Jurnal Online Informatika
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Designing a Virtual Campus Tour using Image Stitching Techniques to Provide Information on College Entrance Test
Full text linkThe University of Bengkulu administers college entrance exams, however some test takers still require assistance in locating the correct room, despite the building being marked. It is crucial to avoid errors in finding the right test room, as it can cause potential students to waste valuable time. Therefore, a more precise and practical solution is necessary to provide information on test locations. This study designs a location-based virtual tour that offers a 360-degree view, providing information on the location of each building and the conditions inside and outside each test room. The virtual tour encompasses 81 buildings, including test rooms, with 28 to 32 images captured at each location, then stitched together using image stitching techniques. The goal of the virtual tour is to create a comprehensive view of the test location and provide more detailed information on the room\u27s condition. Furthermore, the usability of this virtual tour was tested on 140 high school students as potential test participants, utilizing the System Usability Scale (SUS) to evaluate its effectiveness, resulting in a score of 72.19. In other words, the virtual tour was found to be an effective tool in helping users understand the test location
Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy
Full text linkReal-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model\u27s architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy\u27s 2020 research, this modification enhances the model\u27s performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications\u27 efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems\u27 speed-accuracy trade-off
The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students
Full text linkChoosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error
Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review
Full text linkArtificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing
Pengembangan Game Augmented Reality Pembelajaran Bahasa Pemrograman Dasar Menggunakan Agile Scrum
No full textThe agile scrum methodology for augmented reality development increases project team efficiency. Private campus are frequently confronted with the dilemma of new students with various backgrounds that come not only from vocational high schools but also from high schools. First year students in the informatics study programme come not only from vocational informatics high schools, but also from high schools that specialize in social studies and languages. This is a difficult task in terms of imparting a comprehension of the fundamentals of programming. This study develops augmented reality in order to teach HTML and Javascript. By combining basic principles with gaming, the proposed augmented reality (AR) makes programming interesting. Players must comprehend their programming logic in order to be immersed in a virtual environment by answering coding bug questions. During usability testing, the System Usability Scale (SUS) assesses user happiness and AR knowledge. Participants from various programming backgrounds were tested on their knowledge of programming languages. According to usability research, 59% of people found AR programming languages useful for learning and understanding basic programming languages. AR and Agile Scrum make programming more enjoyable. This study demonstrates how augmented reality can be used to teach programming languages. These findings imply that Agile Scrum and AR methods can improve learning and programming foundations. More research and development could lead to more complete and complicated AR learning environments for programming instruction.Game instruksional sangat cocok untuk Agile Scrum, yang berhasil dalam proyek pengembangan perangkat lunak yang bergerak cepat. Studi ini membuat game augmented reality untuk mengajarkan HTML dan Javascript. Game augmented reality (AR) yang disarankan membuat pembelajaran pemrograman menjadi menyenangkan dengan menggabungkan konsep dasar ke dalam gameplay. Membenamkan pemain di lingkungan virtual dengan menjawab pertanyaan bug pengkodean mengharuskan mereka menggunakan bakat pemrograman mereka. Skala Kegunaan Sistem (SUS) mengukur kepuasan pengguna dan pengetahuan game AR selama pengujian kegunaan. Peserta dengan latar belakang pemrograman berbeda diuji pemahaman mereka tentang bahasa pemrograman inti game AR. Sebagian besar gamer memahami bahasa pemrograman dasar, menurut pengujian kegunaan. Game AR dan Agile Scrum menjadikan belajar pemrograman menyenangkan dan mudah. Penelitian ini menunjukkan bagaimana game augmented reality dapat mengajarkan bahasa pemrograman. Temuan menunjukkan bahwa prosedur Agile Scrum dan game AR interaktif dapat meningkatkan dasar-dasar pembelajaran dan pemrograman. Lebih banyak penelitian dan pengembangan dapat mengarah pada lingkungan pembelajaran AR yang lebih lengkap dan kompleks untuk pengajaran pemrograman.
 
Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method
Full text linkMalware is intentionally designed to damage computers, servers, clients or computer networks. Malware is a general term used to describe any program designed to harm a computer or server. The goal is to commit a crime, such as gaining unauthorized access to a particular system, so as to compromise user security. Most malware still uses the same code to produce another different form of malware variants. Therefore, the ability to classify similar malware variant characteristics into malware families is a good strategy to stop malware. The research is useful for classifying malware on malware samples presented as bytemap grayscale images. The malware classification research focused on 25 malware classes with a total of 9,029 images from the Malimg dataset. This research implements the VGG-16 and InceptionResNet-V2 architectures by running 2 different scenarios, scenario 1 uses the original dataset and the other scenario uses the undersampled dataset. After building the model, each scenario will get an evaluation form such as accuracy, precision, recall, and f1-score. The highest score was obtained in scenario 2 on the VGG-16 method with a score of 94.8% and the lowest in scenario 2 on the InceptionResNet-V2 method with a score of 85.1%
Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance
Full text linkVirtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website
Data Mining for Heart Disease Prediction Based on Echocardiogram and Electrocardiogram Data
Full text linkTraditional methods of detecting cardiac illness are often problematic in the medical field. The doctor must next study and interpret the findings of the patient\u27s medical record received from the electrocardiogram and echocardiogram. These tasks often take a long time and require patience. The use of computational technology in medicine, especially the study of cardiac disease, is not new. Scientists are continuously striving for the most reliable method of diagnosing a patient\u27s cardiac illness, particularly when an integrated system is constructed. The study attempted to propose an alternative for identifying cardiac illness using a supervised learning technique, namely the multi-layer perceptron (MLP). The study started with the collection of patient medical record data, which yielded up to 534 data points, followed by pre-processing and transformation to provide up to 324 data points suitable to be employed by learning algorithms. The last step is to create a heart disease classification model with distinct activation functions using MLP. The degree of classification accuracy, k-fold cross-validation, and bootstrap are all used to test the model. According to the findings of the study, MLP with the Tanh activation function is a more accurate prediction model than logistics and Relu. The classification accuracy level (CA) for MLP with Tanh and k-fold cross-validation is 0.788 in a data-sharing situation, while it is 0.672 with Bootstrap. MLP using the Tanh activation function is the best model based on the CA level and the AUC value, with values of 0.832 (k-fold cross-validation) and 0.857 (bootstrap).Prediction of heart disease is a complicated thing when done with traditional medical analysis. Sometimes the prediction results are not as expected and even fail. The development of computational science offers a number of alternatives to help predict the type of heart disease based on the availability of patient medical record data. One alternative that can be used is data mining. The goal of this study is to implementing data mining to predict the type of heart disease based on medical record data from echocardiograms and electrocardiograms. Multilayer perceptron backpropagation (MLP) is used in this study. The results show that MLP with the Tanh activation function is a better predictive model than logistic and Relu. The classification accuracy level (CA) for MLP with Tanh is 0.788 for a data sharing scenario using k-fold cross validation and 0.672 for a data sharing scenario using Bootstrap. From both the CA level and the AUC value, MLP with the Tanh activation function is the best model with a value of 0.832 (k-fold cross validation) and 0.857 (bootstrap)
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent
Full text linkCancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
 
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis
Full text linkPlant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. HLB, caused by gram-negative proteobacteria strains, severely impacts citrus orchards globally, resulting in economic losses. Early detection and classification of HLB-infected plants are crucial for effective disease management. Traditional approaches rely on expert knowledge and time-consuming laboratory tests, hindering rapid detection. This study explores an alternative method using the BEMD algorithm for texture feature extraction and SVM classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on IMF 1, IMF 2, and residue features. The residue component provided the most outstanding level of classification accuracy, reaching 77% for two classes, 72% for three types, and 61% for four classes. In two categories, IMF 1 performed at a 72% accuracy rate, and in four other areas, it performed at a 51% accuracy rate, making it competitive. IMF 2 demonstrated lower accuracy, ranging from 43% for three classes to 57% for two categories. The findings highlight the significance of the image residue component, outperforming IMF features in HLB classification accuracy. The BEMD algorithm coupled with SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies using GLCM-SVM techniques.