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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 5 Documents
 1 
Search results for , issue "Vol 5 No 1 (2022): March: Intelligent Decision Support System (IDSS)" : 5 Documents clear
The Application of C4.5 Algorithm to Prediction Sales at PT. Sumber Sayur Segar Fadhila Fadhila; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): 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.v5i1.45

Abstract

Fresh vegetables, fruits and fresh meat are one of the basic needs for human life. The need for fresh vegetables, fruits and meat is one of the most important factors for buyers before making a purchase transaction. Likewise with the needs of fresh vegetables, fruit and meat needed by restaurants, cafes, hospitals, hotels and so on. With the increasing number of requests from customers for the needs of fresh vegetables, fruit and meat, companies engaged in the supply and sale of these necessities need to record sales transactions so that there are no stock vacancies and excess stock of goods. Therefore, companies must be more careful in providing fresh vegetables, fruits and meat which are in great demand, so it needs a data processing in the form of data mining using the C4.5 algorithm. In this study, the predicted sales transactions are the last three months of January, February and March 2021. Then for the sales prediction criteria used are in the form of price, type of goods, type of unit and month of sale so that from these criteria can be obtained sales transactions that are selling or not selling. Data mining is a process of mining important information from a very large data. While the C4.5 algorithm is a data classification that has numeric and categorical attributes, where the results of the classification process in the form of rules can be used to predict the value of discrete type attributes from new records. The system was built using the PHP programming language and MySQL as the database. This study obtained predictive results which were implemented in the form of a decision tree, namely the category of types of vegetables belonging to the best-selling sales transactions.
Expert System to Diagnose Eye Disease Due to Frequently Using Computer with Bayes Theorem Method Taozara Laia; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): 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.v5i1.46

Abstract

Eye disease due to frequent use of computers is one of the dangerous diseases in health because if not treated quickly it will result in blindness. These eye diseases can be diagnosed through the symptoms that arise in humans or through their clinical picture, through these symptoms an expert system can be made to make a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The expert system made in carrying out the diagnosis uses the Bayes theorem method to calculate the probability of an event occurring based on the influence obtained from the results of observations and experts. The system was built using PHP and MySQL programming as a database. The method used for tracing is Bayes' theorem. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules regarding the eye disease suffered and information about possible treatments that can be carried out as well as treatment solutions.
Lean Approach for Waste Reduction in Production Line by Integrating DMAIC, VSM, and VALSAT Method (Study Case: Assembling Bracket Manufacturing Automotive Industry) Muhammad Kholil; Adizty Suparno; Sulaiman Bin Haji Hasan; Rizki Aprilia
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): 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.v5i1.30

Abstract

The automotive manufacturing industry is a manufacture that produces four-wheeled automotive accessories parts. One of the products is the audio unit and bracket. This research aims to reduce waste in production and reduce cycle time, affecting increasing production output to adapt it to customer needs. Waste identification by integrating lean six sigma, namely DMAIC, VSM, and VALSAT. Based on the analysis results, it was found that the four biggest wastes were motion, inventory, waiting, and process. After carrying out the repair activities, the cycle time decreased at station three from 704 seconds to 246 seconds, thereby increasing the capacity efficiency from 75% to 91 %.
Expert System for Diagnosis of Sexual Diseases (Paraphilia) Using Method Dempster Shafer Nince Rianto Gulo
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): 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.v5i1.48

Abstract

Sexual behavior disorder (paraphilia) is sexual behavior that is unacceptable in society. The lack of knowledge and the many obstacles to getting information about early and independent detection of the community about sexual deviations and ignorance of the community in responding to deviations are also factors that cannot be handled early The main symptoms of this paraphilia disease are repeated childhood traumatic, coming from a family that is too restrictive or too disruptive to children in sexual behavior, and has a disorder in sexual relations since childhood. There are several types of sexual disorders (paraphilia) including Voyeuristic, Exhibitionistic, Frotteuristic, Sexual Masochim, Sexual Sadism, Pedophilic, Fetishistic and Transvestic. The Dempster Shafer method provides space for the expert in providing the value of trust in his knowledge. Based on the problems above, a Web-based system will be built that functions to help the community conduct independent consultations about disturbances that can be accessed with a wide range and unlimited time. The system built using PHP and MySQL programming as a database. The method used for tracing is the dempster shader. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules about the disorder suffered and information about possible treatments that can be done.
Expert System to Diagnose Bonsai Plant Pests with Certainty Factor Method Michael Michael
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 1 (2022): 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.v5i1.77

Abstract

Bonsai ornamental plants are dwarf plants which are generally planted in shallow pots. The main element in bonsai is harmony between pots and plants, in addition to harmony between pots and plants there are also other elements such as trunk size, tree height, root distribution, twigs, and leaf size as well as diseases and pests that attack the bonsai plants need to be considered. The difficulty of the community in conducting consultations and the unavailability of funds and time, so that the community has never conducted consultations about diseases and pests that attack their bonsai plants to experts or plant extension workers from the relevant agencies. Therefore, it is necessary to take action to anticipate the increasing number of bonsai farmers who do not consult on diseases and pests of bonsai plants. This is due to the delay in the diagnosis of the disease. The unavailability of experts or plant extension workers who are close, the community does not have time to conduct consultations and requires money. An expert system is a computer-based system that uses knowledge, facts, and techniques and reasoning in solving problems that can usually only be solved by an expert in the field. The Certainty Factor method is efficient enough to be used in diagnosing the diseases and pests of the bonsai plant. This system can provide early diagnosis of diseases and pests on the bonsai plant based on the symptoms and intensity of the symptoms that are visible from the outside. Users only need to enter the Web to take the first step in solving bonsai plant diseases and pests and choose the symptoms of the disease they are experiencing without having to ask an expert directly. This system is made web-based with PHP programming language and MySQL database.

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