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Journal of Computing and Informatics Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2808375X     DOI : -
Core Subject : Science,
Computer Science & IT
Fokus kajian Journal of Computing and Informatics Research mempublikasikan hasil-hasil penelitian pada bidang informatika, namun tidak terbatas pada bidang ilmu komputer yang lain, seperti: 1. Kriptografi, 2. Artificial Intelligence, 3. Expert System, 4. Decision Support System, 5. Data Mining, dan lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 3 No 1 (2023): November 2023" : 5 Documents clear
Combination of Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) in Determining the Best Cashier Sintaro, Sanriomi; Aldino, Ahmad Ari; Setiawansyah, Setiawansyah; Saputra, Very Hendra
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.969

Abstract

MOORA (Multi-Objective Optimization by Ratio Analysis) method is one of the multi-criteria analysis techniques used for alternative selection based on several different criteria or objectives. In the context of selecting the best cashier, by using the MOORA method to select cashiers based on several relevant criteria. While Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) is a method used to assess the importance of criteria relative to each other in the context of multi-criteria analysis. This method helps in determining the weight of criteria used in multi-criteria decision making. The combination of MOORA and PIPRECIA will produce the best cashier selection based on the criteria used. The results of the best cashier assessment ranking using the Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA) and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained results, namely for Rank 1 obtained by Rini Maya with a final value of 0.343.
Decision Support System for Determining the Best Coffee Shop Applying the OCRA Method using ROC Weighting Erlin Windia Ambarsari; Hetty Rohayani; Ade Irma Agustina Lubis; Ridha Maya Faza Lubis
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.970

Abstract

The place of coffee sales, or more commonly known as a coffee shop, not only offers coffee but also serves a variety of hot and cold beverages. Many individuals, especially young people and students, choose to spend their time in modern coffee shops to sit and relax. Currently, coffee shops are often used as places for discussions, exchanging ideas, or simply relieving stress after activities. Coffee shops have become centers of social interaction with adequate service facilities. Although coffee shops are widespread, many people are not careful in choosing them. When choosing a coffee shop, it is important to select one that not only provides a comfortable environment but also serves the best-tasting coffee. The process of choosing the best coffee shop involves considerations such as price, taste quality, service, atmosphere, and cleanliness. To address this challenge, the author deems it essential to implement a Decision Support System (DSS). DSS is a field of science that utilizes technology to assist in problem-solving and accurate decision-making, without being manipulable. In the context of this research, the author uses the OCRA and ROC methods, as both are known as objective and easily understood methods. By applying the OCRA and ROC methods, the research results show that Gen’s Semar Cafe, with a score of 1.594, is selected as the best coffee shop.
Analisa Metode Backpropagation Dalam Memprediksi Jumlah Perusahaan Konstruksi Berdasarkan Provinsi di Indonesia Muhammad Kurniawansyah; Rafiqotul Husna; Raichan Septiono; Agus Perdana Windarto; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.993

Abstract

This research aims to analyze the number of construction companies in Indonesia and gain an understanding of the trends and characteristics of the construction industry in that country. In this research, data related to the number of construction companies is analyzed using available sources such as government statistical reports, industry publications, and other secondary data sources. The data we use in this research is data on the number of construction companies by province in Indonesia from 2016-2021 which was taken from the website of the Central Statistics Agency (BPS) using the backprogation artificial neural network (JST) method. The analysis results show that the number of construction companies in Indonesia has increased significantly in recent years. It is hoped that this research will encourage strong economic growth and increasing investment in the infrastructure and property sectors has driven demand for construction services. In addition, government policies that support the construction sector, such as infrastructure development programs and regulations that facilitate foreign investment, also contribute to the growth in the number of construction companies. Apart from growth trends, this research also identifies several characteristics of the construction industry in Indonesia. The industry is dominated by small and medium-sized companies operating locally, although there are also large companies involved in large-scale projects. Competition in this industry is fierce, with companies vying to win construction contracts and develop a competitive advantage. The architectural models that we use in this research are 6 architectural models, of which the best architectural model will be obtained. The architectural models include 5-11-1-1 with an accuracy percentage of 61.8%, 5-12-1- 1 with an accuracy percentage of 70.6%, 5-14-1-1 with an accuracy percentage of 82.4%, 5-18-1-1 with an accuracy percentage of 64.7%, 5-20-1-1 with an accuracy percentage of 70.6%, 5-22- 1-1 with an accuracy percentage of 73.5%. So the best architectural model is obtained, namely the 5-12-1-1 model which produces an accuracy rate of 82.4%. with a Mean Square Error (MSE) of 0.00099997 with an error prone of between 0.001-0.05. These results are quite good in predicting the number of construction companies based on provinces in Indonesia
Implementasi Metode Tsukamoto Pada Sistem Pakar Diagnosa Kernikterus Defiyuliyanti Bazikho; Efori Buulolo; Meryance V. Siagian
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.1004

Abstract

Kernicterus is brain damage in infants due to high levels of bilirubin in the blood. Bilirubin is the cause of jaundice, and if left untreated, it can accumulate in the brain. The issue at hand is that the community, especially parents, still struggle to find a solution to determine the diagnosis of Kernicterus in a baby's body. Expert systems are advanced technology that can be used to address diagnostic problems with relevant accuracy. One of the expert system methods that can be employed for diagnosing diseases is Tsukamoto. Therefore, in this research, a expert system is built to obtain a Kernicterus diagnosis with relevant accuracy by implementing the Tsukamoto method to address the issues faced by patients. The results of this research show that testing the Tsukamoto method for diagnosing Kernicterus provides good accuracy. Hence, it can be concluded that implementing the Tsukamoto method in an expert system can be a solution to address the Kernicterus diagnosis problem
Implementasi Metode K-Medoids Untuk Clustring Penerima Bantuan Berdasarkan Normalisasi Data Masyarakat Miskin Dengan Metode Desimal Scaling Rispandi
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.1063

Abstract

The Social Welfare Office is the distributor of aid for the economically disadvantaged population in accordance with the regulations set by the Minister of Social Affairs of the Republic of Indonesia Number 20 of 2019. This aid is provided to the economically disadvantaged population selectively, not continuously, in the form of goods or cash, aiming to improve the welfare of the economically disadvantaged and socially vulnerable. The data of aid recipients from the economically disadvantaged population needs to be processed and normalized to obtain the desired information, facilitating the grouping of aid recipients at the Southeast Aceh Social Welfare Office. The aid recipients' data is processed and normalized to ease the grouping process using Decimal Scaling method, enabling the extraction of desired information. Subsequently, the data is clustered using the K-Medoids method to group aid recipients based on the normalized data, thus simplifying the identification of the most suitable aid recipients. This research employs a system capable of providing a solution for clustering aid recipient data using the K-Medoids method and the RapidMiner application

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