cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Naety
Contact Email
jurnalmedicom@iocscience.org
Phone
+6281381251442
Journal Mail Official
jurnalmedicom@iocscience.org
Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia
Location
,
INDONESIA
Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute Of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS)" : 5 Documents clear
Optimization Of Determination Against K-Means Cluster Algorithm Using Elbow Creation Melda Pita Uli Sitompul; Opim Salim Sitompul; Zakarias Situmorang
Jurnal Teknik Informatika C.I.T Medicom Vol 14 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/cit.Vol14.2022.176.pp1-9

Abstract

Clustering is a data mining method for grouping data that have similar or different characters in each section. One of the methods is using K-Means by measuring the distance between clusters using the shortest distance or Euclidean Distance. K-means entails weakness, which is the determination of clusters in k-means clustering, resulting in the different data grouping and affecting the results of the data cluster distribution. To overcome this issue, the elbow creation method is employed to determine the similarity level in the cluster by observing the comparison between Root Means Square and R Square to measure the homogeneity and heterogeneity of the cluster where this method is applied by considering the changes in the comparison between the RMSSTD (Root Means Square Standard Deviation) and RS (R Squared) values which have the intersection of the RMSSTD and RSquared values. The difference between RMSSTD cluster 1 and RMSSTD cluster 2 was 0.066 and RS cluster 1 and RS cluster 2 was -0.304. Based on those figures, the highest difference was found in cluster 2. All considered, tourist destinations in East Asia frequently visited or interested to visitors are grouped into cluster 2, comprising criteria 6, 7, 8, and 10, or in other words, resort destination, picnic area, beaches, and religious institutions
PERAMALAN JUMLAH MAHASISWA BARU DENGAN PENDEKATAN REGRESI LINIER Yulia Utami; Desi Vinsensia; Aura Nissa; Sulastri Sulastri
Jurnal Teknik Informatika C.I.T Medicom Vol 14 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/cit.Vol14.2022.231.pp10-15

Abstract

Forecasting models are the result of developments in the field of science and technology that provide convenience in predicting future events. This paper aims to develop a linear regression model to predict the number of new students in the next year. The data to be used in this study is the total of students majoring in informatics engineering and information management during the last 5 years. Based on result obtained the number of student for department of Informatics Engineering is 198 people with a MAPE (Mean Absolute Percentage Error) score of 16.5%, and for the new students department of Informatic Management is 8 people with a MAPE score of 16.1%.
Implementation of The Naïve Bayes Method in the COVID-19 Self-Assessment of Cianjur Regency Government Officials Muhammad Nasir; Andy Ramadhany Rahayu; I Putu Robin Sunjaya; Mey Sri Widialestari; Agus Prayitno
Jurnal Teknik Informatika C.I.T Medicom Vol 14 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/cit.Vol14.2022.239.pp16-26

Abstract

The impact of Covid-19 in Indonesia has penetrated into all fields of human activity including in the government sector, efforts to implement work from home for government agencies, especially in the Cianjur district to suppress the positive number of COVID-19 have been carried out. However, in practice the determination of employees to work from home is not appropriate, resulting in a decrease in the performance of government employees in Cianjur Regency, and an increase in positive numbers in the government environment. The method used in this research is an expert system approach with Naive Bayes which is the fastest and most accurate classification method for determining the problem. Based on the classification of the Naive Bayes method, samples were taken from Cianjur Regency government employees with symptoms of fever, cough, muscle aches, and loss of sense of smell, they had the highest probability of being classified as unhealthy and eligible for a swab test compared to other classifications, which was 80% percent. An expert system with a naive Bayes approach can be implemented to determine the health status of Cianjur Regency employees related to Covid-19, the suitability of the swab test, and the determination to work from home. For further research, it is suggested that it can be integrated with the existing institution's attendance system, and if necessary it can be tested with other methods.
Implementasi Gain Ratio Pada Metode KNN Dalam Memprediksi Penjualan Sparepart Elektronik Pada Service Center Panasonic Lhokseumawe Samsul Bahri Siagian; Samsudin Samsudin; Muhammad Dedi Irawan
Jurnal Teknik Informatika C.I.T Medicom Vol 14 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/cit.Vol14.2022.242.pp36-47

Abstract

K-Nearest Neighbor is a good classification technique, but judging by previous studies, the accuracy of the KNN performance obtained is still inferior to other methods. in the classification process, if some characteristics are not good it can cause errors in the new classifier. As for this study, the researcher uses the gain ratio method as a parameter to see the correlation between each attribute in the dataset, and the gain ratio serves as a weighting for each attribute so as to produce a dataset. the correct way of classifying data using the KNN method, this study is very suitable for predicting sales of spare parts at the Panasonic Service Center company, where the company experienced a decline in sales, this research is very useful for predicting sales for the following month. The results of this study produce very precise predictions of distance with an accuracy value of 13%, where the comparison of the highest accuracy value is found in the total attribute with an accuracy distance of 13%, while the lowest accuracy difference is obtained in the month and type of sales dataset with 0.08%. the overall accuracy of all datasets increases by 100% with K=3, and K=5 gets 80% accuracy. so this method can be used to make sales predictions to make it easier for the company.
Completion of Multi-Criteria Decision Making Using the Weighted Product Method on the Server Maintenance Vendor Selection System Rini Nuraini; Dedy Alamsyah; Ri Sabti Septarini; Alfry Aristo J Sinlae
Jurnal Teknik Informatika C.I.T Medicom Vol 14 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/cit.Vol14.2022.247.pp27-35

Abstract

For companies that use information systems or websites in their business activities, server maintenance is an important thing. For this reason, the selection of a server maintenance vendor is crucial. Vendor determination usually begins with gathering information and holding a leadership meeting based on the assumptions of the decision maker. But this method is time consuming and less objective. Vendor selection is a multi-criteria problem where each criterion has a different importance. This can be solved by using the Multi-Criteria Decision Making (MCDM) approach. Weighthet Product (WP) is one of the methods of solving MCDM. The purpose of this research is to develop a decision support system to determine the best maintenance vendor using the Weighted Product (WP) method. The system is built using a waterfall system development approach that starts from analysis, design, coding and testing. The developed system has the ability to manage alternatives, criteria, alternative assessments, calculations with WP, and displays the best alternative results with WP. From the results of black-box testing, it shows that the developed system can function and run well. In addition, the results of manual calculations with the system show the same results.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol 15 No 2 (2023): May: Intelligent Decision Support System (IDSS) Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS) Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 13 No 2 (2021): September: Intelligent Decision Support System (IDSS) Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS) Vol 12 No 1 (2020): March: Intelligent Decision Support System (IDSS) Vol 11 No 2 (2019): Informatik Vol 11 No 1 (2019): Informatika Vol 10 No 2 (2018): Informatika More Issue