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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 6 Documents
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Search results for , issue "Vol 1 No 2 (2022): September 2022" : 6 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.147 KB) | DOI: 10.29207/joseit.v1i2.1995

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.093 KB) | DOI: 10.29207/joseit.v1i2.3439

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.991 KB) | DOI: 10.29207/joseit.v1i2.4941

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (216.325 KB) | DOI: 10.29207/joseit.v1i2.4943

Abstract

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.
Analysis Of the Behavior of Cyberattacks on Online Services Using the Cyber Threat Classification Isaev Sergey Vladislavovich; Kononov Dmitry Dmitrievich
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 | Full PDF (405.923 KB) | DOI: 10.29207/joseit.v1i2.4944

Abstract

The paper contains a study of the dynamics of attacks on online services using the categorization of cyber threats by type in the corporate network of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences. The study was conducted using online service logs and allows solving pressing issues related to ensuring the built-in security of web services, such as: identifying both current and future cybersecurity risks. A summary of the most important logging and analysis techniques is provided. The authors describe the nature and content of the data sources and the software used. The extensive observation period of the study is one of its outstanding features. The structure of the processing system is provided and software tools for attack analysis and categorization are created. The paper shows that using categorized sampling allows for the detection of periodicity and the identification of patterns in specific types of attacks. A correlation matrix was created based on the type of attack. Except for Command Injection, Directory Browsing, and Java Code Injection attacks, which can be aggregated, the research found that most attack types had poor correlation. Based on the classification of cyber threats, the authors proposed a heuristic technique of risk comparison.

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