cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Marsono Marsel.
Contact Email
idss@iocspublisher.org
Phone
+6281381251442
Journal Mail Official
idss@iocspublisher.org
Editorial Address
Romeby Lestari Housing Complex Blok C Number C14, North Sumatra, Indonesia
Location
Unknown,
Unknown
INDONESIA
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
 10   11   12   13   14 
Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency Surorejo, Sarif; Mutaqin, Ahadan Fauzan; Kurniawan, Rifki Dwi; Gunawan, Gunawan
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.v7i2.231

Abstract

This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image Nugroho, Bangkit Indarmawan; Aziz, Taufiq; Santoso, Nugroho Adhi; Gunawan, Gunawan
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.v7i2.233

Abstract

Mango is a fruit crop favored by the community, especially the people of Probolinggo. The most widely planted types of mangoes in the Probolinggo area are Saruman is, golek, and manalagi mangoes because they taste good. This study uses mango leaves as a dataset of three types of mangoes: arumanis, golek, and manalagi. Various ways can be done to distinguish mango types, one of which is by looking at the shape and texture of the mango tree leaves. Suppose you look at the data in the field. In that case, the shape and texture of the leaves of Saruman, golek, and manalagi mangoes have many similarities, making it difficult to distinguish with the naked eye. This research aims to classify mango types based on leaf shape and texture using the K-Nearest Neighbor method. The shape feature extraction process uses compactness and circularity methods, while the texture feature extraction process uses energy and contrast from the co-occurrence matrix approach. The classification method used is K-Nearest Neighbor. The test results of shape feature extraction took 0.043 seconds and texture 0.053 seconds
Application of sma method and ahp to predict the level of tidal flood vulnerability in Tegal City Nugroho, Bangkit Indarmawan; Farkhan, Muhammad; Anandianskha, Sawaviyya; Gunawan, Gunawan
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.v7i2.235

Abstract

This study examines the application of the Simple Moving Average (SMA) and Analytic Hierarchy Process (AHP) methods to predict tidal flood vulnerability in Tegal City. The objective is to develop a more accurate prediction method for tidal flood vulnerability. The methods used are a combination of SMA and AHP. The results indicate that this combination is effective in producing more accurate predictions compared to conventional methods. Villages such as Muarareja, Tegalsari, Mintaragen, and Panggung have been identified as highly vulnerable and require more intensive mitigation. The implications highlight the importance of a multi-method approach to understanding complex phenomena like flood vulnerability. For future research, it is recommended to integrate real-time weather data and consider socio-economic factors to enhance accuracy and relevance in disaster mitigation. The findings are expected to assist in better urban planning and resource allocation, as well as improve community resilience against tidal flood disasters.
Customer segmentation in sales transaction data using k-means clustering algorithm Nugroho, Bangkit Indarmawan; Rafhina, Ana; Ananda, Pingky Septiana; Gunawan, Gunawan
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.v7i2.236

Abstract

Customer segmentation against sales transaction data using K-Means clustering algorithm. The purpose of this research is to develop and validate a customer segmentation model using an optimized K-Means clustering algorithm to enable more accurate customer grouping based on sales transaction data. The methodology used includes quantitative design combined with experimental techniques, quantitative data analysis, and model validation, where rice sales transaction data from Tegal city traditional market is processed to identify customer segments. The results showed the effectiveness of the optimized K-Means algorithm in grouping customers into three clusters based on purchase characteristics, and C4-SUPER rice proved to be the best-selling among consumers. These insights enable the development of more targeted and personalized marketing strategies, enrich the academic literature on customer data analysis, and move towards the practical application of more effective customer segmentation through the use of advanced analytical technologies
Anomaly detection in network security systems using machine learning Santoso, Nughroho Adhi; Lutfayza, Rezi; Nughroho, Bangkit Indarmawan; Gunawan, Gunawan
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.v7i2.238

Abstract

Anomaly Detection in Network Security Systems Using Machine Learning highlights the importance of developing effective models for data security. This research aims to develop an adaptive and automated anomaly detection model using the Naive Bayes algorithm and cross-validation. The methodology applied includes security log data collection, data preprocessing, implementation of Naive Bayes algorithms, and model evaluation using metrics such as accuracy, precision, recall, and F1-score. The results showed that the developed model was able to achieve high accuracy in detecting anomalies, with significant performance in identifying real threats without negative errors. The implication of this research is the improvement of network security through the application of machine learning, providing practical solutions for practitioners to deal with increasingly complex cybersecurity challenges
Application of artificial neural network method for early detection of dengue fever Surorejo, Sarif; Ningrum, Isna Lidia; Ananda, Pingky Septiana; Gunawan, Gunawan
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.v7i2.240

Abstract

Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications
Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces Murtopo, Aang Alim; Januarto, Sigit; Anandianskha, Sawaviyya; Gunawan, Gunawan
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.v7i2.242

Abstract

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
Identification of vacant land in Tegal Regency using cnn algorithm based on goolge earth imagery Andriani, Wresti; Fatkhurrohman, Fatkhurrohman; Gunawan, Gunawan
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.v7i2.243

Abstract

This research developed a Convolutional Neural Network (CNN) algorithm to identify vacant land in Tegal Regency using imagery from Google Earth. By utilizing labeled imagery datasets, CNN models are optimized to recognize texture characteristics, colors, and distribution patterns of vacant land. Preprocessing and image sharing techniques are applied to improve model quality. The results of this study offer a new methodology in visual data processing for accurate and efficient identification of vacant land, providing a solid basis for more sustainable and efficient land use policies. This research contributes significantly to the scientific literature and field practice, particularly in natural resource management and regional planning
Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction Surorejo, Sarif; Ubaidillah, Muhamad Rizal; Syefudin, Syefudin; Gunawan, Gunawan
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.v7i2.245

Abstract

The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
Application of deep neural network with stacked denoising autoencoder for ECG signal classification Gunawan, Gunawan; Aimar Akbar, Aminnur; Andriani, Wresti
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.v7i2.247

Abstract

Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis. This study aims to develop a robust model that enhances the classification of ECG signals by effectively denoising the input data and extracting rich feature representations. The research employs a method involving data preprocessing, feature extraction using SDAEs, and classification with a deep neural network (DNN) validated on the MIT-BIH Arrhythmia Database. The results demonstrate that the proposed model achieves an impressive accuracy of 98.91%, significantly outperforming traditional machine learning methods. The implications of this research are substantial, offering a reliable and automated tool for arrhythmia diagnosis that can be utilized in clinical settings to improve patient care. The study highlights the model's potential for real-time clinical application, although further validation on more extensive and diverse datasets is necessary to confirm its generalizability and robustness. This research contributes to the field by integrating advanced SDAEs with deep learning, paving the way for more accurate and efficient ECG signal classification systems

Page 12 of 16 | Total Record : 157

 10   11   12   13   14 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 8 No 3 (2025): September: Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System Vol 7 No 4 (2024): December: Intelligent Decision Support System Vol 7 No 3 (2024): Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS) Vol 6 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 5 No 4 (2022): Desember: Intelligent Decision Support System (IDSS) Vol 5 No 3 (2022): September: Intelligent Decision Support System (IDSS) Vol 5 No 2 (2022): June: Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: Intelligent Decision Support System (IDSS) Vol 4 No 3 (2021): September: Intelligent Decision Support System (IDSS) Vol 4 No 2 (2021): June: Intelligent Decision Support System (IDSS) Vol 4 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS) Vol 3 No 3 (2020): September: Intelligent Decision Support System (IDSS) Vol 3 No 2 (2020): June: Intelligent Decision Support System (IDSS) Vol 3 No 1, Maret (2020): Exper System, Decision Support System, Datamining Vol 3 No 1 (2020): March: Intelligent Decision Support System (IDSS) More Issue