JOIV : International Journal on Informatics Visualization
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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<![CDATA[Performance Comparison of Zevenet Multi Service Load Balancing with Least Connection and Round Robin Algorithm ]]>
<![CDATA[Ma'arifah, Windiya]]>;
<![CDATA[Sarmini, Sarmini]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.1985
<![CDATA[Amikom Purwokerto University concentrates on Technology and Digital Business. This requires technology to be utilized optimally. The use of technology, especially internships, will make various jobs easier. KRS online is taking lecture schedules online via the AMIKOM Purwokerto Student website. There are several problems with the web server that arise due to the increasing need for information access, which causes the data traffic load to increase. Increased data traffic causes workload overload, resulting in server downtime. Experimental methods were used in this research to look for the causes of the web server's downtime. Then, implement the technology. The purpose is to evaluate the Zevenet load balancer performances by comparing the round-robin and least-connection algorithms. The decision is which algorithm will be used best to implement the Zevenet Load balancer to achieve a more efficient backend server traffic cluster distribution. The TIPHON standard Quality of Service parameters used in Zevenet Load Balancer performance testing are throughput, delay, jitter, packet loss, and CPU usage. The quality-of-service parameter test results show that the Zevenet Load Balancer with the round-robin algorithm has superior performance and shows less CPU usage. It is concluded that using the round-robin algorithm in implementing the Zevenet load balancer to overcome the problem of data traffic load sharing and minimize server downtime on the Student Amikom Purwokerto web server is more appropriate and more effective.]]>
<![CDATA[An Experimental Study on Deep Learning Technique Implemented on Low Specification OpenMV Cam H7 Device ]]>
<![CDATA[Asmara, Rosa Andrie]]>;
<![CDATA[Rosiani, Ulla Delfana]]>;
<![CDATA[Mentari, Mustika]]>;
<![CDATA[Syulistyo, Arie Rachmad]]>;
<![CDATA[Shoumi, Milyun Ni'ma]]>;
<![CDATA[Astiningrum, Mungki]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2299
<![CDATA[This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.]]>
<![CDATA[The Relationship among Academic Self-Efficacy, Academic Resilience, and Academic Flow: The Mediating Effect of Intensity Using Learning Management System ]]>
<![CDATA[Syukur, Yarmis]]>;
<![CDATA[Putra, Ade Herdian]]>;
<![CDATA[Ardi, Zadrian]]>;
<![CDATA[Mardian, Vivi]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2216
<![CDATA[University students can have low academic flow when using a Learning Management System (LMS). Three variables are predicted to correlate with the academic flow (FA) of students who use LMS: academic self-efficacy (ASE), academic resilience (AR), and LMS use intensity (LMSI). This study looks at the link between academic self-efficacy, academic resilience, LMS use intensity, and academic flow among university students who use LMS. This study employs a quantitative approach, using correlational approaches and path analysis. Furthermore, 740 Indonesian university students who used LMS participated in this study. This study used the partial least squares-structural equation model (PLS-SEM) to analyze data. This study found that academic resilience and LMS use intensity are both positively and significantly associated with academic flow in university students who use LMS. Furthermore, the current research results show that academic self-efficacy is not directly related to academic flow among university students. Aside from that, the study's findings imply that LMS usage intensity is a deciding variable for academic flow among university students who use LMS and that it can control the link between academic self-efficacy, academic resilience, and academic flow. Academic resilience and LMS use intensity must be considered when improving university students' academic flow using LMS.]]>
<![CDATA[Multi-Head Attention in Residual Networks to Improve Coral Reef Structure Classification ]]>
<![CDATA[Nuranti, Eka Qadri]]>;
<![CDATA[Intizhami, Naili Suri]]>;
<![CDATA[Tassakka, Muhammad Irpan Sejati]]>;
<![CDATA[Areni, Intan Sari]]>;
<![CDATA[Al Ghozy, Osama Iyad]]>;
<![CDATA[Jefri, Muhammad Rivaldi]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2392
<![CDATA[Residual Networks (ResNet) mark a crucial advancement in convolutional neural network architecture, effectively tackling challenges like vanishing gradients for improved pattern detection in various image classification tasks. This study introduces a novel adaptation of the ResNet50 architecture that integrates a multi-head attention mechanism (MHA), coined MHA-ResNet50, for discerning coral reef structures within images. Strategic modifications are applied to the input of each stage, leading to the development of an MHA block, which is augmented by separable convolution. The deliberate inclusion of the MHA block at various stages in identity-block Resnet50, in adherence to multiscale gate principles, precedes its traversal through fully connected layers. Furthermore, we implemented the Stratified K-fold concept to ensure that each fold has a comparable proportion of each class. We successfully assessed the efficacy of the MHA-Resnet50 model in several MHA-block placement scenarios and saw improvements in the accuracy of coral reef structure predictions. The most optimal results were achieved by incorporating four attention blocks (MHA-ResNet50-4), yielding an accuracy rate of 85.23% in recognition of coral structure images, comprising a mere 409 images. This model showcases adaptability to small datasets while delivering commendable performance. The ResNet50 architecture undergoes enhancement in our proposed model by integrating multi-head attention, separable convolution, and multiscale gate principles. The MHA-ResNet50 model substantially advances accurately predicting coral reef structures, demonstrating adaptability to limited datasets. Future lines of this research involve digging deeper into the model design and using more significant amounts and classes of data to strengthen a more comprehensive range of generalizations.]]>
<![CDATA[Development of Automatic Object Detection and IoT for Garbage Pickup Assignment Problem ]]>
<![CDATA[Bayu Setyawan, Erlangga]]>;
<![CDATA[Novitasari, Nia]]>;
<![CDATA[Zahira, Aulia Dihas]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2740
<![CDATA[Waste management remains a challenge in certain cities, particularly in allocating fleets responsible for collecting garbage from temporary disposal sites. Inadequate planning can lead to the accumulation of substantial waste piles. This study aims to enhance truck assignment by considering truck capacity and the collection route. The assignment process incorporates the fundamental concept of the transportation problem, precisely the northwest corner method. The volume of waste transported aligns with the resident or industrial population within the designated service area. The waste generation capacity determines the future fleet and quantity, forming a crucial element of the ensuing distribution channel. A monitoring system integrating object detection and the Internet of Things (IoT) has been devised to ensure effective garbage collection. Cameras strategically positioned at temporary disposal sites transmit real-time images. The system evaluates garbage collection capacity through object detection facilitated by neural network training. The research outcomes demonstrate the system's capability to identify waste pile levels and validate the garbage pickup process by the designated fleet. Future research should focus on assignment and scheduling in waste transportation, enabling fleet allocation within specific timeframes. Additionally, an object detection algorithm refinement is necessary for more precise identification of waste pile locations.]]>
<![CDATA[A Mixed Integer Linear Programming for Exam-Invigilator Assignment Problem: A Case Study at Universiti Pertahanan Nasional Malaysia ]]>
<![CDATA[Irfan Hanafi, Muhammad Aiman]]>;
<![CDATA[Syed Ali, Sharifah Aishah]]>;
<![CDATA[Mat Jusoh, Ruzanna]]>;
<![CDATA[Ali, Fazilatulaili]]>;
<![CDATA[Abd Rahman, Norzaura]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.1.2196
<![CDATA[The assignment of invigilators for examinations is a complex and challenging task, particularly when faced with numerous factors that must be carefully considered. Critical elements are essential in this process, including staff availability, room capacity, and time constraints, requiring thorough evaluation and coordination. This paper focuses on improving the allocation of invigilators for examinations at Universiti Pertahanan Nasional Malaysia (UPNM). The issue arises when academic staff members responsible for teaching the subject are also assigned as exam invigilators, which conflicts with their primary role of assisting students in addressing their queries during examinations. It is essential to reconsider the distribution of invigilator roles, ensuring that academic staff members can focus solely on providing educational support. In contrast, qualified non-academic staff handle invigilation duties effectively. A mixed-integer linear programming (MILP) model is formulated using the existing examination timetable to solve this problem. The model is solved using a simple algorithm implemented in the XPress MP programming language, resulting in an improved solution that requires less computational effort than the conventional method. This approach offers an alternative and better solution for scheduling examination invigilators at UPNM, ensuring the efficient and effective management of exam procedures while maximizing the utilization of available resources. It can serve as a starting point for future investigations into UPNM's scheduling procedures.]]>
<![CDATA[Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones ]]>
<![CDATA[Huda, Achmad Torikul]]>;
<![CDATA[Setiawardhana, Setiawardhana]]>;
<![CDATA[Dewantara, Bima Sena Bayu]]>;
<![CDATA[Sigit, Riyanto]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.1961
<![CDATA[Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.]]>
<![CDATA[Detecting Need-Attention Patients using Machine Learning ]]>
<![CDATA[Law, Theng Jia]]>;
<![CDATA[Ting, Choo-Yee]]>;
<![CDATA[Zakariah, Helmi]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2277
<![CDATA[In healthcare, detecting patients who need immediate attention is difficult. Identifying the critical variables is challenging in patient detection because human intervention in variable selection is required. Consequently, patients who need immediate attention often experience prolonged waiting times. Researchers have investigated various approaches to identify those who require attention. One of the techniques is leveraging Artificial Intelligence (AI). However, identifying the optimal feature set and predictive model is complex. Therefore, this study has attempted to (i) identify the critical features and (ii) develop and evaluate predictive models in detecting those who need attention. The dataset is collected from one of the healthcare companies. The dataset collected contains 67 variables and 51102 records. It consists of patient information and questionnaires answered by each participant registered in the Selangor Saring Program. Important features were identified in detecting those who need attention on treated data. Multiple classifiers were developed due to their simplicity. The models were evaluated before and after hyperparameter tuning based on accuracy, precision, recall, F1-score, Geometric Mean, and Area Under the Curve. The findings showed that the Stacking Classifier produced the highest accuracy (69.9%) when using the blood dataset. In contrast, Extreme Gradient Boosting achieved the highest accuracy (81.7%) when the urine dataset was used. This work can be extended to explore the incorporation of Points of Interest and geographical data near patients’ residences and study other ensemble models to enhance the performance of detecting those who need attention.]]>
<![CDATA[Classification of Dermoscopic Images Using CNN-SVM ]]>
<![CDATA[Minarno, Agus Eko]]>;
<![CDATA[Fadhlan, Muhammad]]>;
<![CDATA[Munarko, Yuda]]>;
<![CDATA[Chandranegara, Didih Rizki]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2153
<![CDATA[Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.]]>
<![CDATA[Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures ]]>
<![CDATA[Ahmed, Mirza Jamal]]>;
<![CDATA[Abdullah, Nurul Azma]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.8.2.2755
<![CDATA[The field of Digital Image Forensics (DIF) faces a critical issue in accurately identifying children in digital images, notably in cases involving the proliferation of child sexual abuse content. Existing techniques face hurdles due to model architecture limitations, dataset suitability concerns, and classification imbalance, impeding their ability to recognize children to deter pornographic images. Addressing this challenge, this study introduces Implicit Feature Extraction (IFE), a specialized approach for distinguishing child and adult images in object detection. Leveraging Convolutional Neural Networks (CNNs), the IFE method automates the extraction of discriminative facial features, surpassing the constraints of Explicit Feature Extraction (EFE) methods, which achieve an accuracy of around 70%. The research focuses on three core objectives introducing IFE for detailed face feature detection in DIF's child and adult image identification, implementing IFE with CNNs to enhance image classification, and conducting a thorough evaluation of the proposed technique's performance using key metrics like accuracy and balanced classification results and comparing the result with a basic CNN model’s performance. This research's significance lies in its notable contributions to digital image forensics, particularly in combatting child exploitation. The fusion of IFE with CNNs showcases 92% accuracy in distinguishing child and adult images, promising advancements with practical implications in child protection and forensic investigations. The comprehensive evaluation using the UTKFace dataset underscores the proposed technique's efficacy, marking a substantial improvement in child image identification within digital image forensics.]]>
