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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 62 Documents
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Search results for , issue "Vol 8, No 1 (2024)" : 62 Documents clear
Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation Ahda, Fadhli Almu'iini; Wibawa, Aji Prasetya; Dwi Prasetya, Didik; Arbian Sulistyo, Danang
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1818

Abstract

Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.
Architectural Visualization Approach Using Google SketchUp and Lumion on the Development of Maritime Tourism Haedar Akib; - Ismail; Edi Suhardi Rahman; Ahmad Wahidiyat Haedar; Sirajuddin Saleh; Muhammad Rizal
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1673

Abstract

This study aims to portray the strategy and design of marine tourism development through a data visualization approach using Google SketchUp and Lumion. The type of research conducted is Research and Development (R&D), employing the ADDIE approach (Analysis, Design, Development, Implementation, and Evaluation). The findings of this study reveal that the miniature design of Pulau Sembilan on Larea-rea Island incorporates local cultural themes in the construction of Rulan (Nine House), with a visualization of the roof following the design of the traditional Karampuang house. Additionally, the researcher presents designs concerning the jetties of Larea-rea Island and the product facility designs. Ultimately, contributes to development of Rulan (Nine House) and the efficient land utilization contributes to the region's income enhancement in the tourism sector of Larea-rea Island. It is recommended that future research on marine tourism development should involve a more comprehensive analytical framework that considers social, economic, and environmental aspects. This will help in understanding the overall impacts of marine tourism development. It is also important to investigate the ecological impacts of tourism and come up with effective mitigation strategies to prevent harm to the marine environment. Another interesting research area is to integrate the perspectives of local communities and their participation in the planning and implementation of tourism development. This will ensure the sustainability of such projects and gain community acceptance.
Impact of External Factors on Determining E-commerce Benefits among SMEs in Jakarta and Palembang Hardiyansyah, -; Aries, -; Muchardie, Brian Garda; Rillia, Anneke
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1371

Abstract

Technological trends have triggered a more advanced technology-based approach to influencing customers and encouraging the growth of the e-commerce industry in Indonesia. E-commerce is now considered a bridge for MSME players to market with a broader reach, even to international markets, which is one of the factors for the rise of Indonesian MSMEs, as well as the growth of the digital economy in Indonesia. However, very few MSMEs are still using technology to grow their business. This article examines how external factors, specifically customers and competitors, can encourage SMEs in Jakarta and Palembang to adopt e-commerce and promote e-commerce adoption. Small and medium enterprises benefit from applying this technology. The research method used was the quantitative conjoint type, using primary data in questionnaires distributed to 101 MSME owners in Jakarta and Palembang using a Google form. The data analysis technique in this study used structural equation modeling (SEM) based on partial least squares (PLS) by examining the measurement model and model structure. The results of this study indicate that perceived customer benefits have a significant influence on external relationships and are found to influence cost reduction, as well as a significant influence on loyalty. customer's status. At the same time, perceptions of competitive value increase relationships with external parties and customer loyalty. In contrast, competitive value only affects customer loyalty without significantly affecting cost reduction and external relations
A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data Gao, Xiaoling; Jamil, Nursuriati; Ramli, Muhammad Izzad; Syed Zainal Ariffin, Syed Mohd Zahid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2194

Abstract

This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced image classification. The application of SMOTE in imbalanced picture datasets is still underexplored, even though CNNs have been shown to be successful in image classification and that ensemble learning approaches have improved their performance. To investigate whether or not SMOTE can increase classification accuracy and other performance measures when combined with CNN-based classifiers, our research makes use of a CIFAR-10 dataset that has been artificially step-imbalanced and has varying imbalanced ratios. We conducted experiments using five distinct models, namely AdaBoost, XGBoost, standalone CNN, CNN-AdaBoost, and CNN-XGBoost, on datasets that were either imbalanced or SMOTE-balanced. Metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were included in the evaluation process. The findings indicate that SMOTE dramatically improves the accuracy of minority classes, and that the combination of ensemble classifiers with CNNs and oversampling techniques significantly improves overall classification performance, particularly in situations when there is a high-class imbalance. When it comes to enhancing imbalanced classification tasks, this study demonstrates the potential of merging oversampling techniques with CNN-based ensemble classifiers to minimize the impacts of class imbalance in picture datasets. This suggests a promising direction for future research in this area.
Creation of Cultural Local Wisdom-Based Picture-Science Stories Application for the Introduction of Scientific Literacy for Early Childhood Eliza, Delfi; Mulyeni, Trisna; Budayawan, Khairi; Hartati, Sri; Khairiah, Fisna
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2234

Abstract

This article aims to explain the design, development, implementation, and evaluation of the Picture Science Story (CSB) application integrated with local wisdom based on an Android application for early childhood. This application can be used with a touch screen. The aim of making this application is to introduce the science process skills and local wisdom to the children. The research method used was R and D. The initial stage of developing the CSB prototype model was to conduct the needs analysis, child characteristics, and curriculum analysis. Then, the design, development, implementation, and evaluation of the CSB application were carried out. The application used a layered platform. Many experts from different fields were involved in the design process: graphic design experts to create images, multimedia experts to create applications, and teachers for the science material. The CSB prototype design was validated by material, media, and user experts, namely Al-Huffaz Kindergarten teachers and children. The participants in this research were 13 Alhufazh Kindergarten students. A questionnaire was given to get a response from the teachers, consisting of aspects of understanding multimedia, function, and configuration. The average score of the teachers’ responses was 92%. Meanwhile, the average score of children's responses was 95%. Based on the results of validation and trials, it was found that the CSB application integrated with local wisdom based on an Android application was valid, effective, and practical for early childhood. The suggestions from users require multidisciplinary knowledge in designing picture-science stories based on Android applications. Then, the feedback addressed the importance of using the Science Story Creation prototype and integration of the Local Wisdom of Minangkabau Culture to introduce Early Childhood Science Literacy.
Convolutional Neural Networks-Based For Predicting Aerodynamic Coefficient Of Airfoils At Ultra-Low Reynolds Number Kasman, Alief Sadlie; Zikri, Arizal Akbar; Fariduzzaman, Fariduzzaman; Srigutomo, Wahyu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2197

Abstract

Many applications, including airplane design, wind turbines, and heat transmission, use symmetric or asymmetric airfoils. Engineers employ these airfoil shapes to optimize performance and efficiency. Each airfoil has a unique set of aerodynamic coefficients that must be calculated to maximize the airfoil design. Engineers utilize numerous ways to calculate coefficients, such as lift and drag. One of the methods is the prediction method, which effectively reduces time and cost. This study's training dataset is obtained from particle-based numerical computation using the Lattice Boltzmann Method (LBM). Then, Convolutional Neural Networks (CNN) are used as a prediction method to get the aerodynamic coefficients of airfoils for lift and drag based on two different Reynolds numbers. In CNN, airfoil geometry representation is essential. The Signed Distance Function (SDF) was used to convert airfoil geometry into RGB pictures. On the other hand, the SDF method cannot explain different flow conditions; in this case, it is represented by the Reynolds number (Re). Therefore, we propose a Text-based Watermarking Method (TWM) to differentiate between Re = 500 and Re = 1000. Each airfoil representation was trained and tested to generate each prediction model using a modified LeNet-5. The computation results show that using CNN with TWM on SDF to define the Reynolds numbers could predict the lift and drag coefficients with varying angles of attack. Future research can focus on generalizations to different aerodynamic aspects and practical applications in complex scenarios.
Data Pre-processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification Apandi, Siti Hawa; Sallim, Jamaludin; Mohamed, Rozlina; Ahmad, Norkhairi
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1618

Abstract

The increased usage of the internet worldwide has led to an abundance of web pages designed to supply information to internet users. The use of web page classification is becoming increasingly necessary to organize the growing number of web pages. This classification model serves as a tool to restrict internet usage to specific categories of web pages. To develop the classification model, it’s crucial to check the quality of the dataset, as it determines the performance of the web page classification model. Raw datasets are typically unreliable and subject to noise, which complicates data analysis. This is why data pre-processing is necessary to prepare the dataset properly. In this study, website browsing records serve as the dataset. The primary goal of this paper is to investigate data pre-processing techniques for website browsing records, focusing on Game and Online Video Streaming web pages. Data pre-processing involves two main steps: data cleaning and web content pre-processing. After completing the data cleaning process, the datasets are reduced from the original. This demonstrates that many datasets can be eliminated due to their inactivity or unsuitability as the datasets for Game and Online Video Streaming web pages. Meanwhile, web content pre-processing removes noise from an HTML document, retaining only relevant words that can represent the web page by creating a word cloud image. Convolutional Neural Networks (CNN) will be used to construct a model for categorizing web pages to determine whether they fall under Game or Online Video Streaming. The pre-processed data will be used as the input for this model.
Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model Farell, Geovanne; Zin Latt, Cho Nwe; Jalinus, Nizwardi; Yulastri, Asmar; Wahyudi, Rido
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2201

Abstract

Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.
Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions Tan, Shao Xian; Ong, Jia You; Goh, Kah Ong Michael; Tee, Connie
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2313

Abstract

In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. This study proposes techniques to enhance vehicle object detection and classification using augmentation methods based on the YOLO (You Only Look Once) network. The primary objective of the trained model is to generate a local vehicle detection system for Malaysia which have the capacity to detect vehicles manufactured in Malaysia, adapt to the specific environmental factors in Malaysia, and accommodate varying lighting conditions prevalent in Malaysia. The dataset used for this paper to develop and evaluate the proposed system was provided by a highway company, which captured a comprehensive top-down view of the highway using a surveillance camera. Rigorous manual annotation was employed to ensure accurate annotations within the dataset. Various image augmentation techniques were also applied to enhance the dataset's diversity and improve the system's robustness. Experiments were conducted using different versions of the YOLO network, such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8, each with varying hyperparameter settings. These experiments aimed to identify the optimal configuration for the given dataset. The experimental results demonstrated the superiority of YOLOv8 over other YOLO versions, achieving an impressive mean average precision of 97.9% for vehicle detection. Moreover, data augmentation effectively solves the issues of overfitting and data imbalance while providing diverse perspectives in the dataset. Future research can focus on optimizing computational efficiency for real-time applications and large-scale deployments.
A Classification Algorithm Inspired by the Chromatographic Separation Mechanism Dedicated to the Classification of Variable-length and Multi-class Vectors Mariusz Święcicki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2324

Abstract

Nowadays, one of the critical problems related to data mining is the processing of large data sets. This article presents an algorithm that may apply to the issues associated with classifying large-volume data sets. The motivation behind defining this type of algorithm was that the methods used to process this data type are subject to several significant limitations. The first considerable limitation of using classical classification methods is ensuring a constant data size. The second type of constraint is related to the data dimension. The last limitation in using classic classification algorithms is associated with the situation in which a given input vector may contain data belonging to many classes simultaneously, in which case we are talking about so-called multi-class vectors. The presented algorithm is inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. As we know, in the case of chromatographic separation, we are dealing with a similar class of problems that occur when processing large data sets, firstly: the molecules of a chemical substance have a different number of molecules - i.e., they have different lengths, which corresponds to the situation that occurs when processing large data sets. In this work, a classification algorithm inspired by the mechanism of resolution chromatography is presented. The article presents the results of calculations for sample data sets. It discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class and multi-class data.

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