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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.
Arjuna Subject : -
Articles 1,172 Documents
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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.
Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA Low, Adeline Hui-Min; Lim, Amy Hui-Lan; Chua, Fang-Fang
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2341

Abstract

Teachers, schools, and parents contribute to equipping students with essential knowledge and skills during their education years. When students are approaching the end of their education, they are randomly selected to participate in Program for International Student Assessment (PISA) to assess their reading proficiency. Existing work on analyzing PISA achievement results concentrates solely on identifying factors related to Parent or in combination with Student. Limited work has been proposed on how factors related to Teacher and School affect the students’ reading proficiency in PISA. This study focuses on identifying the factors related to Teacher and/or School that affect East Asian students’ reading proficiency in PISA. The PISA achievement results from East Asian students are chosen as the domain study because they are consistently the top performers in PISA in the past decade. Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbors (KNN) and Random Forest (RF) are compared. Hamming score is used as the evaluation metric. The results indicate that RF produces the best predictive models with highest Hamming score of 0.8427. Based on the findings, School-related factors such as the number of school’s disciplinary cases, size of the school, the availability of computers with Internet facilities, the quality and educational qualifications of teachers have higher impact on the PISA achievement results. The identified factors can be used as a reference in assessing the current school’s teaching, learning environment, and organizing extra activities as part of intervention programs to cultivate reading habits and enhance reading abilities among students.
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
Performance Comparison of Zevenet Multi Service Load Balancing with Least Connection and Round Robin Algorithm Ma'arifah, Windiya; Sarmini, Sarmini
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1985

Abstract

Amikom Purwokerto University concentrates on Technology and Digital Business. This requires technology to be utilized optimally. The use of technology, especially internships, will make various jobs easier. KRS online is taking lecture schedules online via the AMIKOM Purwokerto Student website. There are several problems with the web server that arise due to the increasing need for information access, which causes the data traffic load to increase. Increased data traffic causes workload overload, resulting in server downtime. Experimental methods were used in this research to look for the causes of the web server's downtime. Then, implement the technology. The purpose is to evaluate the Zevenet load balancer performances by comparing the round-robin and least-connection algorithms. The decision is which algorithm will be used best to implement the Zevenet Load balancer to achieve a more efficient backend server traffic cluster distribution. The TIPHON standard Quality of Service parameters used in Zevenet Load Balancer performance testing are throughput, delay, jitter, packet loss, and CPU usage. The quality-of-service parameter test results show that the Zevenet Load Balancer with the round-robin algorithm has superior performance and shows less CPU usage. It is concluded that using the round-robin algorithm in implementing the Zevenet load balancer to overcome the problem of data traffic load sharing and minimize server downtime on the Student Amikom Purwokerto web server is more appropriate and more effective.
Project-Based Learning Model Development Using Flipped Classroom for Drawing Learning in College Mayar, Farida; Putra, Febri Wandha; Monia, Fenny Ayu; Kosassy, Siti Osa; Fadli, Rima Pratiwi; Arinalhaq, Ririen
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2227

Abstract

The learning process was initially carried out online during the pandemic then after the post-pandemic, learning activities began to be carried out face-to-face. This raises a new problem, because lecturers who teach are starting to be required to use various media in the learning process in the new normal era. But in reality, the lecturers still use old methods such as lectures or task-based learning. Therefore, the research objective is to develop a new model that can be used in the learning process in higher education. This type of research is development research using the ADDIE approach. The instrument used was a student learning motivation questionnaire and a student learning satisfaction questionnaire following the lesson. Another instrument used is a Likert model scale with three alternative answers. The operational procedure taken in this research and development goes through three stages, namely: (1) preliminary study, (2) preparation of conceptual models (3) validity test and (4) practicality test. The results of the validity test show that the model book and manual are in the very valid category from the aspects of design, language and content. Furthermore, the practicality test results show that model books and guidebooks for project-based learning models using flipped classrooms for drawing lectures in tertiary institutions are practically used by lecturers. The implications of this research can help lecturers in designing practical learning.
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.
Arabic Character Recognition Using CNN LeNet-5 Satya Nugraha, Gibran; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri; Yudo Husodo, Ario; Hamami, Faqih
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2422

Abstract

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
Predicting Student's Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach Kannan, Rathimala; Jet, Chew Chin; Ramakrishnan, Kannan; Ramdass, Sujatha
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2342

Abstract

Recent changes in the labor market and higher education sector have made graduates' employability a priority for researchers, governments, and employers in developed and emerging nations. There is, however, still a dearth of study about whether graduate students acquire the employability skills that businesses want of them because of their higher education. To determine a student's future employment and career path, it is critical to evaluate their soft skills. An emerging area called educational data mining (EDM) aims to gather enormous volumes of academic data produced and maintained by educational institutions and to derive explicit and specific information from it. This paper aims to predict students' soft skills such as professional, analytical, linguistic, communication, and ethical skills, based on their socio-economic, academic, and institutional data by leveraging data mining methods and machine learning techniques. All five soft skills were predicted using prediction models created using linear regression, probabilistic neural networks, and simple regression tree techniques. This study used a dataset from an open source that Universidad Technologica de Bolivar published. It covers academic, social, and economic data for 12,411 students. The experimental results demonstrated that the linear regression algorithm performed better than the others in predicting all five soft skills compared to machine learning methods. This finding can assist higher education institutions in making informed decisions, providing tailored support, enhancing student success and employability, and continuously modifying their programs to meet the needs of students.
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.

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