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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 7 Documents
 1 
Search results for , issue "Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)" : 7 Documents clear
Implementation of TOPSIS method in decision support system for used motorcycle purchase recommendation Putra, Muhammad Ridho Alghifari; Manurung, Jonson; Hidayati, Ajeng
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.289

Abstract

The selection of used motorcycles involves evaluating multiple criteria, such as price, production year, transmission type, vehicle type, mileage, fuel consumption, and engine capacity. This complex decision-making process often leads buyers to rely on subjective judgments or third-party recommendations, which may result in suboptimal choices. To address this issue, this research develops a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision Making (MCDM) method, which ranks alternatives based on their proximity to the ideal solution. The study introduces innovation by applying TOPSIS to the specific context of used motorcycle selection, providing a data-driven, objective approach in contrast to conventional methods. A quantitative approach was employed, with data collected from online marketplaces and authorized dealerships. The results indicate that the 2019 Honda Revo, priced at Rp. 8,600,000, is the most optimal choice, achieving the highest preference score of 0.862887804. The effectiveness of the TOPSIS method in structuring the selection process ensures a more systematic and accurate decision-making process. Furthermore, the study highlights the influence of key criteria, such as fuel efficiency and mileage, in determining the ranking of alternatives. Future research should focus on integrating additional factors, such as maintenance history and vehicle condition, and exploring the development of web-based or mobile platforms to improve real-world implementation and enhance user accessibility. This system contributes to smarter, more informed decision-making in the used vehicle market, offering a significant advancement over traditional selection methods.
Heart disease prediction using machine learning models Vernando, Deden; Manurung, Jonson; Saragih, Hondor
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.291

Abstract

Heart disease remains one of the leading causes of death globally, with mortality rates continuing to rise each year. Early detection is critical to reducing the burden of this disease; however, conventional diagnostic methods are often costly, time-consuming, and reliant on specialist expertise. This study aims to evaluate the effectiveness of four machine learning (ML) algorithms—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—in predicting heart disease using clinical datasets. The methodology involves data preprocessing, feature selection using the Random Forest algorithm, and performance evaluation through metrics such as accuracy, precision, recall, F1-score, and support. Experimental results indicate that KNN achieved the highest accuracy after feature selection, while SVM demonstrated the highest recall despite lower precision. RF offered the most balanced performance, making it a reliable model for real-world medical applications. These findings highlight the importance of selecting appropriate algorithms and features to improve the performance of predictive models. The study suggests that future research should incorporate larger datasets, apply systematic hyperparameter tuning, and explore deep learning techniques to further enhance prediction accuracy.
Segmentation of Waste Management of All Provinces in Indonesia Using K-Means Clustering Mad'hika, Yudha Randa; Pirman, Arif
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.296

Abstract

The amount of waste in Indonesia continues to increase along with the increasing population and welfare. Waste data there are so many waste data throughout Indonesia that it is difficult to determine which managed waste data from provinces will be taken so a recommendation is needed to determine it. Mapping waste management based on the results of waste managed into animal feed raw materials, compost raw materials, recycled raw materials, up-cycle raw materials and energy source raw materials is expected to help the government (or local government) make more appropriate policies. Therefore, this research uses a clustering method, namely k-means clustering. Based on the results of the analysis using the elbow method, the optimal number of clusters selected in this study is k=2. Next, the process of clustering managed waste is carried out using the K-Means clustering algorithm. The clustering results on waste management data display data information with a low level of proportion of waste management volume consisting of 28 provinces and a high level of proportion of waste management volume consisting of 6 provinces. Based on the evaluation of the k-means clustering results, the maximum value of the silhouette coefficient = 0.940 and the Davies-Bouldin index value = 0.430. The concrete recommendations are to make the province with the highest proportion of waste management as a pilot project for the construction of PLTSa, develop a Public-Private Partnership scheme for investment in waste-to-energy processing technology and accelerate licensing and local regulations that support the operationalization of WtE.
Digitalization of guest record management through a web-based information system to support security and efficiency Mawadah, Helvina Salsabila; Prabukusumo, M Azhar; Saputra, Bagus Hendra
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.297

Abstract

Digitization of visitor registration improves efficiency and security in visit management in many institutions. This project seeks to create a web-based information system that replaces manual registration techniques to reduce the possibility of data loss and inaccuracy of registration, while increasing the efficiency and accuracy of guest information processing. The study used a software development methodology that leverages the Rapid Application Development (RAD) concept, which facilitates rapid and adaptive system development. The novelty of this system lies in its integration of automated blacklist detection and personalized notification workflows, features that are not commonly found in prior visitor registration systems. The system was designed and implemented specifically within an institutional environment but has the potential to be generalized and adapted for diverse organizational contexts, including government offices, corporate facilities, and educational institutions. The study results show that the web-based information system improves efficiency in visitor registration through automated verification, real-time monitoring, and notification integration, thereby facilitating safer and more organized guest management. This solution aims to enable institutions to improve their visitor reception processes, strengthen workplace security, and facilitate digital transformation in visit management.
Comparison of coronary heart disease prediction using basic model and ensemble learning Rachmat, Rachmat; Iskandar, Syamsul Bhahri; Kasmawaru, Kasmawaru; Suherwin, Suherwin
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.298

Abstract

Coronary heart disease (CHD) remains one of the leading causes of death worldwide, highlighting the urgent need for accurate early detection. This study aims to compare the performance of various machine learning models—including Decision Tree, K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, XGBoost, and Stacking Ensemble—in predicting CHD using the UCI Heart Disease Dataset. The models were evaluated using five metrics: accuracy, precision, recall, F1-score, and AUC. The results indicate that Stacking and Logistic Regression achieved the highest AUC scores (0.80), while XGBoost obtained the best F1-score (0.40). Simpler models such as Decision Tree and KNN showed relatively lower performance. In addition, feature importance analysis using permutation methods revealed that features like number of major vessels (ca), thalassemia (thal), ST depression (oldpeak), and age play a critical role in prediction accuracy. These findings demonstrate that ensemble learning approaches, especially Stacking and XGBoost, can effectively improve diagnostic performance and offer strong potential for clinical decision support systems (CDSS) in the early detection of coronary heart disease.
Implementation of smoke detector system in household kitchen based on HTTP protocol with DHT22 and MQ135 sensor integrated with WhatsApp chatbot Aulia Desy Nur Utomo, Aulia Desy Nur Utomo; Feri Yasin; Muhammad Agung Nugroho
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.299

Abstract

This research develops an Internet of Things (IoT)-based smoke detection system using MQ135 and DHT22 sensors integrated with WhatsApp chatbot via HTTP protocol. The system is designed to provide real-time notifications related to air quality, including gas concentration, temperature, and humidity. The test results show accurate and stable sensor performance in detecting environmental parameters. Evaluation is done through Quality of Service (QoS) analysis based on delay, jitter, packet loss, and throughput parameters. The test shows a fairly high network stability with 0% packet loss and an average throughput of 2045.53 Kbps, which falls into the Excellent and Good categories. However, the average delay of 2173.65 ms and jitter of 254.11 ms were classified as Poor, indicating the need for improvement in the responsiveness aspect. This research is expected to contribute to the integration of air quality monitoring systems with practical and easily accessible instant messaging services. The system offers an innovative solution for real-time smoke detection and early warning. Limitations of this study include limited data size and room for improvement in the interpretability and scalability of the system for wider IoT implementation.
Redefining hash functions for quantum security with SHA 256 Riswantoro, Dadan Shavkat; Rimbawa, H.A Danang
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): 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.v8i2.301

Abstract

The rapid advancement of quantum computing technology presents a significant challenge to the field of cryptography, particularly affecting the security of hash functions that form the foundation of many cryptographic protocols. Hash functions are widely used to ensure data integrity, generate digital signatures, and securely store passwords. However, the emergence of quantum algorithms—such as Grover’s algorithm—threatens to undermine the security assumptions on which these hash functions are based by significantly reducing their effective security levels.  This paper aims to provide a comprehensive analysis of the vulnerabilities introduced by quantum computing to traditional hash functions, detailing how these weaknesses can be exploited by quantum adversaries. We explore the fundamental properties of hash functions, including pre-image resistance, second pre-image resistance, and collision resistance, and assess how these properties are affected in a quantum context. Furthermore, we examine the implications of these vulnerabilities for existing cryptographic systems and emphasize the urgent need for the development of post-quantum cryptographic standards. In response to these challenges, we review ongoing research efforts focused on designing hash functions that are resilient to quantum attacks. We evaluate several promising candidates for post-quantum hash functions, considering their security properties, performance metrics, and practical applicability. The findings of this paper highlight the necessity of transitioning to post-quantum cryptographic solutions to safeguard sensitive information in an increasingly quantum-capable world. Ultimately, we advocate for proactive measures within the cryptographic community to adopt and implement these new standards, thereby ensuring robust data security in the age of quantum computing.

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