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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.
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Articles 33 Documents
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Search results for , issue "Vol 9, No 6 (2025)" : 33 Documents clear
Video Compression Using Quadtree Decomposition and Bitplane Coding Mahdi, Sura Hameed; Aziz Sahy, Seba; Hasoon, Jamal N.
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3172

Abstract

Due to the exponential rise in multimedia content, video files now make up a large amount of internet traffic and data storage requirements. Video compression is essential for reducing video data storage and transmission bandwidth, as it involves processing frames before they are transmitted or stored. High-resolution formats offer enhanced viewing experiences but result in enormous file sizes, posing challenges related to storage, bandwidth consumption, and transmission efficiency. This paper aims to address these challenges by developing a novel video compression algorithm that optimizes the balance between file size reduction, processing speed, and visual quality preservation. This method requires intensive computation, especially for video frames, and the image compression equipment is highly complex and expensive in terms of hardware. In this paper, an efficient method for video compression is proposed by combining several techniques, including quadtree, bitmap coding, and DCT. The video files are divided into scenes, which are further categorized into two types of frames: keyframes and related frames. The keyframes are segmented by the quadtree decomposition method into three region types (small block, medium block, and large block). The small block is compressed using bitmap coding (lossless compression), the medium block is compressed using DCT techniques, and the large block (that has fewer details) is compressed by replacing the mean of the color RGB values. All blocks are merged into a compressed file with the location of each block for decompression. The proposed method achieves an accurate compression result of approximately 17% from the input video size and can be extended to be combined with other methods.
Optimizing Machine Learning Models for Anomaly-based IDS using Intercorrelation Threshold Wahyu Adi, Prajanto; Sugiharto, Aris; Malik Hakim, Muhammad; Rizki Saputra, Naufal; Hanif Setiawan, Syariful
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3355

Abstract

This study aims to improve the performance of attack detection on the Bot-IoT dataset that faces class imbalance. The method used involves developing a feature selection model based on the Pearson correlation coefficient between features, with an adaptive threshold applied. The datasets used consist of two types: D1, with the 10 best features, and D2, with all features. The oversampling technique is applied to the minority class, followed by calculating feature correlations to determine the best feature using a threshold based on the average of the highest and lowest correlations. The feature selection process is carried out iteratively, with performance testing across several machine learning algorithms, including KNN, Random Forest, Logistic Regression, and SVM. The results show that the proposed feature selection method can improve the performance of the minority class without sacrificing the majority class's performance. On the D1 dataset, the Random Forest algorithm achieved 96% accuracy, while KNN achieved 93%. On the D2 dataset, KNN achieved balanced performance, with average precision, recall, and F1-score of 0.99 for both classes, while Random Forest achieved lower results on the minority class. The implications of this study indicate that correlation-based feature selection can improve attack detection performance on datasets with high class imbalance, and it can be implemented in future studies to address similar problems in IoT-based intrusion detection systems.
E-commerce Product Review Classification using Neural Network-Based Approach Ihtada, Fahrendra Khoirul; Abidin, Zainal; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2845

Abstract

E-commerce has become an integral part of how people shop, with the rise of customer reviews on various platforms. These reviews provide important insights into product, customer service, and delivery. The growing volume of e-commerce reviews makes manual sorting time-consuming and error-prone for business owners. This study aims to classify e-commerce reviews into three categories: product, customer service, and delivery. The data was collected from e-commerce customer reviews on Tokopedia and labeled using crowdsourcing for ground truth. To classify the reviews, a Neural Network is performed with various numbers of node and learning rate. TF-IDF is also used for feature extraction to capture important features from the review data. From nine test scenarios, model B3 with 50 nodes in the first hidden layer and a learning rate of 0.1 provided the best performance with an accuracy of 65.85%, precision of 62.27%, recall of 58.61%, and f1-score of 59.71%. Validation using K-Fold Cross Validation shows an average accuracy of 64.17% at k=10. Word analysis with TF-IDF identified dominant words in each category. The B3 model is not yet able to classify reviews perfectly, due to the large and unbalanced dataset, less complex model architecture, and less effective TF-IDF preprocessing. However, this study shows potential for better classification in the future. With optimization, this model can be very useful for e-commerce business owners to gain insight from customer reviews and can help them to identify aspects that will lead to customer satisfaction and trust.
Strategic Recommendations in Increasing Gen Z User Engagement towards Gamification Elements with Fuzzy AHP and Octalysis Approaches Marisa, Fitri; Istiadi, -; Ahmad, Sharifah Sakinah Syed; Handajani, Endah Tri Esti; NoerTjahyana, Agustinus; Maukar, Anastasia L
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3324

Abstract

Generation Z (Gen Z), often referred to as the "digital native" generation, interacts extensively with digital technology and social media. E-commerce companies need to adopt the right strategies, such as gamification, to increase user engagement among Gen Z. However, there is limited research evaluating which gamification elements are most effective in engaging Gen Z users. This study addresses this gap by identifying the most impactful gamification elements that enhance Gen Z user engagement and providing strategic recommendations for e-commerce designers and developers. Using the Fuzzy AHP method and Octalysis approach, this study evaluates five gamification elements: Point, Reward, Referral, Leaderboard, and Level across four key parameters: Motivation, Engagement, User Experience, and Retention. The Fuzzy AHP results indicate that the "Reward" element ranks highest with a score of 1.0, followed by "Level" with a score of 0.829. "Leaderboard" comes in third with a score of 0.669, while "Point" and "Referral" score 0.606 and 0.220, respectively. The low score of "Referral" suggests its limited effectiveness in fostering social connectedness among Gen Z users. The Octalysis analysis reveals that "Reward" has the most significant influence on core drives such as "Development and Accomplishment" and "Scarcity and Impatience," with an average score of 7.25, followed by "Level" with a score of 7.125. These findings underscore the importance of prioritizing "Reward" and "Level" to optimize user engagement for Gen Z. The practical implications of this study suggest that e-commerce platforms should integrate these gamification elements to create more engaging and interactive shopping experiences for Gen Z users, aligning with their preferences and motivations.
Performance Improvement for Hotspot Prediction Model Using SBi-LSTM-XGBoost and SBi-GRU-XGBoost Sukmana, Husni Teja; Aripiyanto, Saepul; Alamsyah, Aryajaya; Henry, Amir Acalapati; Nandaputra, Riandi
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3047

Abstract

Forest fires damage ecosystems and harm all living beings, often triggered by low rainfall that worsens fire spread. Climatic factors such as the El Nino–Southern Oscillation (ENSO) also contribute to reduced rainfall and prolonged dry seasons. This study aims to enhance the performance of fire prediction models to support forest fire mitigation. Modified artificial neural network algorithms—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with bidirectional stacked layers—are employed as baseline models. An experimental approach was used to compare the performance of LSTM and GRU models with their ensemble versions, where XGBoost was added to improve prediction accuracy. The results show that the proposed ensemble algorithms significantly outperform the baseline models in multivariate fire prediction. The SBi-LSTM-XGBoost and SBi-GRU-XGBoost models demonstrated more than a 40% performance improvement compared to the original SBi-LSTM and SBi-GRU models. In multivariate modelling, the ensemble models achieved an R-value of 1.0000, with an average MAE of 0.0007, RMSE of 0.0009, and MAPE of 0.0008. This study also identified limitations of the LSTM and GRU models in processing ENSO data due to their non-linearity and weak correlation with hotspot data. As a contribution, our experiments show that integrating XGBoost into LSTM and GRU models effectively overcomes these limitations, significantly improving hotspot prediction accuracy and supporting better forest fire mitigation strategies.
Developing an AR Navigation System: Bridging Indoor and Outdoor Environments Achmad, Zacky Maulana; Sukaridhoto, Sritrusta; Zainuddin, Muhammad Agus
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3420

Abstract

Navigation systems are designed to assist people or objects in moving efficiently and accurately from one place to another by providing directions to reach a destination. There are two main types of navigation systems: Indoor Navigation, which involves navigation within indoor environments, and Outdoor Navigation, which is used in outdoor or open environments. Both have drawbacks, such as signal limitations in multi-floor buildings and hardware requirements. This study focuses on developing a Seamless Indoor-Outdoor Navigation System that integrates indoor and outdoor navigation within a single application, allowing users to seamlessly transition between environments without switching applications or requiring additional hardware. The development uses AR technology and Immersal, overlaying digital content such as 3D pinpoints and 3D paths onto the real world through smartphones to show destinations to users. Immersal SDK adds real-world location mapping, application development, and localization for indoor and outdoor environments. The system was implemented at the PENS Campus, and testing was conducted, including: 1) Navigation Testing, which demonstrated efficient route visualization in the D3, D4, and S2 buildings and PENS Road. 2) User testing with the PIECES Framework, involving 35 respondents, showed high satisfaction with a top score of 4.49 for Information. 3) Indoor-outdoor integration Testing confirmed the system’s success in navigating between environments. 4) Multilevel Floor Navigation Testing demonstrated its ability to navigate multi-floor buildings, 5) Software Testing showed the system's performance met targeted frame rates of 30 FPS for Android and 60 FPS for iOS devices.
Understanding Performance Efficiency in ISO/IEC 25000: A Systematic Literature Review Rojas, Hesmeralda; Renteria, Ronald; Martinez, Virgilio
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3074

Abstract

Performance efficiency is a critical aspect of software quality, particularly in modern applications handling large data volumes, simultaneous users, and complex operations. This study aimed to provide a comprehensive overview of current research on performance efficiency under ISO/IEC 25010. This involved examining the topics driving this research, alongside their contexts, objectives, applications, tools, and metrics, enabling the visualization of emerging trends in the quantification and evaluation of performance efficiency. To this end, a systematic literature review was conducted from 2014 to 2024, following a protocol that combined automated and manual searches. This process yielded 38 primary studies. The results revealed five central research topics, with time behavior identified as the most studied sub-characteristic (48%), followed by resource utilization (36%) and capacity (16%). The study also analyzed the reasons for this distribution of research interest. A total of 68 metrics were identified: 41 related to time behavior, 16 to resource utilization, and 11 to capacity. Additionally, 46 tools were identified for evaluating these three sub-characteristics. This analysis provides a solid foundation for objectively measuring and comparing software performance. The findings of this study offer a holistic view of performance efficiency. From an academic perspective, it supports the development and validation of research in software engineering. It provides a comprehensive understanding of ISO/IEC 25010, facilitating systematic improvements and tracking its evolution. From an industry perspective, it serves as a practical resource for enhancing competitiveness by promoting compliance with the standard and improving knowledge of performance efficiency. 
Enhancing Security in Cross-Border Payments: A Cyber Threat Modeling Approach Amiruddin, Amiruddin; Briliyant, Obrina Candra; Windarta, Susila; Setiadji, Muhammad Yusuf Bambang; Priambodo, Dimas Febriyan
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3205

Abstract

Cross-border payment (CBP) systems are critical to the global economy but are increasingly susceptible to cyber threats due to their complex structures and diverse transaction models. This paper analyzes cyber vulnerabilities across four CBP models: correspondent banking (SWIFT), infrastructure (ApplePay), closed-loop (PayPal), and peer-to-peer (Ripple). It employs the STRIDE methodology and adapts the cyber threat modeling framework proposed by Khalil et al. Key objectives include identifying vulnerabilities, assessing the impact of threats, and proposing mitigation strategies. The corresponding banking model shows the highest threat impact due to extensive transaction elements crossing trust boundaries. In contrast, the closed-loop model demonstrates lower vulnerability because of fewer components outside its trust boundary. Peer-to-peer and infrastructure models present moderate risk levels influenced by blockchain transparency and infrastructure dependencies. Critical threats identified include abuse of authority, malware, and script injection, which can result in significant losses, such as financial theft, service outages, and data breaches. Results indicate that interactions between processes across trust boundaries exacerbate cyber risks. Strategic recommendations include reducing system complexity, reinforcing security protocols at trust boundaries, and integrating advanced threat detection mechanisms. The study highlights these vulnerabilities and risks and underscores the need for robust cybersecurity measures to protect CBP systems. This research contributes to the existing knowledge by providing a detailed threat assessment and practical insights for improving CBP security. Future studies should explore alternative modeling methods, update security contexts to reflect real-world scenarios, and analyze the impact of open banking technologies.
Sample An Improved Lite-Yolov4 Object Detection Model for Mobile Augmented Reality Mansoor Nafea, Mohammed; Siok Yeeb, Tan; Tareq, Mustafa; Ahmed Jubair, Mohammed; Fatikhan Ataalla, Abdalrahman
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3341

Abstract

Augmented reality (AR) enhances user experiences by overlaying digital information on real-world objects or places. Augmented reality makes unprecedentedly immersive experiences possible in marketing, industry, education, entertainment, fashion, and healthcare. While current augmented reality methods can identify 3D items in their environment, the recognition of tiny, complex objects remains a problem for most object detection methods. In addition, object detection is a key in computer vision and AR systems. The Object detection process aims to classify and localize objects in applications like face detection, text detection, and people counting. Many natural features detection models were proposed, like YOLO, YOLO-LITE, and YOLOv4-tiny. However, the detection of objects from natural images remains a challenging task, often compromising accuracy or requiring longer processing times. To overcome these challenges, this article suggests a novel method that combines the strengths of YOLO-LITE and YOLOv4-tiny into a hybrid model. The suggested model name is LITE-YOLOv4, which stands for “LITE-You Only Look Once Version 4. The model design depends on YOLO-LITE as a backbone. LITE-YOLOv4 uses a feature pyramid network to extract feature maps of various sizes. It also utilizes a "shallow and narrow" convolution layer to optimize its object detection capability. The proposed model aims to achieve a speed and accuracy balance, making it suitable for use in AR apps on portable devices and PCs without GPUs. LITE-YOLOv4 achieved a mean average precision (mAP) of 52.6% on the PASCAL VOC dataset and 33.3% on the COCO dataset. The suggested model achieved a respectable speed, which is 20 frames per second (FPS). LITE-YOLOv4 provides better accuracy and reasonable computational time than state-of-the-art non-GPU models.
A Cascading of YOLOv8 and Random Forest Regression in Oil Palm Fresh Fruit Bunch Mass Estimation System using Unmanned Aerial Vehicle Imagery Indrabayu, -; Nurhadi, Muhammad Ijlal; Tandungan, Sofyan; Rahmat, Muhammad Abdillah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3137

Abstract

Efficient management of oil palm farms requires accurate pre-harvest planning to maximize productivity. Traditional methods for estimating the mass of Fresh Fruit Bunches (FFBs) typically involve manual sampling and weighing, which are time-consuming and prone to errors. This study presents a novel system combining unmanned aerial vehicle (UAV) photography with geometric feature extraction using YOLOv8-Segmentation and machine learning models—Random Forest Regression (RFR)—to estimate FFB mass. The system addresses challenges posed by dynamic drone imagery, including environmental variations and frond occlusions. Instead of directly integrating YOLOv8 with the regression models, geometric features such as the minor axis, perimeter, and eccentricity are extracted from the segmented images and used to train the RFR for mass estimation. The top-performing model, using features extracted from YOLOv8-Small-Segmentation with the minor axis and eccentricity, achieved a Root Mean Square Error (RMSE) of 3.95 and a Mean Absolute Error (MAE) of 2.87 for frond-covered FFBs. For frond-uncovered FFBs, the best-performing features were the minor axis, perimeter, and area extracted using YOLOv8-Large-Segmentation, resulting in an RMSE of 3.91 and MAE of 2.91. These results demonstrate the system's capability to accurately estimate FFB mass based on UAV-captured imagery and feature extraction. This approach offers a scalable and efficient solution for pre-harvest planning in oil palm plantations, addressing the limitations of traditional methods while improving operational efficiency and accuracy in yield estimation.

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