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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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Implementation of Data Mining to Predict Stocks of Goods Using the Apriori Algorithm at Mom's Kitchen Bakery Ruminse Situmorang; Fricles Ariwisanto Sianturi
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 3 (2020): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The application of the Apriori Data Mining Algorithm in predicting stock items can be used to predict what types of goods or brands should be owned or what stocks are right for use in Mom's Kitchen bakery. By utilizing software that is designed or a system that has been made to predict the stock of goods at Mom's Kitchen bakery is one of the right ways to find out what customers are interested in using the Apriori algorithm. The results of this study are, companies can more easily provide products that customers want more based on the stock that has been provided. The system built is web-based using php with MySQL database.
Implementation Of Data Mining In Determining Sales Pattern Of Snack Products Using Apriori Algorithm (Case Study: PT Siantar Top Tbk) Siti Patimah; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

PT Siantar Top.Tbk is a company engaged in snack manufacturing which is located at Jl Raya Medan Tebing Tinggi, Ujung Serdang, Tanjung Morawa, Deli Serdang Regency, North Sumatra. In the sales transaction data processing process at PT Siantar Top Tbk, it has not been able to provide accurate information about the pattern or relationship of a set of items purchased by customers. So that the company has difficulty knowing every product that is sold, because the sales data is always increasing, but the company does not understand how to manage the sales data of these snack products. Because the snack product sales data is only archived and not managed by the company to get new results. The purpose of this research is to design and build a priori algorithm in determining sales patterns. This system is designed using UML and is built with the programming languages ​​PHP, HTML, CSS, Javascript and Mysql as the database. Then the determination of the sales pattern of snack products that are successful every month at PT Siantar Top Tbk using the Apriori algorithm. A priori algorithm is a data mining technique to find associative rules between a combination of items.
Application of Data Mining in Determining Sales Patterns at 212 Mart Supermarkets in Lubuk Pakam Using the Apriori Algorithm Anzelia Anzelia; Penda Sudarto Hasugian
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Data mining is a search and analysis on a very large database so that an interesting pattern is found with the aim of extracting information and knowledge that is accurate and potential, as well as understandable and useful from a large database. In this final project, one of the data mining techniques is implemented, namely the a priori algorithmcan define the rule pattern of the data set. The data in this study aims to determine the pattern of sales at 212 mart supermarkets in Lubuk Pakam. This research is obtained from the results of transaction data in the form of consumer purchase receipts, the data used are 10 transaction data with 43 total products. From the results of manual calculations, there are 23 rule association results in accordance with the minimum limit values ​​for support and confidence that have been determined and the highest analysis results are found in french fries and chitato with a minimum support of 30% and 100% confidence. The results of the data mining process obtained can be used for the arrangement or arrangement of layout patterns that are adjusted to the association rules to suit consumer purchasing patterns.
Decision Support System for Selection of Thesis Advisors Supervisors Thesis Advisors according to the Field of Science Using the AHP Method Jelita Sari Simanungkalit; Hengki Tamando Sihotang
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Thesis is a scientific work and a condition that must be taken in order to achieve a bachelor's degree. Thesis supervisors play an important role because they have the responsibility and ensure that students are able to compile the thesis properly so that it is ready for testing and quality. In higher education, a system is needed that can help and ease a job, especially in the selection of thesis supervisors. In selecting the thesis supervisor, it is still based on subjectivity, which causes the title submitted by students to not match the field of science of the thesis supervisor so that the lecturer appointed to guide the topic of the title submitted by students has a different field of expertise from the student's title. In this determination, there are sometimes less than optimal decisions where the appointed lecturer is not in accordance with the thesis topic proposed by the student. This system is made using the AHP method so that the programmed system can be carried out more effectively. AHP method is one of the methods used in decision support systems with the calculation process by comparing each criterion.
Prediction of 2020 Mobile Sales Trends Using the Weighted Product Method Devi Permata Sari Sianturi; Jijon Raphita Sagala
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Mobile is a communication tool that has the ability like a computer with a variety of functions that is easy to carry anywhere and anytime.The current trend of cellphones is marked by the emergence of increasingly sophisticated types, models and brands with increasingly diverse features such as games, music, cameras, videos and social media. Therefore, nowadays there are many cellphone shops that sell cellphones, in addition to that the development of cellphones has made choosing a cellphone a long and complicated process to produce the best choice that suits your needs. This study aims to produce a decision for the selection of mobile phones using the weighted product method. Weighted product uses the multiplication technique to relate the attribute rating, where the rating of each attribute must be ranked first with the weight of the attribute concerned to produce the largest value that will be selected as the best alternative. The application of the Weighted Product method uses criteria, namely camera (C1), ram (C2), rom (C3), price (C4), weight (C5), and battery (C6). The results of this study indicate that the recommended alternative is A3, namely OPPO type OPPO A9 2020 with a V value of 0.093.
Decision Support System for Web-Based Employee Candidates at PT.Indomarco Prismatama Using the ELECTRE Method Raudhatul Hasanah Sinaga; Murni Marbun
Journal of Intelligent Decision Support System (IDSS) Vol 3 No 4 (2020): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Indomaret or PT.Indomarco Prismatama is one of the largest franchised minimarket companies in Indonesia which is engaged in services and sales. Indomaret is a subsidiary of the Salim Group. The number of Indomaret Stores continues to grow, so PT.Indomarco Prismatama will need a lot of employees so PT.Indomarco has opened job vacancies to meet the specified number of employees. In recruiting prospective employees, Indomaret takes a long time to recruit prospective employees because they have to make decisions fairly and appropriately. The purpose of this study is to design and build a decision support system for recruitment of prospective employees. This system is designed using UML and is built with the programming languages ​​PHP, HTML, CSS, Javascript and MySql as the database. Then determine the prospective employees who successfully passed the selection of employee admissions at PT.Indomarco Prismatama using the ELECTRE method. The ELECTRE (Elimination and Choice Expressing Reality) method is one of the methods of ranking concept decision making, namely by using pairwise comparisons between alternatives on the appropriate criteria. The research result obtained is the decision to determine the applicants who successfully passed the selection. Of the 5 samples of potential applicants, namely A1, A2, A3, A4 and A5 the applicants who successfully passed were A5 because they had the highest score compared to the other samples.
Expert System Diagnosing Diabetes Using the Web-Based Dempster Shafer Method Siti Ilya Suwella; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: 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.v4i4.31

Abstract

Diabetes is a very dangerous disease because diabetes can attack other organs of the body so that it can cause death. This is due to the lack of technology-based information that can help determine the symptoms of diabetes. With the limited number of diabetes experts, an expert system is needed to diagnose diabetes. The method applied to the system is the Dempster Shafer method, by determining 17 symptoms and 3 types of disease, as well as compiling a rule base in determining the relationship of each symptom so that the results in the calculation achieve 100% accurate results. This method has 5 steps in its completion. The results showed that the Dempster Shafer method could be used to diagnose the symptoms of diabetes. So that the existence of this system can help the wider community in finding information about the symptoms of diabetes.
Expert System Diagnosing Anxiety Disorder Using Based Naïve Bayes Method Danvy Nadhira; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: 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.v4i4.34

Abstract

Psychological problems such as Anxiety Disorder have become a common problem in the world. Most sufferers of this disease are under 45 years old on average and usually this disorder often attacks the mentality of women. However, this disease is often considered trivial and not many sufferers realize it. Not to mention the handling of course requires quite a lot of money. Another thing is the lack of information about this disease, which causes the risk of sufferers of Anxiety Disorder to increase every year. To solve existing problems, it can be solved by using computer technology in the health sector that can diagnose a disease like an expert, namely an expert system application disease symptoms. The purpose of this study is as a first step to treat this disorder as quickly as possible. The application of the Naïve Bayes method plays a role in being able to provide diagnostic results because the Naïve Bayes method is considered capable of providing the largest clarification of the value of v based on the selected symptoms.
Implementation Of Data Mining With C4.5 Algorithm For Determining The Home Industry Product Marketing Strategy Teresia Herniamwati Zebua; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: 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.v4i4.37

Abstract

me Industry is one of the SMEs that produce home-made products, such as pastries. Not all of these products are sold by consumers. This study uses a web-based C4.5 algorithm to determine marketing strategies for Home Industry products that are not selling well by classifying the indicators that most influence consumers in buying Home Industry products so that they can provide information about marketing strategies that will be carried out. Based on the results of the study, the highest gain value was the packaging attribute with a value of 1.86, so that the packaging attribute was used as the root in the formation of a decision tree. Then the second highest gain is the price attribute with a value of 1.26, and the third highest gain is the taste attribute with a value of 1.03, then the fourth highest gain is the service attribute with a value of 0.89.
Decision Support System in Determining the Location of Village Health Services (Puskesmas) in Pagar Merbau District Using the Profile Matching Method Wan Wimar Yahya; Fristi Riandari
Journal of Intelligent Decision Support System (IDSS) Vol 4 No 4 (2021): December: 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.v4i4.38

Abstract

Community health center (puskesmas), is a health service facility that organizes public health efforts and first-level individual health efforts, and prioritizes promotive and preventive efforts. In accordance with geographical conditions, area size, transportation facilities, and population density in the working area of the puskesmas so that the puskesmas services are accessible to the population, to assist the smooth process of services that will make it easier for the community. Therefore, to assist in determining an ideal puskesmas development location, a Decision Support System (DSS) is used, the process of calculating the results of determining the location of the puskesmas construction using the Profile Matching method. As for the alternatives of this study, 6 villages were taken, namely Tanjung Mulia, Sumberejo, Sidodadi, Pagar Merbau II, Sidoarjo Satu Pasar Miring, and Bandar Dolok. And from the results of calculations using the Profile Matching method, it displays the ranking of the results of determining the location, where the highest value is in Tanjung Mulia village with a value of 3.69 so that it can be used as a solution or consideration in determining the location of the puskesmas to be built.

Page 2 of 16 | Total Record : 157

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