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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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Automatic Topic-Based Web Page Classification Using Deep Learning Apandi, Siti Hawa; Sallim, Jamaludin; Mohamed, Rozlina; Ahmad, Norkhairi
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.7.3-2.1616

Abstract

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.
Adoption of Industry 4.0 with Cloud Computing as a Mediator: Evaluation using TOE Framework for SMEs Abu Bakar, Muhammad Ramzul; Mat Razali, Noor Afiza; Ishak, Khairul Khalil; Ismail, Mohd Nazri; Tengku Sembok, Tengku Mohd
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.8.2.2205

Abstract

Industry 4.0 represents a significant shift in production processes, necessitating the integration of humans, products, information, and robots into digitalized workflows. While this transformation offers numerous benefits, its adoption, particularly among small and medium enterprises (SMEs), is hindered by various challenges such as financial constraints, maintenance costs, and a lack of digital culture and awareness. This study examines the adoption of Industry 4.0, specifically through cloud computing technologies, within the manufacturing and service sectors of SMEs in Malaysia. Cloud computing is economical, straightforward, and easily implemented for SMEs. We propose a conceptual model based on an extended Technology-Organisation-Environment (TOE) model, integrating refined constructs and considering digital organizational culture as a moderator, with cloud computing acting as a mediator to enhance firm performance. The study investigates the relationship between these constructs and addresses overlooked factors influencing adoption. Utilizing a structured questionnaire with 54 items derived from previous research, we employ partial least squares structural equation modeling (PLS-SEM) to analyze data collected from a pilot study. Our findings confirm the reliability and validity of the proposed conceptual model, meeting established criteria for composite reliability, average variance extracted (AVE), Cronbach's alpha, and discriminant validity (HTMT Criterion). Furthermore, this study presents empirical findings on technological, organizational, and environmental influences on adopting cloud computing. The insights gained from this research offer valuable guidance to enhance the performance of SMEs in the Industry 4.0 landscape.
The Attitudes of Student-teachers toward Moodle as a Supplementary Learning Platform Suparjan, -; Purwanta, Edi; Septiarti, Serafin Wisni; Purnomo, Yoppy Wahyu
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2852

Abstract

During the COVID-19 pandemic, Moodle (Modular Object-Oriented Dynamic Learning Environment) emerged as a leading online learning platform, providing essential support for educational institutions transitioning to remote learning. However, its role as a supplementary tool alongside traditional teaching methods in non-pandemic contexts remains underexplored in academic research. This study aims to investigate the attitudes of higher education student-teachers toward Moodle as an asynchronous online learning platform that enhances traditional classroom learning. The research employed a descriptive-quantitative method, focusing on students from the Teacher Training and Education Faculty, specifically those in the Elementary School Teacher Education Department at Universitas Tanjungpura, Indonesia. Participants were selected through purposive sampling, targeting individuals with experience using Moodle in online and offline learning environments. Data were collected using a structured questionnaire to assess student-teacher attitudes toward Moodle. It underwent a rigorous validation process to ensure the instrument's validity, including expert reviews. The reliability of the questionnaire was assessed through a pilot test conducted with 25 elementary school student-teachers from a state university. The results indicate that most student-teachers have positive attitudes toward Moodle, particularly valuing its effectiveness in enhancing their traditional learning experiences. Despite these favorable views, further research is necessary to develop a more comprehensive understanding of Moodle as a learning management system. Future studies should explore a broader range of educational contexts and levels to better understand the diverse challenges and benefits of applying Moodle in different teaching environments.
Programming Language Selection for The Development of Deep Learning Library Rachmawati, Oktavia Citra Resmi; Barakbah, Ali Ridho; Karlita, Tita
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.8.1.2437

Abstract

Recently, deep learning has become very successful in various applications, leading to an increasing need for software tools to keep up with the rapid pace of innovation in deep learning research. As a result, we suggested the development of a software library related to deep learning that would be useful for researchers and practitioners in academia and industry for their research endeavors. The programming language is the core of deep learning library development, so this paper describes the selection stage to find the most suitable programming language for developing a deep learning library based on two criteria, including coverage on many projects and the ability to handle high-dimensional array processing. We addressed the comparison of programming languages with two approaches. First, we looked for the most demanding programming languages for AI Jobs by conducting a data-driven approach against the data gathered from several Job-Hunting Platforms. Then, we found the findings that imply Python, C++, and Java as the top three. After that, we compared the three most widely used programming languages by calculating interval time to three different programs that contain an array of exploitation processes. Based on the result of the experiments that were executed in the computer terminal, Java outperformed Python and C++ in two of the three experiments conducted with 5,4047 milliseconds faster than C++ and 231,1639 milliseconds faster than Python to run quick sort algorithm for arrays that contain 100.000 integer values. 
The Analysis Factors Influencing the Implementation of Digital Social Entrepreneurship Application in Learning Engineering Education Using Structural Equation Modelling Ganefri, -; Nordin, Norazah Mohd; Yulastri, Asmar; Hidayat, Hendra
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2236

Abstract

A big part of being an entrepreneur is keeping up with modern technological advancements. However, many factors can lead to the ambition to launch an online company. Digital social entrepreneurship methodology examines the effects of college students' entrepreneurial mindset, smartphone usage habits, and Locus of Control on their digital business intentions. This research is fundamental because it provides information to universities that they can use to evaluate their plans for a digital-based entrepreneurship learning model that will help them provide a good education. This study involved 428 respondents, and the data obtained from the respondents were examined using the application of structural equation modeling with a survey approach for this research, which looks at a small portion of the community and collects data through questionnaires. The primary data was examined using SmartPLS 4.0 software and structural equation modeling. This study found that having an entrepreneurial mindset, smartphone use, and locus of control exerts a substantial and meaningful impact on one's aspiration to become a digital entrepreneur. We wanted to find out how college students' thinking about being an entrepreneur affects their desire to become a digital entrepreneur, using smartphone usage habits and locus of control as influencing factors. To make someone who wants to become an entrepreneur, this research needs to measure Digital Entrepreneurial Intention appropriately in students who take Entrepreneurship courses.
Smart Contract and IPFS Decentralized Storage for Halal Certification Process Agung, Anak Agung Gde; Yuniar, Irna; Hendriyanto, Robbi
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.8.1.1497

Abstract

The halal industry today has achieved rapid development. Halal product is mandatory for Muslims and a big business for Indonesia. For others, it affirms the product's quality assurance and becomes a trending lifestyle. The product owner must submit an application and undergo several processes to obtain a halal certificate. However, there are challenges in the certification process and documentation. The proposed system automates the flow between certification processes through digital signing and stores the certificate and fatwa file. The study investigates the utilization of blockchain to manage the process and the integration of decentralized storage (IPFS) to store the digital version of the fatwa and certificate. A smart contract is designed and deployed on the Ethereum blockchain, and the transaction time and cost are analyzed. A smart contract enforces that certain actions are executed once the required conditions are fulfilled. The proposed system would cost 24.6 USD and require 227 seconds on average for the system setup. Each submission requires 9.86 USD and takes 92 seconds on average. Verification is free, and the average result can be obtained in one second. The appointed officer sets each entity to interact with the contract, and the digital documents (fatwa and certificate) are available online using IPFS. Progress of the certification is transparent to the public, increasing the public's trust. The study demonstrates a smart contract's capability to manage a product's certification process.
Community Blockchain Record-Keeping Method for Agricultural Land Leases using Design Science Research Approach Loh, Yin Xia; Huspi, Sharin Hazlin; Amerhaider Nuar, Ahmad Najmi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3619

Abstract

In Malaysia, agricultural land lease records are still predominantly maintained on paper, making them vulnerable to loss, damage, and tampering. The study presents a novel, community-based blockchain record-keeping system designed explicitly for agricultural land leasing. Its primary objective is to enhance the transparency, trust, and efficiency of lease transactions between landowners and small-scale farmers. The system leverages Hyperledger Fabric in combination with the Interplanetary File System (IPFS) to ensure lease agreements are stored securely and immutably. By using decentralized storage, the documents remain accessible when needed while reducing the risk of unauthorized modifications. The design of this system is grounded in Work System Theory (WST), which emphasizes the integration of technology with the people, processes, and environmental factors involved in land leasing. To ensure the development approach aligns with the complexities of the real-world context, the study employs Situational Method Engineering (SME). This methodology involves selecting and tailoring components from existing methods to create a solution customized for agricultural land leasing. By combining a robust technical foundation with a design that accounts for community dynamics and legal considerations, the study demonstrates how blockchain can serve not only as a data management tool but also as a means of promoting fairness and transparency in rural land governance. The artefact marks a significant step toward building digital trust in the management and documentation of agricultural land. 
Implementing K-Nearest Neighbors (k-NN) Algorithm and Backward Elimination on Cardiotocography Datasets Kurniawan, Muchamad; Yuliastuti, Gusti Eka; Rachman, Andy; Budi, Adib Pakar; Zaqiyah, Hafida Nur
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1996

Abstract

Having a healthy baby is a dream for mothers. Unfortunately, high maternal and fetal mortality has become a vital problem that requires early risk detection for pregnant women. A cardiotocograph examination is necessary to maintain maternal and fetal health. One method that can solve this problem is classification. This research analyzes the optimal use of k values and distance measurements in the k-NN method. This research expects to become the primary reference for other studies examining the same dataset or developing k-NN. A selection feature is needed to optimize the classification method, particularly for improving accuracy results. This study used the cardiotocography dataset from cardiotocograph examinations related to fetal conditions. The cardiotocography dataset consisted of 2,126 records with 22 features and variables. It also had three classification classes, normal, suspect, and pathological, from the Universal Child Immunization Machine Learning Repository website. It employed the K-Nearest Neighbor (k-NN) method and the backward elimination feature with ordinary least squares regression. The test in this research applied the scenarios of three distance calculations, i.e., Euclidean distance, Manhattan distance, and Minkowski distance, as well as four variations of k values. Evaluation of each scenario indicated the accuracy of the confusion matrix and execution time. This study compared K-Nearest Neighbor (k-NN) and Backward Elimination methods with K-nearest neighbor (k-NN) without selection features. The best accuracy of the Backward Elimination and K-Nearest Neighbor (K-NN) methods was 91%, as was the K-Nearest Neighbor (k-NN) method without selection features. Both had similar k values (k = 3) and Manhattan distance. The backward elimination method reduced the number of features from 22 to 14. Meanwhile, the execution times of the Backward Elimination and K-Nearest Neighbor (k-NN) methods got better results as each distance averaged 26.54, 19.23, and 68.09 seconds. K-Nearest Neighbor (k-NN) execution times without selection features were 26.83, 19.39, and 68.84, respectively. In conclusion, backward elimination did not increase accuracy because it yielded the same accuracy. However, backward elimination and K-nearest Neighbor (k-NN) produced faster results, with differences of 29%, 16%, and 75%, respectively.
Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System Swari, Made Hanindia Prami; Rizki, Agung Mustika; Satwika, I Kadek Susila; Handika, I Putu Susila
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3134

Abstract

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years.  Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value.  ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.
Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns Parlika, Rizky; Mustafid, Mustafid; Rahmat, Basuki
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.8.1.1543

Abstract

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.

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