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Journal of Systems Engineering and Information Technology

JOSEIT
Website

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 40 Documents

1 2 3 4

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

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

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

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

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%.

Optimization of Flood Prediction using SVM Algorithm to determine Flood Prone Areas Saruni Dwiasnati; Yudo Devianto
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022
Publisher : Ikatan Ahli Informatika Indonesia

Flooding is one thing that can slow down the economic pace in the affected area. Bandung is called the city of flowers and the city of fashion because the nickname makes Bandung a city with a variety of fashions growing in multiple places as a starting point for the buying and selling process. Not only did Bandung spawn fashions that became hits every year, but it also had many Meccas of traditional food preparation that were extraordinarily unique and interesting. Creating a flood-prone area model can make it easier to provide information for communities in Bandung Prefecture that belong to flood-prone and non-flood-prone areas. The SVM algorithm is a technique that can be used in the case of classification and regression, which is very popular lately. SVM is in a class with Artificial Neural Networks (ANN) in terms of features and conditions of problems that can be solved, and to be able to increase its accuracy it uses what can be optimized with PSO (Particle Swarm Optimization), where the test data is used BNPB official website data, BPS Bandung District and BMKG processed. The accuracy rate generated by using the SVM algorithm is 85.71% and the generated AUC is 0.841, while the accuracy rate generated by using the PSM-based SVM algorithm is 97.62%. and AUC produced at 1,000.

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

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.

Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity Fried Sinlae; Anugrah Sandy Yudhasti; Arief Wibowo
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022
Publisher : Ikatan Ahli Informatika Indonesia

The level of data accuracy in everyday life is necessary because it is reflected in the ever-advancing development of information technology. Analysis of data processing in information that can provide knowledge with the help of data mining systems. Algorithms commonly used for prediction are Naive Bayes and Decision Trees. The purpose of this study is to compare the Nave-Bayes algorithm and the decision tree algorithm in terms of the accuracy of predicting the productivity of the Weckerle machine at PT XYZ. The method used is a literature study from various related sources and understanding of the data in the source related to the subject of the classification method of the Naive Bayes algorithm and the decision tree into the data mining system. The results of this study are a classification using the Nave-Bayes algorithm with a higher level of confidence than the decision tree algorithm.

Application of Data Mining for Visit Prediction at Amikom Creative Economy Park Rumini; Norhikmah
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022
Publisher : Ikatan Ahli Informatika Indonesia

A creative economy park is a place designed with strategic goals for technology skills collaboration, information and knowledge transfer, creation of innovative high-tech enterprises and entrepreneurs, introduction of new technology industries in creative economy enterprises to promote economic development. Yogyakarta Amikom University has been declared a Creative Economy Park and is known as Amikom Creative Economy Park (ACEP). ACEP includes multiple multimedia environments for targeting businesses such as software development, film, television, games, radio, animation, advertising, investment consulting, and project design. Every year, the number of institutions visiting Amikom Yogyakarta University carries the slogan Amikom Creative Economy Park with a fairly busy program of visits. The agenda for accepting this visit was carried out by Amikom's Public Relations Department (DKUI, Directorate of Public Relations and International Affairs). The evolution of visitor numbers from year to year, forecasts must be made to support the planning and preparation process when receiving visits. This research will discuss the trend of visitors having a comparative study in Amikom Creative Economy Park in the future. The data used in this study is visitor data from January 2019 to December 2019. This predictive data analysis uses the Autoregressive Integrated Moving Average (ARIMA) method and Exponential Smoothing as a comparison for the accuracy of the prediction. With the forecast of this visit, the planning and preparation for the Directorate of Public Relations and International Affairs and for the University AMIKOM Yogyakarta is to be done.

Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison Galih
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v1i2.4942

Until now, many colleges are running to improve the quality of education to create a competitive environment. The wealth of data contained in the college can be put to good use according to the needs and processed into useful information to find out the relationship between the attributes of the data contained in it for analysis and the expected result in the form study achievements are related to study time, i.e. in adequate or late in the probable study period can be classified. Data mining, which refers to the analysis of data in the field of educational institutions, is also known as educational data mining (EDM). In the study conducted using two models of Naive Bayes Classifier i.e. Algorithms and C 4.5. The value of best accuracy in the Naive Bayes Classifier (NBC) algorithm model was 86.83% with a ratio of 80% training data, while in the model algorithm C 4.5 was 88.10% with a ratio of 90% training data. The application of EDM is expected to be maximized and developed so that it can contribute to the world of education and advance, especially in the field of data mining.

Fault Detection of Mechanical Equipment Failure Detection Using Intelligent Data Analysis Maksim Andreevich Kovito
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 2 (2022): September 2022
Publisher : Ikatan Ahli Informatika Indonesia

Poor maintenance of machinery in manufacturing plants has always been an important link in the production process. In addition to computer technology, artificial intelligence technologies and various intelligent sensors are widely used in manufacturing industries. The amount of data generated by production machines and equipment at all stages of the production process is also growing rapidly, and it is particularly important to analyze the data generated by these devices in order to detect and even predict malfunctions. Intelligent data mining provides advanced data analysis techniques for this purpose. This article introduces the basic concepts of data mining, its processes, the main data mining technologies, and provides recommendations for applying data mining to detect failures in devices.

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Contact Name
Ronal Watrianthos

Contact Email
ronal.watrianthos@gmail.com

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+6281263621335

Journal Mail Official
joseitjournal@gmail.com

Editorial Address
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/

Location
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Unknown

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Ronal Watrianthos

Contact Email ronal.watrianthos@gmail.com

Phone +6281263621335

Journal Mail Official joseitjournal@gmail.com

Editorial Address 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/

Location Unknown, Unknown INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Ronal Watrianthos

Contact Email
ronal.watrianthos@gmail.com

Phone
+6281263621335

Journal Mail Official
joseitjournal@gmail.com

Editorial Address
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/

Location
Unknown,

Unknown

INDONESIA