process because it has been tested and proven to beeffective in image processing and pattern recognition. By classifying differentvehicles, CNN can automatically extract features from image data and detect complexpatterns. The CNN method provides high efficiency and accuracy in classifyingvarious vehicles for various practical applications such astraffic management and license plate recognition systems. The studyperformed motor vehicle image recognition by determiningthe types of two-wheeled vehicles (motorcycles) and 4-wheeled vehicles (cars) using a combination of Otsu threshold and CNN method. From the results of the research, two types of vehicles can be well identified, showing the confidence level of the classification process. of.).
Digital Transformation of Village Finance: Web-Based SISKEUDES Design for Enhancing Transparency and Accountability in Naru Village, Bima Regency Mawansyah, Julfikar
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): 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.v7i2.229

Abstract

This research aims to evaluate the implementation and effectiveness of the web-based Siskeudes application in Naru Village, Bima Regency, in enhancing transparency and accountability in village financial management. The research method used is a mixed-method approach, combining qualitative and quantitative analysis. Through literature review, needs analysis, as well as system design and development, this research creates an integrated and user-friendly web-based Siskeudes prototype. System testing is conducted from unit testing to user testing, followed by pilot implementation in Naru Village and comprehensive evaluation. HR training is also a focus of the research to strengthen the village staff's capacity in operating the application effectively. The research concludes that the implementation of web-based Siskeudes is effective in enhancing transparency and accountability in village financial management, while HR training supports the optimal use of the application. Thus, this research makes a significant contribution to improving transparent and sustainable village financial governance.

Page 11 of 16 | Total Record : 157

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