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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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The Implementation and Empirical Analysis of Android Learning Application toward Performance among Students Electronics Engineering Education Hidayat, Hendra; Harmanto, Dani; Orji, Chibueze Tobias; Anwar, Muhammad
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.8.3.2235

Abstract

The integration of technology into the realm of education is experiencing exponential growth, and an ever-evolving selection of media formats is being created to facilitate teaching and learning in a more effective manner. The objective of this research endeavor is to ascertain the degree to which the implementation of learning applications influences the academic achievement of students enrolled in electrical engineering-related programs. To accomplish this objective, learning methodologies and self-directed learning must be implemented as variables that impact students' academic performance. To facilitate this inquiry, a total of 339 representative samples of participants were collected. The collected data were subjected to analysis using the SmartPLS 4.0 software and the Structural Equation Model (SEM) with partial least square (PLS) correction. Following a thorough analysis, it was determined that the data provided an accurate representation of the population. The findings of this study have practical implications-students who engage in self-directed learning and implement effective learning strategies will see a substantial improvement in their overall learning outcomes. Students desire easy access to a variety of educational resources and materials, according to the findings. This aspiration motivates the proliferation of mobile media devices. To facilitate students' access to a diverse range of learning strategies, instructors possess the ability to provide accommodation. These applications benefit students by streamlining the process of obtaining access to learning-supporting materials and resources
Features, Analysis Techniques, and Detection Methods of Cryptojacking Malware: A Survey Kadhum, Laith M; Firdaus, Ahmad; Hisham, Syifak Izhar; Mushtaq, Waheed; Ab Razak, Mohd Faizal
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.8.2.2725

Abstract

Various types of malwares are capable of bringing harm to users. The list of types are root exploits, botnets, trojans, spyware, worms, viruses, ransomware, and cryptojacking. Cryptojacking is a significant proportion of cyberattacks in which exploiters mine cryptocurrencies using the victim’s devices, for instance, smartphones, tablets, servers, or computers. It is also defined as the illegal utilization of victim resources (CPU, RAM, and GPU) to mine cryptocurrencies without detection. The purpose of cryptojacking, along with numerous other forms of cybercrime, is monetary gain. Furthermore, it also intended to stay concealed from the victim's viewpoint. Following this crime, to the author's knowledge, a paper focusing solely on a review of cryptojacking research is still unavailable. This paper presents cryptojacking detection information to address this deficiency, including methods, detection, analysis techniques, and features. As cryptojacking malware is a type that executes its activities using the network, most of the analysis and features fall into dynamic activities. However, static analysis is also included in the security researcher’s option. The codes that are involved are opcode and JavaScript. This demonstrates that these two languages are vital programming languages to focus on to detect cryptojacking. Moreover, the researchers also begin to adopt deep learning in their experiments to detect cryptojacking malware. This paper also examines potential future developments in the detection of cryptojacking.
Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval Azhar, Yufis; Akbi, Denar Regata
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.8.3.2179

Abstract

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.
A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System Khaleefah, Shihab Hamad; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Khattak, Umar Farooq; Yaacob, Siti Salwani; Alanda, Alde
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.8.2.2701

Abstract

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method Andrian, Rafi; Putra Kusuma, Gede
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.8.3.2247

Abstract

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements
Lightweight Image Encryption Based on A Hybrid Approach Jabbar Altaay, Alaa A.; N. Hasoon, Jamal; Kassim Albahadily, Hassan
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.8.2.2757

Abstract

A secure image could be achieved by encryption, a technique for securing images over different media transmission lines with privacy and keeping them safe for the receiver. This paper proposes an image encryption approach to achieve excellent security by combining a lightweight encryption algorithm with the chaotic Peter De Jong map. The Lilliput algorithm, lightweight encryption, uses the Peter De-Jones map to produce keys. The suggested approach achieved a suitable level of complexity that matched the historical demands for transmission images. Two methods were used to conduct the tests on a standard image collection: an encrypted image and a generated key. Standard metrics find the similarity between the input and output images to achieve an accurate proposal performance. The encrypted image's entropy was assessed and discovered that it matched the original image values exactly. The results were satisfactory regarding obtaining a precise correlation rate between the original and encrypted photos. The decryption and reconstruction of the image were completed quickly and steadily, with a high success rate and excellent outcomes. The proposed approach was evaluated on a dataset of well-known test photos with unique features, including varying degrees of lightness and shade to create the perfect test.
Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis Nazrin, S.N.; Zaman, Halimah Badioze; Jothi, Neesha; Jouay, Doha; Lahrach, Badreddine; Halimah, M.K.
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.8.3.3097

Abstract

The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research.
Deep Learning Models for Dental Conditions Classification Using Intraoral Images Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Bayu Dewantara, Bima Sena; Brahmanta, Arya
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.8.3.1914

Abstract

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations. 
Web and Android-based Test Application Development and its Implementation on Final Semester Examination Ambiyar, -; Panyahuti, -; Devega, Army Trilidia; Islami, Syaiful
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.8.2.2120

Abstract

This research aims to revolutionize the examination process in vocational schools by developing the FlyExam application, an Android-based test platform derived from improvements to the TCExam interface. The core goal was to create a powerful, easy-to-use, and effective tool for semester assessment. Following a Research and Development (R&D) approach, this research uses a 4D model: Define, Design, Develop, and Disseminate. Validation procedures require expert evaluation of the technical aspects and usability of the application. At the same time, practicality is assessed through engagement with students and teachers, and effectiveness is measured by student performance. Expert reviews and user feedback confirm the validity and practicality of the application. During implementation, the LAN network topology proved to be a conducive environment for conducting semester exams, increasing the efficiency and reliability of the testing process. The integration of TCExam and FlyExam on mobile devices shows the potential of transitioning from traditional paper-based exams to digital platforms, offering greater flexibility and accessibility. Future research efforts could explore FlyExam's scalability and adaptability in various educational contexts and its long-term impact on assessment practices and academic outcomes. Additionally, ongoing improvements based on user feedback can lead to further improvements and the incorporation of new features, ensuring FlyExam remains relevant and effective in meeting evolving vocational education needs. In summary, the development of FlyExam represents significant progress in the modernization of assessment methodology, with the potential to simplify the process and improve the learning experience in vocational schools.
Modified Alexnet Architecture for Classification of Cassava Based on Leaf Images Sholihin, Miftahus; Md Fudzee, Mohd Farhan; Ismail, Mohd Norasri; Wati, Efi Neo; Arshad, Mohamad Syafwan; Gusman, Taufik
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.8.3.2966

Abstract

The objective of this study is to address the drawbacks of conventional classification approaches through the implementation of deep learning, specifically a modified AlexNet. The primary aim of this study is to precisely categorize the four distinct varieties of cassava, namely Manggu, Gajah, Beracun, and Kapok. The cassava dataset was obtained from farmers in Lamongan, Indonesia, and was used as a source of information. Data collection on cassava leaves was carried out with agricultural research specialists. A total of 1,400 images are included in the dataset, with 350 images corresponding to each variety of cassava produced. The central focus of this research lies in a comprehensive evaluation of the modified AlexNet architecture's performance compared to the original AlexNet architecture for cassava classification. Multiple scenarios were examined, involving diverse combinations of learning rates and epochs, to thoroughly assess the robustness and adaptability of the proposed approach. Among the evaluation criteria that were rigorously examined were accuracy, recall, F1 score, and precision. These metrics were used to determine the predictive capabilities of the model as well as its potential utilization in the actual world. The results show that the modified AlexNet design has better performance than the original AlexNet for recall, accuracy, precision, and F-1 score, all achieving a rate of 87%. In situations where a learning rate of 0.0001 and an epoch count of 150 are utilized, the performance of the approach stands out significantly, displaying an excellent level of competency. Nevertheless, it is crucial to recognize that distinct fluctuations in performance were noted within particular contexts and with diverse learning rates.

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