Journal of Intelligent Decision Support System (IDSS)
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.
Articles
12 Documents
Search results for
, issue
"Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS)"
:
12 Documents
clear
<![CDATA[Correlation between chord guitar and song year era using apriori algorithm ]]>
<![CDATA[Rifki Fahrial Zainal]]>;
<![CDATA[Arif Arizal]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.126
<![CDATA[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.]]>
<![CDATA[Solar-Powered smart irrigation and fertilization with loRa remote monitoring ]]>
<![CDATA[Santi Febri Arianti]]>;
<![CDATA[Antonius Antonius]]>;
<![CDATA[Daniel Simbolon]]>;
<![CDATA[Edwinner Lamboris Sitorus]]>;
<![CDATA[Erdianto Parluhutan Sitorus]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.145
<![CDATA[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.]]>
<![CDATA[Application of SMART and TOPSIS in determining beneficiaries of latrine construction assistance ]]>
<![CDATA[Apni Rahmadani Tanjung]]>;
<![CDATA[M.Fakhriza]]>;
<![CDATA[Aninda Muliani Harahap]]>;
<![CDATA[Nur Sakinah Tanjung]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.146
<![CDATA[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.]]>
<![CDATA[The application of particle swarm optimization (PSO) to improve the accuracy of the naive bayes algorithm in predicting floods in the city of Samarinda ]]>
<![CDATA[Faldi Faldi]]>;
<![CDATA[Trisha NurHalisha]]>;
<![CDATA[Wawan Joko Pranoto]]>;
<![CDATA[Hendra Saputra]]>;
<![CDATA[Asslia Johar Latipah]]>;
<![CDATA[Sayekti Harits Suryawan]]>;
<![CDATA[Naufal Azmi Verdikha]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.148
<![CDATA[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.]]>
<![CDATA[Optimization of K-Means algorithm in grouping data using the statistical gap method ]]>
<![CDATA[Alfiansyah Hasibuan]]>;
<![CDATA[Djubir R.E. Kembuan]]>;
<![CDATA[Christine Takarina Meitty Manoppo]]>;
<![CDATA[Medi Hermanto Tinambunan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.149
<![CDATA[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.]]>
<![CDATA[A systematic literature review of gray level co-occurence matrix on plants ]]>
<![CDATA[Anwar Sadad]]>;
<![CDATA[Ema Utami]]>;
<![CDATA[, Anggit Dwi Hartanto]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.153
<![CDATA[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.]]>
<![CDATA[Comparison of three fuzzy logic algorithm methods for cellular selection ]]>
<![CDATA[Gunawan Gunawan]]>;
<![CDATA[Wresti Andriani]]>;
<![CDATA[Sawaviyya Anandianskha]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.154
<![CDATA[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.]]>
<![CDATA[The utilization of drone emprit in seeing the trend of increasing oil fuel through social media data ]]>
<![CDATA[Wahyuddin S]]>;
<![CDATA[Zul Rachmat]]>;
<![CDATA[Abdillah Abdillah]]>;
<![CDATA[Andi Irfan]]>;
<![CDATA[Muhammad Idris]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.156
<![CDATA[This study investigates the use of drone emprit as a tool to monitor the trend of increasing fuel prices through analysis of data obtained from social media platforms using twitter. The aim of this study is to develop a new approach in understanding and forecasting fuel price fluctuations by utilizing widely available data on social media. The research combines drone emprit technology to get a visual picture of the situation at various fuel distribution sites, and integrates it with text and sentiment analysis taken from social media platforms. The methodology used includes visual data collection using drone emprit, collection of text data from social media platforms, and integrated data processing and analysis. The results of this study are expected to provide deeper insights into the factors influencing fuel price increases, including social and economic factors reflected in online conversations. By combining visual data and text analysis, the study contributes to the development of new methodologies for understanding and forecasting economic trends using innovative data sources.]]>
<![CDATA[The influence of system quality, information quality, and service quality on the net benefit of academic information systems with user satisfaction as an intervening variable ]]>
<![CDATA[Ivana Melinda]]>;
<![CDATA[Amelia Setiawan]]>;
<![CDATA[Samuel Wirawan]]>;
<![CDATA[Hamfri Djajadikerta]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[The increasing interest of young people to occupy tertiary institutions of education, the higher the level of competition that exists. Therefore, every tertiary institution is competing to improve the quality of education by developing Student Portal. This study aims to determine how the influence of system quality, information quality, and service quality on net benefits mediated by student portal user satisfaction. This research was conducted by collecting 59 primary data of respondents through a questionnaire. The analytical test tool used SmartPLS v.3.2.7 with the SEM (Structural Equation Modeling) analysis method. The results of this study indicate that the quality of information and service quality has an influence on student portal user satisfaction, user satisfaction has an influence on net benefits, while the effect of system quality on user satisfaction cannot be proven statistically. In this situation, system quality, information quality, and service quality have a weak effect on net benefits with user satisfaction as a mediating variable]]>
<![CDATA[Prototype temperature monitoring system for medicine refrigeration in the pharmaceutical installation ]]>
<![CDATA[Willy Willy]]>;
<![CDATA[Haryono Haryono]]>;
<![CDATA[Handri Santoso]]>;
<![CDATA[Ito Wasito]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35335/idss.v6i3.159
<![CDATA[Patient safety in a hospital is a healthcare service that is safe and non-harmful to patients. All components of healthcare services (doctors, nurses, and other health teams) in hospitals must be aware of and care about patient safety while in the hospital. One of the most important components is monitoring the temperature of the medicine refrigeration. Human resources or work overload often leads to the need for more monitoring of the temperature of medicine refrigeration. Therefore, IoT technology can be the solution to assist in monitoring the temperature of the medicine refrigerator. The method used in this study is observation. Based on the conducted research, it is proven that the IOT system for temperature monitoring can reduce missed temperature records. The results of this study indicate that the DHT22 sensors have good accuracy as they remain within the accuracy range of the room thermometer used as a reference, with a temperature reading accuracy of ±1oC and a maximum temperature measurement limit of 70oC. The data collection process uses the ESP8266 as the microcontroller, which is then connected to the DHT22 module as a temperature and humidity sensor and sends a database every 30 seconds. The real-time temperature and humidity measurement results can be viewed through mobile apps using the Flutter programming language and the website. If the temperature exceeds 8oC, the fan LED will automatically turn on and send notifications to WhatsApp registered using Python and Twilio. Furthermore, the existing data can be analyzed using a machine learning model, enabling the prediction of when the refrigerator will be damaged as a preventive measure]]>
