JOIV : International Journal on Informatics Visualization
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The Implementation and Empirical Analysis of Android Learning Application toward Performance among Students Electronics Engineering Education
Get PDFThe 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 resource
Batik Image Representation using Multi Texton Co-occurrence Histogram
Get PDFThis paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems
Why People Benefit from Online Learning: Empirical Assessment from Jordan
Get PDFMost countries have imposed online learning on universities and schools due to the COVID-19 pandemic. These days, despite the end of the impact of the COVID-19 pandemic on the educational sector, many countries in the world are still adopting this type of education and trying to develop its methods due to the many benefits it provides. The main objective of conducting this study is to determine the main factors affecting the acceptance of online learning in Jordan. The data were analyzed using SmartPLS 4. 940 questionnaires were distributed in Irbid and Amman. The study's results supported the hypotheses, as it was found that the acceptance of e-learning is statistically and positively associated with the four variables. This study provides essential guidelines for decision-makers and those in charge of the educational process, as it supports the body of knowledge with new variables that were not used in previous studies. Online learning is considered inevitable for adoption in universities and schools, especially when looking at the benefits that institutions derive from its adoption. Saving time, effort, and costs are the most important benefits when applying online learning. This study attempted to determine the main factors affecting the acceptance of online learning in Jordan. The study's results aligned with the hypotheses that technological development, women's empowerment, disabilities, and environmental benefits significantly affect the acceptance of online learning. This study presents a new model and theoretical framework that researchers in this field can build upon
A New Approach of Steganography on Image Metadata
Get PDFIn this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography. The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results
Understanding User Engagement Strategies for Podcasts Videos on Youtube in Indonesia: A Study on Content Creation
Get PDFCOVID-19 has transformed human life by utilizing technology to obtain information. Based on Katadata.com, Indonesia ranks second in the world's highest number of podcast listeners in the third quarter of 2021, accounting for 35.6% of the total internet users. Based on YouTube user statistics from Global Media Insight, Indonesia also ranks fourth globally for the highest number of YouTube users in 2023, totaling 139 million. Thus, this study aims to examine the factors that can influence the strategy to attract the right audience in building podcast content and provide recommendations for appropriate user engagement by comparing the genres of current issues and business & finance podcasts on YouTube Indonesia. The research method used is descriptive analytics, using the open-source Netlytics tool to analyze text and automatically summarize and visualize public online conversations on YouTube. The results of this study indicate that current issue genres are more prevalent in Indonesian society, with one of the most influential factors being the topic and guests to currently viral podcasts. This study also analyzes other factors that influence user engagement. Therefore, the findings of this research can be utilized as an opportunity for companies/institutions to enhance their branding/promotion through YouTube video podcasts. This research can also serve as a reference for other podcast content creators in building and improving user engagement on their YouTube channels to attract more interest from Indonesian society
Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model
Get PDFVocational 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
Implementation of Multi Extension in Blockchain-Based IoT Platform for Industrial IoT Devices
Get PDFThe rise of the Internet of Things (IoT) has led to the creation of technologies to improve human life. IoT involves integrating the Internet with the physical world, spanning applications like smart homes, industries, supply chains, academia, and more. By the end of 2020, around 212 billion IoT devices were globally deployed, presenting substantial opportunities for manufacturers and diverse applications. There have been numerous implementations of IoT across various fields, including Blockchain IoT (B-IoT), Artificial Intelligence of Things (AIoT), Digital Twin, and new communication protocols like the Matter protocol. We conducted a comprehensive testing of the blockchain (B-IoT) extension system on various bandwidths and scenarios, such as blockchain API execution time, speed, retention performance, and smart contract vulnerability testing. Our testing has been successful, and several messaging systems were used. Kafka was recommended to overcome the pending transaction problem caused by unprocessed messages. Our smart contract exhibited high severity. The Artificial Intelligence of Things extension, tested on real environments for person and vehicle counters, has shown successful results. Digital Twin, integrated into the IoT platform to perform and control 3D assets such as the postgraduate PENS building, has demonstrated efficient performance. Matter protocol achieved an average task execution speed of 0.48 tasks per second. Matter P2P communication was also successfully tested in this research by implementing the Access Control List (ACL) command
Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data
Get PDFHigh-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). Comparison of algorithms conducted using the same datasets. The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds
Deep Learning Models for Dental Conditions Classification Using Intraoral Images
Get PDFThis 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.
Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images
Get PDFBrain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient car