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Ainul Hizriadi, S.Kom., M.Sc.
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Data Science: Journal of Computing and Applied Informatics
Published by Universitas Sumatera Utara
ISSN : 25806769     EISSN : 2580829X     DOI : -
Core Subject : Science,
Computer Science & IT
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
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Articles 6 Documents
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Search results for , issue "Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 6 Documents clear
Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning) Patria, Harry
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-8309

Abstract

The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers with a low frequency of previous fraud. With the data imbalance, traditional machine learningis likely to produce false negatives so that they do not accurately predict potential fraud. This study optimizes the machine learning approach based on Neural Networks to improve model accuracy through the integration of KNIME and Python Programming with KERAS and TensorFlow models. The study also conducts a comparative analysis to scrutinize the performance of Adam and Adamax Optimizer. Using data from European cardholders in 2013, this study proves that workflows and neural network algorithms can detect with up to 95% accuracy even with a very small fraud sample of only 0.17% or 492 of 284,807 transactions. In addition, the Adam optimizer performs higher accuracy than the Adamax optimizer. The implication is that this supervisory technology innovation can be developed to minimize transaction crimes in the financial services sector.
Application of Nonlinear Autoregressive Neural Network Model to Forecast Local Mean Sea Level Chi, Yeong Nain
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-8975

Abstract

The primary purpose of this study was to apply the nonlinear autoregressive neural network to model the long-term records of the monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana, as extracted from the National Oceanic and Atmospheric Administration Tides and Currents database. In this study, the empirical results revealed that the Bayesian Regularization algorithm was the best-suit training algorithm for its high regression R-value and low mean square error compared to the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for the nonlinear autoregressive neural network. Understanding past sea levels is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the existing data should be able to improve our understanding and significantly narrow our projections of ffuture sea-level changes.
Decision Support System for Election of OSIS Chair for Muhammadiyah Schools Using the Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) Method Winda Suci Lestari Nasution; Patriot Nusa
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-9071

Abstract

OSIS (Organisasi Intra Sekolah) is an official Student Council, which found in every school in Indonesia. The problem to be solved in a school is the need for an OSIS as a forum for students in schools to achieve the goals of coaching and developing students in accordance with the school's vision and mission. The main task of OSIS task is to achieve the goals in accordance with the school's vision and mission, therefore OSIS chair should have competencies and skills. The right decisions are needed for the implementation of the school's vision and mission. This first stage of research is performed by doing interview and survey to determine the criteria of OSIS chair. Based on interview and questionnaire has indicate that the student council member elects the chair based on several criteria consist of managerial ability, responsibility, communication and cooperation as well as discipline. The method proposed in the selection of the OSIS chairperson using simple multi attribute rating technique exploiting rank (SMARTER) approach and using Rank Order Centroid (ROC) weighting. The result of this study indicates that 75% of OSIS coaches and members need a decision-making system that can assist OSIS in making computerized decisions in determining the next OSIS chair candidate.
The Development of an Android-Based “LaporKPS” Application to Support the Service Center for Reports of Sexual Violence and Harassment Cases Adhim Bagas Wisnu Aji; Eko Supraptono
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-9092

Abstract

Cases of sexual violence and harassment have recently occurred among the public. Many print and non-print media report on issues of sexual violence and harassment. The PPP case is an abuse of the relationship between women and men which harms one of the parties because they are harassed or degraded in dignity, either verbally or non-verbally. Semarang was the highest rank in cases of sexual violence in Central Java in 2018. The PPP case reporting mechanism requires victims to go to a service center. Many risks make victims reluctant to report the incident that occurred because they are embarrassed in society and afraid that perpetrators will intimidate them. This paper aims to develop an android-based application to summarize the reporting mechanism by reporting PPP incidents experienced by victims using gadgets. Several menus provided include reporting menu, counseling menu, article menu, and Robo menu to identify categories of forms of harassment based on user input. The application is developed by using a customized waterfall method. Several testing has been developed to evaluate the application, such as Blackbox testing (achieves 100%), usability testing (achieves 79%), and media content testing (achieves 85.45%).
Detection of the Use of Mask to Prevent the Spread of COVID-19 Using SVM, Haar Cascade Classifier, and Robot Arm Pratiwi, Andini; Nababan, Erna Budhiarti; Amalia
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-9289

Abstract

In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly.
Erratum: Development of an Android-Based “LaporKPS” Application to Support the Service Center for Reports of Sexual Violence and Harassment Cases Editor
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

There is a correction in the writing of the author's name in this publication due to technical errors of the editor. The published article and Web Metadata:Adhim Bagas Wisnu Aji1, Eko Sipraptono2 Correction:Adhim Bagas Wisnu Aji1 and Eko Supraptono2 Date of Correction:May 19, 2023 Status:Reupload as an online publication

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