cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ainul Hizriadi, S.Kom., M.Sc.
Contact Email
ainul.hizriadi@usu.ac.id
Phone
-
Journal Mail Official
jocai@usu.ac.id
Editorial Address
-
Location
Kota medan,
Sumatera utara
INDONESIA
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.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear
Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model: Time Series Forecasting of Global Price of Soybeans Chi, Yeong Nain
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): 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.v5.i2-5674

Abstract

Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.
Analyzing Main Topics Regarding The Electronic Information and Transaction Act in Instagram Using Latent Dirichlet Allocation Kresnawan, Hans; Felle, Sola Graciana; Mokay, Hanna Gloria; Rakhmawati, Nur Aini
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): 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.v5.i2-6125

Abstract

Indonesia is currently experiencing its fourth industrial revolution in the 21st century. With the introduction of the internet, Indonesia is expected to gain more than a hundred billion US Dollars and twenty-six million job openings by 2030. The rising usage of information technology prompts regulators to develop The Electronic Information Transaction Act to protect the populace from cybercrime. However, the law attracts numerous criticism due to its vague interpretation. This resulted in numerous arrests of innocents throughout Indonesia. Thus, the public is trying to voice their opinions on social media for the sake of preventing any more cases in the future. The usage of Latent Dirichlet Allocation could provide numerous benefits for this research. The separation between latent topics among random mixtures helps to identify the common ground and correlation between each post. These latent topics will be elaborated with a sample post to provide insights and expectations of the public towards the law.
A Web-Based Diabetes Prediction Application Using XGBoost Algorithm Herlambang Dwi Prasetyo; Pandu Ananto Hogantara; Ika Nurlaili Isnainiyah
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): 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.v5.i2-6290

Abstract

One of the diseases that is generally characterized by symptoms of an increase in glucose levels in the blood and is one of the body diseases classified as chronic is diabetes. Diabetes suffered by a person from time to time can cause serious damage to other organs such as blood vessels, kidneys, heart and nerves. Machine learning provides various data mining algorithms that can be used to assist medical experts. The accuracy of machine learning algorithms is a measure of the effectiveness of decision support systems. Prediction of diabetes can be seen from the patient's medical record data, therefore the author wants to create a diabetes prediction system independently through a website-based application system. This application system will be combined with data observation, namely the science of data mining using the XGBoost algorithm. The dataset is divided into training data by 80% and testing data by 20%. Before the data modeling was carried out, we carried out various parameter setting scenarios with the hope of evaluating and evaluating the implementation to be applied, the parameters we adjusted were colsample_bytree, gamma, learning_rate, max_depth, n_estimators, reg_alpha, reg_lambda, and subsample. After sharing the data and tuning parameters, the resulting model by applying the XGBoost algorithm has an accuracy of 74.67%, the resulting precision value is 57.40%, the resulting recall value is 65.94%, the resulting specificity value is 78, 50%.
MobileNets-V1 Architecture for Web Based Fish Image Classification Herlambang Duwi Prasetyo; Pandu Ananto Hogantara; Ika Nurlaili Isnainiyah
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): 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.v5.i2-6291

Abstract

Recently, the research study about fish identification become a very challenging to researchers. Climate and environmental changes have a major impact on fish species and their environment. To identify fish using manual process is time consuming and need effort to gather samples in different environment. The identification of fish species is performed by using feature extraction and a series of features. Generally, the characteristic is divided into two groups namely general characteristics and anatomical features. General characteristics is characteristic that can be seen directly without the aid of tools. The characteristics include color, texture, and fiber direction. Although, manual is performed by expert but is possible that identification is not accurate. Therefore, to overcome the problem, we create a web-based application for identifying fish by using image as input. We use 10 class data with 300 images for each class. Then, we split into training and testing with 80:20 ratio. The application was developed by using the MobileNets- V1 model. The proposed method has accuracy on 89 %, that obtain from training score is 91.04%, validation is 88,96%. This score is higher than other methods that used in this application. Total time for computation process is about 127 minutes.
Implementation of Moving Object Tracker System Mohanad Abdulhamid; Adam Olalo
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): 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.v5.i2-6450

Abstract

The field of computer vision is increasingly becoming an active area of research with tremendous efforts being put towards giving computers the capability of sight. As human beings we are able to see, distinguish between different objects based on their unique features and even trace their movements if they are within our view. For computers to really see they also need to have the capability of identifying different objects and equally track them. This paper focuses on that aspect of identifying objects which the user chooses; the object chosen is differentiated from other objects by comparison of pixel characteristics. The chosen object is then to be tracked with a bounding box for ease of identification of the object's location. A real time video feed captured by a web camera is to be utilized and it’s from this environment visible within the camera view that an object is to be selected and tracked. The scope of this paper mainly focuses on the development of a software application that will achieve real time object tracking. The software module will allow the user to identify the object of interest someone wishes to track, while the algorithm employed will enable noise and size filtering for ease of tracking of the object.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): (In Press) Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 9 No. 2 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 8 No. 2 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 5 No. 1 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 4 No. 2 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 3 No. 1 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 2 No. 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI) More Issue