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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 49 Documents
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Search results for , issue "Vol 7, No 3 (2023)" : 49 Documents clear
Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment Liliek Triyono; Rahmat Gernowo; Prayitno Prayitno; Mosiur Rahaman; Tri Raharjo Yudantoro
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1243

Abstract

It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%. 
AI Educational Mobile App using Deep Learning Approach Haslinah Mohd Nasir; Noor Mohd Ariff Brahin; Farees Ezwan Mohd Sani @ Ariffin; Mohd Syafiq Mispan; Nur Haliza Abd Wahab
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1247

Abstract

Moving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the children complete the activity without interacting with the environment. This study presents an educational mobile application that uses a deep learning approach for interactive learning to enhance English and Arabic vocabulary. Android Studio software and Tensorflow tool were used for this application development. The convolution neural network (CNN) approach was used to classify the item of each category of vocab through image recognition. More than thousands of images each time were pre-trained for image classification. The application will pronounce the requested item. Then, the children will need to move around looking for the item. Once the item’s found, the children must capture the image through the camera’s phone for image detection. This approach can be integrated with teaching and learning techniques for fun learning through interactive smartphone applications. This study attained high accuracy of more than 90% for image classification. In addition, it helps to attract the children's interest during the teaching using the current technology but with the concept of ‘Play’ and ‘Learn’. In the future, this paper recommended the involvement of IoT platforms to provide widen applications.
Capturing User Experience of Customer-Centric Software Process through Requirement Process: Systematic Review Wahyu Andhyka Kusuma; Azrul Hazri Jantan; Novia Indriaty Admodisastro; Noris Mohd Norowi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1499

Abstract

Agile and User Experience have become popular for decades due to the ability to understand customer needs. However, both methods have different perspectives on the point of view, value, and quality. Moreover, user research in UX is usually conducted in the long term. The human aspect is a critical thing in Agile, the purpose of this aspect is to understand the value and need of the product, and with the user stories, several developers try to understand the human aspect of customers. In the elicitation process of the UX, developers used user stories to capture customer personality. One important factor is emotion; UX researchers measure emotions from the product journey, but it is unpleasant when the customer finds out the product does not meet expectations. This study aims to research the implementation of capturing emotion in user experience among Agile software development activities from several perspectives. In addition, Limited resources in software projects require innovation that can guarantee the sustainability and quality of the product. In this paper, we used modified systematic mapping to extract, classify, and interpret articles from popular publishers and map the user experience life cycle to answer several existing problems. This research shows that a combination of user requirement and UX increase the product's usability. Moreover, involving the user in the development center increases the project's success.
Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method Puan Maharani Kurniawan; Agung Teguh Wibowo Almais; M. Amin Hariyadi; M. Ainul Yaqin; Suhartono Suhartono
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1698

Abstract

Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.
Closer Look at Image Classification for Indonesian Sign Language with Few-Shot Learning Using Matching Network Approach Irma Permata Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1320

Abstract

Huge datasets are important to build powerful pipelines and ground well to new images. In Computer Vision, the most basic problem is image classification. The classification of images may be a tedious job, especially when there are a lot of amounts. But CNN is known to be data-hungry while gathering. How can we build some models without much data? For example, in the case of Sign Language Recognition (SLR). One type of Sign Language Recognition system is vision-based. In Indonesian Sign Language dataset has a relatively small sample image. This research aims to classify sign language images using Computer Vision for Sign Language Recognition systems. We used a small dataset, Indonesian Sign Language. Our dataset is listed in 26 classes of alphabet, A-Z. It has loaded 12 images for each class. The methodology in this research is few-shot learning. Based on our experiment, the best accuracy for few-shot learning is Mnasnet1_0 (85.75%) convolutional network model for Matching Networks, and loss estimation is about 0,43. And the experiment indicates that the accuracy will be increased by increasing the number of shots. We can inform you that this model's matching network framework is unsuitable for the Inception V3 model because the kernel size cannot be greater than the actual input size. We can choose the best algorithm based on this research for the Indonesian Sign Language application we will develop further.
Performance Assessment of QoS metrics in Software Defined Networking using Floodlight Controller Diyar Jamal Hamad; Khirota Gorgees Yalda; Nicolae Țăpuș
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1288

Abstract

The quality of service is not the same in all parts of the network. Some areas experience a low level and others a higher level of fixed quality services. The shortcomings in legacy networks encouraged researchers to find a new paradigm of the network to obviate legacy networks' deficiencies. The effort to create network services is called Quality of Service (QoS). Software-Defined Networking (SDN) focuses on separating the control layer from the data layer, and their communication is done through a central controller named SDN controller. After separation, the data layer moves the packets through the network according to the commands it receives from the controller. The controller obtains applications (QoS requests), translates them to low-level instructions, and implements them in the data layer. In this paper, we create an infrastructure for Quality of Service (QoS) in tree topology using a meter table per flow in Software Defined Networking Floodlight open-source controller. Meters are introduced into the OpenFlow protocol version 1.3, which calculates the packet rates allocated to them and allows control of those packet rates. Meters are directly connected to flow entry. Any flow entry can determine a meter in its command collection, which calculates and supervises the sum of all flow entries to which it is connected. When we get statistics from the meter table in each switch, we manage the network and affect the routing algorithms.
A Genetic Algorithm-based Group Formation to Assign Student with Academic Advisor: A Study on User Acceptance using UTAUT Tan Xue Ying; Azleena Mohd Kassim; Nor Athiyah Abdullah
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1667

Abstract

Group formation to assign students with academic advisors based on student demography can be exhaustive as various possibilities and combinations can be formed. Hence, this paper proposed a genetic algorithm-based approach to automate group formation based on student demography to assign students to their academic advisors. The genetic algorithm (GA) will optimize the group formation of students with a balanced number of nationalities, races, and genders. Also, this paper examines the user acceptance of the proposed genetic algorithm-based application to automate group formation using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The survey aims to study the impact of independent and moderating variables on dependent variables. The result proved that all the independent variables, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC), have a positive impact on the dependent variable, Behavioral Intention (BI). In contrast, the moderating variable Experience (EX) and Voluntariness of Use (VU) have a negative impact on Behavioral Intention (BI). Thus, this paper concludes that the proposed application can increase the performance and efficiency of group formation and automatically assign students to academic advisors. However, respondents are reluctant and not ready to use the system. Thus, training and workshops can be conducted to introduce and train the users to utilize the system. Future works can be done where the application of the proposed genetic algorithm-based system can be further expanded to different academic purposes such as team formation for group assignment and team member selection for competition.
A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Nur Ghaniaviyanto Ramadhan; Azka Khoirunnisa; Kurnianingsih Kurnianingsih; Takako Hashimoto
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1171

Abstract

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
Saudi Learners' Perception of Infographics in Education: A Survey Suzan Alyahya
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1521

Abstract

Learners' learning experiences diverge and undergo rapid shifts due to trends in technology, and the transfer of knowledge in varying formats and styles is ubiquitous. Infographics find its application in instructional design and technology that drives education with state-of-the-art tools and applied methods. Being a format of data, Infographics or visual images presents information or knowledge using visuals. This study aims to present the student perceptions of learning via infographics at Princess Nourah bint Abdurrahman University (PNU) in Saudi Arabia. The study employs a survey questionnaire consisting of 13 close-ended questions posed to assess the learners' responses at PNU. Applied Likert's scale-based questions present a conversion of user data input to quantitative figures, which present the level of understanding and the role of infographics in education. The survey involved 45 undergraduate female students pursuing undergraduate degree courses at PNU. Using survey research design methodology, the study investigated learners' perceptions of infographics to add to their learning experiences and provides a quantitative analysis of observed responses to the survey questionnaire. The study conducts an online survey and classifies participants of different age groups into five categories for assessment. The study findings reveal that PNU learners perceive a positive role of infographics in their learning. However, learners showcase varied perceptions of a) the acceptance of assignments based on infographics and b) the use of static versus animated infographics. The study guides research scholars toward the intuition of infographics in learning environments and reports the two research problems to be addressed in future works.
Web-based E-learning in Elementary School: A Systematic Literature Review Herwulan Irine Purnama; Insih Wilujeng; Cepi Safruddin Abdul Jabar
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1203

Abstract

This article presents literature review on web-based e-learning in elementary school in the latest literature. SLR method and PRISMA protocol with the stages of identification, screening, eligibility, inclusion, and abstraction, data analysis assisted by the Publish or Perish 7 application, VOSviewer, and NVIVO 12 Plus. The results of searching for articles on Scopus through the Publish or Perish 7 application are 507. Then the articles were filtered according to compatible themes into 50 articles. The topic findings are web-based e-learning, elementary school, the impact of web-based e-learning and web-based e-learning concept, academic performance, teaching/learning strategies, online learning, Covid-19, HPC database, web-based applications, distance learning, 3D visualization, automation, strategic learning, semantic web, technology, education, linguistic content, big data architecture, learning setting, e-readiness, linguistic content, STEM, etc., that are directly or indirectly connected. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described according to the research questions. The findings in this article explain that web-based e-learning integrates pedagogy and technology and becomes part of digital multimedia implemented in e-learning, blended learning, and face-to-face that impacts elementary school students and teachers directly or indirectly. Future research needs to explore web-based e-learning in schools that is current, safe, and needed by students and teachers.

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