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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 51 Documents
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Search results for , issue "Vol 8, No 3 (2024)" : 51 Documents clear
The Rise of Deep Learning in Cyber Security: Bibliometric Analysis of Deep Learning and Malware Kamarudin, Nur Khairani; Firdaus, Ahmad; Osman, Mohd Zamri; Alanda, Alde; Erianda, Aldo; Kasim, Shahreen; Ab Razak, Mohd Faizal
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.8.3.1535

Abstract

Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y.
Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm Binti Mohd Sabudin, Umi Syahirah; Makhtar, Siti Noormiza; Nor, Elya Mohd; Muhamed, Siti Anizah; Mohd Sani, Fareisya Zulaikha; Kamarudin, Nur Diyana
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.8.3.2040

Abstract

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.
Optimizing Retail Recommendation via Similarity Measures and Machine Learning Approach Bhattacharijee, Arpita; Ting, Choo Yee; Ghauth, Khairil Imran; Peng, Loh Yuen; Hashim, Noramiza; Mohd Isa, Wan Noorshahida; Suvon, Injamul Haque; Matsah, Wan Razali
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.8.3.2360

Abstract

Finding a suitable retail business with potential success in a specific location can be challenging for retailers. The process is often lengthy and inconsistent due to the subjective nature of expert opinions. Previous research has demonstrated several techniques that consider numerous influential attributes for location optimization problems. However, while many studies rely on a business's core data for analytical purposes, accessing this information is often a significant constraint. This study aims to address the challenge of extracting valuable location features to enhance the profitability of chosen businesses despite the inaccessibility of core business data. The proposed methodology involves three main steps. First, an analytical dataset must be created by utilizing geographic and demographic information. Second, we conduct similarity measures by applying Manhattan distance to the entire analytical dataset, using an ideal business outlet that contains the footfall information. Through this process, we can identify businesses that share similar characteristics with popular outlets. Finally, several supervised machine learning models are trained to employ the extracted meta-features. Experimental results show that the XGBoost classifier performs best with an 87% accuracy score, outperforming the baseline models. The proposed methodology in this research presents a robust framework that demonstrated remarkable efficiency in achieving the stated objectives and improving the performance of retail business recommendations within a given location. Future work could consider a broader range of features that could potentially enhance model performance by applying ensemble learning. 
Comparative Analysis of Imputation Methods for Enhancing Predictive Accuracy in Data Models Zamri, Nurul Aqilah; Jaya, M. Izham; Irawati, Indrarini Dyah; Rassem, Taha H.; Rasyidah, -; Kasim, Shahreen
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.8.3.1666

Abstract

The presence of missing values within datasets can introduce a detrimental bias, significantly impeding the predictive algorithm's ability to discern patterns and accurately execute prediction. This paper aims to elucidate the intricacies of data imputation methods, providing a more profound understanding of prevalent imputation methods, including list-wise deletion (IGN), mean imputation (AVG), K-Nearest Neighbors (KNN), MissForest (MF), and Predictive Mean Matching (PMM). The dataset employed in this study consists of financial data about S&P 500 companies in the Compustat North America database. The training and validation dataset encompasses 1973 instances, consisting of data during the fourth quarter of 2009, the first quarter of 2010, and the third quarter of 2014. Within this set, 457 missing values were identified and imputed. The test dataset comprises 197 randomly selected instances from the fourth quarter of 2014, equivalent to ten percent of the total instances in the training dataset. The evaluation findings prominently position the dataset derived from MF imputation as the leading performer among all the imputed datasets. The insights derived from this study are intended to assist practitioners in making informed choices when selecting the most suitable data imputation method, particularly in the context of predictive modeling tasks.
Shuttlecock Detection Using Residual Learning in U-Net Architecture Haq, Muhammad Abdul; Tarashima, Shuhei; Tagawa, Norio
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.8.3.2132

Abstract

This paper introduces an enhanced approach for detecting shuttlecock. Detecting fast-moving objects, such as a shuttlecock, is crucial in various applications, including badminton video analysis and object tracking. Many deep-learning techniques have been proposed in literature to address this challenge. However, low image quality, motion blur, afterimage, and short-term occlusion can hinder accurate detection. To overcome these limitations, this research focuses on improving the existing method called TrackNetV2, which utilizes the U-Net architecture. The primary enhancement proposed in this study is incorporating residual learning within the U-Net architecture, emphasizing processing speed, prediction accuracy, and precision. Specifically, each U-Net layer is augmented with a residual layer, enhancing the network's overall performance. The results demonstrate that our proposed method outperforms the existing detection accuracy and reliability technique.
Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear Minarno, Agus Eko; Izzah, Tsabita Nurul; Munarko, Yuda; Basuki, Setio
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.8.3.2154

Abstract

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.
IoT Smart Gardening on Herbal Plant and Analytic Virtualization Platform System Abdul Ghafar, Nor Iskandar; Kassim, Murizah
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.8.3.2541

Abstract

The Internet of Things (IoT) is an emerging technology developing recently. The technology of IoT allows users to communicate with controllers and sensors, which are widely used in the agricultural sector, such as farming and weather forecasts. Recently, it has been identified that traditional herbal home gardens are difficult to monitor where herbal plants are sensitive and need a proper monitor. This research has developed an IoT smart gardening for herbal plant systems that helps to analyze four herbal plant types: Aloe Barbadensis miller, Salvia Rosmarimus, Clinacanthus nutans, and Curcuma. The system was designed using a NodeMCU ESP32 controller, soil moisture sensor, and DHT11 temperature and humidity sensor. The collected data was monitored in real-time on the Blynk platform via mobile phones and web browsers on sunny and rainy days. The result shows that the highest temperature on a sunny day was captured at 30°C, and the lowest was 26°C. However, during a rainy day, the highest temperature captured is 28°C and the lowest was 24°C. The highest humidity percentage on a sunny day was 80%, and the lowest was 40%, while the highest humidity on a rainy day was 98%, and the lowest was 68%. The soil moisture data that captured the water content of the soil of each herbal plant on a sunny day has been lost due to high temperatures. However, each herbal plant barely lost its water content due to low temperature and high humidity percentage conditions. The comparison showed that temperature, humidity, and weather could affect the water content in the soil. This research has significantly improved the agriculture sector in monitoring herbal plants and moving from traditional planting with an IoT system concept.
Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model Hamad Khaleefah, Shihab; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Farooq Khattak, Umar; Ahmed Jubair, Mohammed; Afyenni, Rita
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.8.2.2758

Abstract

A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.
Assessment of Student Satisfaction with E-learning in Jordan Using TAM and UTAUT as a Mediator for Synchronous and Asynchronous Learning Aldiabat, Khaled; Gharaibeh, Malik; AlQudah, Nidal
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.8.3.2501

Abstract

This study aims to evaluate student satisfaction with e-learning in Jordan by combining the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). This research also delved into the study of the mediating role played by both synchronous and asynchronous learning and their impact on students’ satisfaction with e-learning. Data was collected through a questionnaire distributed to 590 students studying at four private Jordanian universities in northern Jordan. To analyse the study model, Smart PLS was used because it offers a robust methodology for such complex constructs. The results show that both synchronous and asynchronous learning significantly and positively affect students’ satisfaction with e-learning. The results also revealed that the relationship between synchronous learning and perceived usefulness was statistically significant. Regarding asynchronous learning, the relationship with perceived ease of use was positive and statistically significant. Turning to the TAM and UTAUT variables as independent variables, the results revealed a direct and statistically significant relationship between these variables and students’ satisfaction with e-learning. This study provides valuable insights into the factors that influence student satisfaction with e-learning in Jordan and presents a new approach by integrating synchronous and asynchronous learning as a mediator within the TAM and UTAUT frameworks. The findings highlight the nuances of technology acceptance and use in the context of e-learning, which can benefit educational institutions and policymakers in enhancing the e-learning experience for students in Jordan.
Application Development Model E-Commerce in Traditional Markets Using the TOGAF Framework Irawan, Bei Harira; Prihadi, Deddy; Rahmatika, Dien Noviany; Nugroho, Catur; Waskita, Jaka; Meyrawati A, Octania
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.8.3.2388

Abstract

The existence of traditional markets is increasingly being eroded by the presence of online market models that already utilize various e-commerce platforms. However, the TOGAF Framework provides a ray of hope. Even though the existence of traditional markets is unlikely to disappear, if traders in traditional markets do not try to adapt to using e-commerce, it will have an impact on losing customers, losing turnover, and will even affect the acceleration of their business. Architectural design for system development in traditional market activities can provide an important picture of how to advance traditional market industries that will not be out of competition with modern markets. TOGAF (The Open Group Architecture Framework) provides a detailed method of how patterns build, manage, and implement enterprise architecture and information systems called the Architecture Development Method. Analysis using the TOGAF Framework provides an overview of how to plan, design, develop and implement information system architectures for traditional markets. The results of this design analysis are an information technology architectural design which is the basis for traditional market managers, especially in Indonesia, in advancing the business processes of traders in the market so that they are not unable to compete with modern markets but with gap analysis constraints that must really be considered with regard to internet infrastructure problems in the market, sellers who are not fully able to use technology due to age and not all forms of goods on the market can be sold through e-commerce platforms. This gap analysis will make e-commerce implementation slow to implement.

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