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Ronal Watrianthos
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ronal.watrianthos@gmail.com
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Professional Organization - Ikatan Ahli Informatika Indonesia (IAII) / Indonesian Informatics Experts Association Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu 12540 South Jakarta - Indonesia http://iaii.or.id/
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Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 1 No 1 (2022): March 2022" : 5 Documents clear
Determination of Sub-Contractors Using AHP and SAW Method Arif Zikri; Adam Syahputra Cristanto; Imelda Imelda
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (555.607 KB) | DOI: 10.29207/joseit.v1i1.1834

Abstract

One factor in the durability of a contractor company is its sub-contractor. The management of sub-contractors for companies engaged in construction services is one aspect of the company's durability to be able to complete the project that the selection is doing manually by viewing one single file That is sent by the company who wants to be a sub-contactor, it is of course besides very troublesome also inefficient and time consuming quite a while. In determining the winner of the Procurement Committee is still having difficulty to determine the standard of the company that will become a sub-contractor, not to mention the assessment is subjective so that the elected candidate is not the best candidate. The methods used in this research use Analytical Hierarchy Process (AHP) for weighting of criteria and for calculation of value using Simple Additive Weighting (SAW) of the results of the research obtained from the SAW method used Obtained by the best sub-contractor of PT. 4Cipta with a total of 0.962 from 5 alternatives listed
Implementation of Analytic Network Process Algorithm in E-Lowker System Herry Derajad Wijaya; Wawan Gunawan
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (880.739 KB) | DOI: 10.29207/joseit.v1i1.1901

Abstract

Job vacancies information system at this time that there are still many companies that use the manual method to get the best employees and the number of job applicants who have not used the internet as a medium of information, but in this case the research aims to facilitate companies in getting the best employees, namely using the ANP Method in a web-based job information system and facilitate job applicants in utilizing internet media as a source of information that has been provided by the company. The ANP method is to find the weighted sum of the performance ratings for each alternative on all attributes, in this study the programming languages ​​used are PHP, MySQL database, CSS and Java Script. The criteria used in this study are education, experience, age and expertise. In this study shows that the Implementation of the ANP Method in the Web-Based Job Vacancy Information System is able to produce the best prospective workers for the company in accordance with the criteria
Comparative Analysis of Deep Learning Models for Vehicle Detection Rendi Nurcahyo; Mohammad Iqbal
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.663 KB) | DOI: 10.29207/joseit.v1i1.1960

Abstract

Deep Learning techniques are now widely used instead of traditional Computer Vision. There are many Deep Learning model algorithms for each use case such as Object Detection has several models, including Faster R-CNN, SSD, and YOLO v3. The performance and results of each Deep Learning model have advantages and disadvantages. Therefore, we must determine which model is suitable for the use cases and datasets that we have so that we can make the best Deep Learning model. Based on this need, this paper will make a comparative analysis of the Deep Learning model for Vehicle Detection (the spesific of Object Detection) from the models mentioned, namely, Faster R-CNN, SSD (Single Shot Detector), and YOLO v3 (You Only Look Once) to see the advantages and the disadvantages and which ones are the best. And after a comparison, it was concluded that of the three models mentioned only YOLO v3 model is able to be used as real time detection because it has low latency due to YOLO v3 only performs single convolution process so that it makes the process simpler and faster without reduce the accuracy.
Implementation Counting and Yolo Object Detection Methods for Identification Degree of Road Saturation Rico Aditya Utama
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (637.479 KB) | DOI: 10.29207/joseit.v1i1.1965

Abstract

Identification of road conditions such as congestion at this time still requires manual efforts in getting results. The congestion parameters certainly need to be monitored, especially in crowded areas and big city business centers, such as on Jalan Jendral Sudirman, Central Jakarta. Many parameters to identify the level of road congestion, one of them is by observing the value of the degree of road saturation. The purpose of this study is to propose a system that can calculate the value of the degree of saturation quickly and accurately using a camera. The method that is being proposed is to combine the Computer Vision with YOLO Object Detector techniques based on Deep Learning and the Object Counting method to get the value of traffic flow in the observed area. The results obtained by this system are quite good, this is supported by the error value obtained by the system around 3-4%.
Implementation of Decision Tree for Making Decision of Claim Product from Steel Production Anissa Lestari; Saruni Dwiasnati
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.299 KB) | DOI: 10.29207/joseit.v1i1.2233

Abstract

Product Claims is requests from consumers for products purchased from suppliers in accordance with agreements agreed by both parties. Products that have been claimed from consumers produce historical data sets that can be used as evaluations for producers to produce higher quality products. This study aims to process production data and shipment data then classify the types of products claimed based on the results of claim report from consumers. Data mining can be extracted information from a very large amount of data with specific methods to obtain information or new science. The method used in this study is the C4.5 algorithm method using the production code attribute as a claim or non-claim label attribute. This study produced a decision tree of 4 variables, there are thick of product, width of product, weight of product, destination of product, and type of product claim as label. This decision tree concept collects data which then calculates the value of entropy and gain to determine the rule. The conclusion from this study is the C4.5 algorithm helps classify the product claims and form a decision tree that can provide information about production results and can ensure with consumers related to product limits that may be claimed according to the agreed agreement. Evaluation of the results obtained that the algorithm C4.5 is 99.9% accuracy.

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