JOIV : International Journal on Informatics Visualization
Not a member yet
786 research outputs found
Sort by
An Android Malware Detection System using a Knowledge-based Permission Counting Method
Get PDFAs the number of cases of damage caused by malicious apps increases, accurate detection is required through various detection conditions, not just detection using simple techniques. In this paper, we propose a knowledge-based machine learning method using authority information and adding its usage counting features. This method is classifying training apps and malicious apps through machine learning using permission features in manifest.xml of Android apps. As a result of the experiment, accuracy, recall, precision, F1 score are 99.01%, 97.70%, 100.0%, 99.01%, respectively. Since Recall is higher than other indicators, it accurately predicts malicious apps as malicious. In other words, the proposed system is effective in preventing the distribution of malicious apps
Development of Programming Log Collection System Requirements Using Interface Requirement Analysis Techniques
Get PDFAs software affects each industry, companies are increasingly trying to recruit talent. Despite the interest and investment of companies, it is difficult to find talents with technical expertise and specialization in a specific field at the same time. As a result, companies have begun to discover talents who can overcome their lack of expertise through talents with high problem-solving skills. Countries worldwide that felt the need to discover competitive talents began to show changes in education for nurturing talents. In particular, the results of the expansion and increase of programming education that cultivate problem-solving ability have begun to be seen. However, programming education is different from existing education, and many learners have difficulties with the introductory process due to the difficult debugging process. In order to analyze the difficulties of introductory learners and support their learning, a system that can collect data from the programming process and analyze behavior types is required. There are several methods for deriving the system requirements, but the interface requirements analysis method was selected in this study. We approached how to process data in the system by deriving the type of data that the system administrator wants to collect. This study laid the foundation for a system that can analyze the programming process of introductory learners by deriving the functional and non-functional requirements required by the data collection system through interface requirements analysis
Classifying Vehicle Types from Video Streams for Traffic Flow Analysis Systems
Get PDFThis paper proposes a vehicle types classification modelfrom video streams for improving Traffic Flow Analysis (TFA) systems. A Video Content-based Vehicles Classification (VC-VC) model is used to support optimization for traffic signal control via online identification of vehicle types.The VC-VC model extends several methods to extract TFA parameters, including the background image processing, object detection, size of the object measurement, attention to the area of interest, objects clash or overlap handling, and tracking objects. The VC-VC model undergoes the main processing phases: preprocessing, segmentation, classification, and tracks. The main video and image processing methods are the Gaussian function, active contour, bilateral filter, and Kalman filter. The model is evaluated based on a comparison between the actual classification by the model and ground truth. Four formulas are applied in this project to evaluate the VC-VC model’s performance: error, average error, accuracy, and precision. The valid classification is counted to show the overall results. The VC-VC model detects and classifies vehicles accurately. For three tested videos, it achieves a high classification accuracy of 85.94% on average. The precession for the classification of the three tested videos is 92.87%. The results show that video 1 and video 3 have the most accurate vehicle classification results compared to video 2. It is because video 2 has more difficult camera positioning and recording angle and more challenging scenarios than the other two. The results show that it is difficult to classify vehicles based on objects size measures. The object's size is adjustable based on the camera altitude and zoom setting. This adjustment is affecting the accuracy of vehicles classification
Design Thinking Approach for User Interface Design and User Experience on Campus Academic Information Systems
Get PDFAbstract—Currently, an academic system with structured data is needed for all lecture institutions, especially universities in Indonesia, Siliwangi University, with its academic system, namely the Campus Academic Information System (SIMAK). Over time, complaints from the visual aspect and user experience that did not keep up with the times became a new problem for SIMAK with student access rights. Therefore, the UI/UX aspect in developing an application is vital in accessing the available features. In this study, the method applied is Design Thinking to develop SIMAK WEB and SIMAK MOBILE application designs according to the data and input obtained from users. The research stages include Empathize, Define, Ideate, Prototype, and Test. The final result is user testing from expert users with ten examiners, each producing a success rate percentage of 100% for SIMAK WEB and a percentage of 90% for SIMAK MOBILE. In addition, the User Experience Questionnaire (UEQ) assessment from the same expert user plus end-users of 39 respondents and 33 respondents for web and mobile respectively increased 6 UEQ scales, namely Attractiveness, Clarity, Efficiency, Accuracy, Stimulation and lastly especially Novelty which has an increase of 5.286 and 5.264 from the initial value of -0.880. The Novelty scale is the only scale with a negative impression initially and was successfully evaluated in this study with a good score. The implication for further research is that an in-depth study and application of unique methods regarding the conversion of designs into prototype form is necessary so that coding can run smoothly. Keywords— Design Thinking, Campus Academic System, User Experience, User Experience Questionnaire, User Interfac
High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease
Get PDFIn some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system
Investigation on Java Mutation Testing Tools
Get PDFSoftware Testing is one of the most significant phases within the software development life cycle since software bugs can be costly and traumatic. However, the traditional software testing process is not enough on its own as some undiscovered faults might still exist due to the test cases’ inability to detect all underlying faults. Amidst the various proposed techniques of test suites’ efficiency detection comes mutation testing, one of the most effective approaches as declared by many researchers. Nevertheless, there is not enough research on how well the mutation testing tools adhere to the theory of mutation or how well their mutation operators are performing the tasks they were developed for. This research paper presents an investigative study on two different mutation testing tools for Java programming language, namely PIT and µJava. The study aims to point out the weaknesses and strengths of each tool involved through performing mutation testing on four different open-source Java programs to identify the best mutation tool among them. The study aims to further identify and compare the mutation operators of each tool by calculating the mutation score. That is, the operators’ performance is evaluated with the mutation score, with the presumption that the more prominent the number of killed mutants is, the higher the mutation score, thus the more effective the mutation operator and the affiliated tool.Â
Student Engagement Mechanism of Online Learning: The Effect of Service Quality on Learning Management System
Get PDFSince typical classrooms do not include discussions, collaborative learning, or interactive learning activities, engagement is a major challenge in distant learning. Online learning satisfaction levels should be measured as evaluation material for future implementation. Although online learning has many advantages, a high dropout rate remains a significant challenge. This study investigates how higher education students' engagement and satisfaction with online learning are enhanced by information, system, and service aspects. The research design was quantitative research, and we used a questionnaire to collect data. The questionnaire was designed on a five-rating interval scale. The sampling technique was simple random sampling. The target minimum sample was counted using the Slovin method, and 206 undergraduate students taking online courses were surveyed online. The model was tested using structural equation modeling partial least squares (SEM PLS). This method is useful for investigating the relationship between constructs. The model was tested with the application of the SmartPLS program. The results revealed a positive and significant effect of system quality, information quality, service quality to student engagements, and their impact on student satisfaction, both direct and indirect. This study answers the literature gap and verifies the importance of online learning quality factors on students’ satisfaction and engagement. These results are expected to help to improve online learning in higher education settings, specifically on students' engagement and satisfaction, leading to perseverance and success
Entropy Based Method for Malicious File Detection
Get PDFRansomware is by no means a recent invention, having existed as far back as 1989, yet it still poses a real threat in the 21st century. Given the increasing number of computer users in recent years, this threat will only continue to grow, affecting more victims as well as increasing the losses incurred towards the people and organizations impacted in a successful attack. In most cases, the only remaining courses of action open to victims of such attacks were the following: either pay the ransom or lose their data. One commonly shared behavior by all crypto ransomware strains is that there will be attempts to encrypt the victims’ files at a certain point during the ransomware execution. This paper demonstrates a technique that can identify when these encrypted files are being generated and is independent of the strain of the ransomware. Previous research has highlighted the difficulty in differentiating between compressed and encrypted files using Shannon entropy, as both file types exhibit similar values. Among the experiments described in this study, one showed a unique characteristic for the Shannon entropy of encrypted file header fragments, which was used to differentiate between encrypted files and other high entropy files such as archives. The Shannon entropy of encrypted file header fragments has a unique characteristic in one of the tests discussed in this study. This property was used to distinguish encrypted files from other files with high entropy, such as archives. To overcome this drawback, this study proposed an approach for test case generation by enhancing the entropy-based threat tree model, which would improve malicious file identification. The file identification was enhanced by combining three entropy algorithms, and the test case was generated based on the threat tree model. This approach was then evaluated using accuracy measurements: True Positive, True Negative, False Positive, False Negative. A promising result is expected. This method solves the challenge of leveraging file entropy to distinguish compressed and archived files from ransomware-encrypted files in a timely manner
Classification of Tempeh Maturity Using Decision Tree and Three Texture Features
Get PDFTempe is an average food from Indonesia, eaten in Indonesia. Even today, tempe is around the world, and vegans around the world use tempeh as a meat substitute. This study plans to work on the accuracy of tempe characterization by utilizing the three-element extraction technique and the choice tree arrangement strategy. This research uses a decision tree method with three texture features in its classification. The results obtained indicate that this method has the highest Gabor channel level, including extraction, which is 71% accuracy, the split proportion is 10;90 and the lowest is 60% with parted balance of 90:10. The most important level value of GCLM extraction precision is 86% with a split proportion of 90;10 and the lowest price level and 60% level with a split ratio of 10;90 for Wavelet including the highest extraction rate price is 77%. It can be said that from the extraction of three elements, GLCM is the element extraction with the highest value from Gabor and Wavelet, including extraction at a split proportion of 10:90 by 86%. The test shows the Featured Tree highlight designation. The extraction technique was superior to different strategies for interaction characterization of tempe development quality. In the next research, improve the accuracy performance so that it can reach 100% using the CNN deep learning method. Then you can also add Support Vector Machine (SVM) and Naive Bayes methods based on the GLCM Extraction feature
Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network
Get PDFLearning content can be identified through text, images, and videos. This study aims to predict the learning content contained on YouTube. The images used are images contained in the learning content of the exact sciences, such as mathematics, and social science fields, such as culture. Prediction of images on learning content is done by creating a model on CNN. The collection of datasets carried out on learning content is found on YouTube. The first assessment was performed with an RMSProp optimizer with a learning rate of 0.001, which is used for all optimizers. Several other optimizers were used in this experiment, such as Adam, Nadam, SGD, Adamax, Adadelta, Adagrad, and Ftrl. The CNN model used in the dataset training process tested the image with multiple optimizers and obtained high accuracy results on RMSprop, Adam, and Adamax. There are still many shortcomings in the experiments we conducted in this study, such as not using the momentum component. The momentum component is carried out to improve the speed and quality of neural networks. We can develop a CNN model using the momentum component to obtain good training results and accuracy in later studies. All optimizers contained in Keras and TensorFlow can be used as a comparison. This study concluded that images of learning content on YouTube could be modeled and classified. Further research can add image variables and a momentum component in the testing of CNN models