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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 51 Documents
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Search results for , issue "Vol 9, No 4 (2025)" : 51 Documents clear
Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm Aziz, Firman; Mustamin, Nurul Fathanah; Rijal, Muhammad; Tanniewa, Adam M
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3079

Abstract

This study aims to enhance the performance of Human Activity Recognition (HAR) systems by implementing the Stacked Sparse Autoencoder (SSAE) algorithm combined with Support Vector Machine (SVM). The objective is to enhance the classification accuracy of human activities using sensor data. The materials for this study include a dataset collected from wearable devices equipped with accelerometers and gyroscopes. These devices generate time-series data representing a range of activities, such as walking, running, sitting, and standing. The raw data were preprocessed through normalization and segmented into fixed time windows to ensure uniformity and reliability for analysis. The methods utilized involve employing SSAE for automated feature extraction. The SSAE algorithm extracts hierarchical and abstract features from sensor data, enabling the model to learn complex patterns that traditional methods might overlook. The extracted features are then input into the SVM classifier to perform activity classification. SSAE was trained using unsupervised learning techniques, followed by supervised fine-tuning with labeled datasets. The results demonstrate that the SSAE-SVM model achieves superior performance compared to traditional SVM. The SSAE-SVM achieved 89% accuracy, 87% precision, 89% sensitivity, and 88% F1 score, significantly outperforming the traditional SVM’s 37% accuracy, 75% precision, 37% sensitivity, and 36% F1 score. These findings underscore the potential of SSAE in enhancing HAR systems by effectively extracting features from sensor data. Future research should focus on the real-time implementation of SSAE, leveraging diverse sensor modalities, and exploring its applicability in broader fields, such as predictive maintenance and personalized health monitoring.
Hybrid-Based Recommender System Based on Electronic Product Reviews Muhammad Syafiq Chelvam, Nor Liyana Natasha; Haw, Su-Cheng; Krisnawati, Lucia D.; Mahastama, Aditya
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3561

Abstract

The era of abundant information and the continuous introduction of new products and services has made it increasingly challenging for users to navigate numerous options. Recommender systems have emerged as essential tools to help users find personalized and relevant information quickly. This paper proposes a hybrid recommender system that effectively processes online customer reviews using word embedding and clustering techniques. The system generates product-feature words, detects sentiment words and their intensity, analyzes word correlations, and extracts variables from the reviews for the product. Word embedding models, such as Word2Vec, are employed to capture the semantic content of product reviews and descriptions. The attributes extracted from the text data and word embeddings are combined to create a hybrid representation of products. Based on this hybrid representation, the system calculates the similarity among products using cosine similarity and other measures. Finally, it returns a ranked list of recommended best products based on how similar they are to either an inputted product or user preferences. We have implemented the system and experimental evaluations have been carried out on the “Datafiniti Electronics Product Data" dataset. We aim to provide personalized recommendations to users based on online reviews, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age. The developed prototype will provide personalized recommendations to users, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age.
AquaFlora Smart Terrarium: A Self-Sustaining Internet of Things (IoT)-based Terrarium for Smart Ecosystem Management Irushan Bandara, A.M.T.; Mohamed Halip, N. H.; Purnshatman, Tatchanaamoorti; Mohd Yusoff, Nurulanis; Mohamed Halip, Mohd Hazali
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3403

Abstract

Using Internet of Things (IoT) technology in smart terrariums is a growing trend that aligns with the move towards more automated and better- well-managed ecosystem gardening. AquaFlora Smart Terrarium is a state-of-the-art system designed to create and maintain the perfect environment for various plants. It features a network of sensors, actuators, and electronic components, all orchestrated by an ESP32 microcontroller. The system leverages four actuators to control light, humidity, irrigation, and cooling, ensuring optimal conditions for plant growth. Three sensors, which are the Capacitive Soil Moisture Sensor, DHT22, and BH1750FVI to monitor soil moisture, temperature, humidity, and light intensity, providing real-time data to the microcontroller. The terrarium can be conveniently controlled via a mobile app and Node-RED, allowing for remote monitoring, control, and automation through Firebase and MQTT. Node-RED visualization of sensor data over a 10-hour period demonstrated the effectiveness of the automatic mode in maintaining stable plant conditions. Soil moisture remained above 60%, temperature ranged between 30.1°C and 33.1°C, humidity between 69.10% and 74.00%, and light intensity between 23 Lux and 175 Lux. The AquaFlora Smart Terrarium represents a significant innovation in plant care, offering a reliable and automated solution to create and sustain the ideal environment for healthy plant growth.
Development of Augmented Reality Media for Local Wisdom Learning Sapta, Andy; Risnawati, Erna; Panjaitan, Dedy Juliandri; Marisa, -; Nisa, Uliya Khoirun
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.4359

Abstract

Indonesia, as an archipelago, has much local wisdom. Teaching local wisdom materials in Indonesia so far tends to use images or videos that are not interactive. This medium has limitations in terms of student involvement. Therefore, it is necessary to develop learning media that can effectively display local wisdom from various cultures. This research uses the Research and Development method to develop Augmented Reality applications. Augmented Reality Local Wisdom: This research developed local wisdom in Indonesia, including the Batak, Javanese, Sundanese, and Betawi tribes. Material culture encompasses traditional clothing, vernacular architecture, indigenous weapons, and regional motifs. The research population comes from the provinces of North Sumatra, Jakarta, West Java, and Yogyakarta. This study's research product involves developing a project-based learning model grounded in local wisdom and supported by Augmented Reality. The development results show that using Augmented Reality in learning, particularly through the project-based learning model, is beneficial because it helps students describe material culture more effectively. This study suggests that students can become more engaged and interactive in their learning. Additionally, using Augmented Reality, students can explore culture more contextually, enhance their problem-solving skills, and reduce the time and costs associated with learning about local culture. The use of AR in local wisdom-based learning could be an innovative solution to enhance the quality of education and cultural preservation in the digital age.
Multi-Document Summarization Using Tuna Swarm Optimization and Markov Clustering Widiartha, I Made; Hartati, Rukmi Sari; Wiharta, Dewa Made; Sastra, Nyoman Putra; Astuti, Luh Gede
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3365

Abstract

The Internet contains a large number of documents from various sources with similar content. The contents of documents that are almost identical will lead to news redundancy, making it difficult for readers to distinguish between factual information and opinions. Multi-document summarization has been designed to enable readers to easily understand the meaning of news documents without needing to read multiple documents. Multi-document summarization aims to extract information from several texts written about the same topic. The resulting summary report enables users to obtain a single piece of information from multiple similar pieces of information sourced from various locations. Various approaches have been used in creating multi-document summaries. Issues regarding accuracy and redundancy are still a significant focus of research. In this paper, a new multi-document summarization model was built using Tuna Swarm Optimization (TSO) and Markov Clustering (MCL) methods. The dataset of this research is Indonesian language news from various online media sources. Based on hyperparameter tuning using training data, the best TSO model performance was obtained at variable values a = 0.7, z = 0.9, and the optimal number of tuna fish > 80. From the research results, it was found that TSO outperformed other swarm intelligence methods. The use of MCL has proven to be effective, as evidenced by the performance results, where TSO achieved an average ROUGE value 7.95% higher when MCL was applied. In this performance test, four standard evaluation metrics of the ROUGE toolkit were used.
Secondary Structure Protein Prediction-based First Level Features Extraction Using U-Net and Sparse Auto-encoder Al-Azzawi, Adil; Alsaedi, Muneera
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3179

Abstract

Protein secondary structure prediction (PSSP) is an important challenge in bioinformatics. Existing methods for PSSP are generally divided into three categories: neighbor-based, model-based, and meta-estimator-based methods, each using supervised or supervised learning methods model-based are often neural networks, hidden Markov models are available; they support vector machines and other machine learning techniques based on multiple sequence alignments and evolutionary data from increasingly large protein databases. This paper presents a powerful machine learning approach for PSSP, which is a new feature extraction method using sparse autoencoders to identify new protein features. The sparse autoencoder efficiently identifies new features in the training data and provides an accurate prediction of occurrences. Two machine learning methods are used: unsupervised learning methods based on sparse auto-encoders and semi-supervised learning methods using deep learning methods. Experimental results show that the deep learning method gets 86.719% accuracy on the test set, while the unsupervised pretraining method gets 85.853% accuracy on the training set after being improved by surface propagation. Fine-tuning and layer-wise pretraining significantly improve the performance of the proposed model. The results show that the deep learning method achieves an accuracy of 86.7% in the training set and 71.4% in the test set. In comparison, Sparse Autoencoders alone achieved an accuracy of 67%, demonstrating the effectiveness of the combination of these methods. This study highlights the role of advanced deep learning techniques in PSSP accuracy. Future research should consider using big data, exploring deep learning algorithms, and refining optimization methods to further encourage predictive performance in bioinformatics.
QSroute: Enhancing Software Defined Networking Routing Scheme through Advanced QoS Metrics Integration Kamarudin, Imran Edzereiq; Ameedeen, Mohamed Ariff
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3025

Abstract

The surge in global internet traffic, primarily fueled by the rise of streaming platforms, cloud computing, and data-intensive applications, has posed significant challenges in effectively managing network resources. Ensuring consistent Quality of Service (QoS) across various types of traffic, such as video streaming, online gaming, and real-time communications, is becoming an increasingly challenging task. Traditional routing techniques, such as best-effort and shortest-path methods, are increasingly falling short in meeting these demands due to their inability to account for different traffic requirements. To address these limitations, this paper introduces QSroute, a novel QoS-based routing (QBR) scheme specifically designed for Software Defined Networking (SDN) environments. QSroute leverages the global view provided by SDN to dynamically optimize routing decisions based on critical QoS metrics, including available bandwidth, packet delay, and packet loss ratio. Our approach computes the optimal path between source and destination nodes by minimizing a composite link cost derived from these metrics. Extensive simulation results show that QSroute significantly outperforms existing QBR schemes, particularly in terms of reducing end-to-end delays, minimizing jitter, and improving overall throughput. These performance gains underscore QSroute’s potential as a highly effective solution for addressing the complex demands of modern network environments, offering enhanced scalability and network efficiency. Future research will investigate the integration of additional QoS metrics, as well as the scheme's scalability in more complex, multi-domain SDN environments, to further enhance its applicability.
Crowdsourcing Innovations in Renewable Energy and Collective Intelligence Labash, Huda; Kareem Abass, Haithem; Abdulkarim Dheeb, Shaimaa; Bodnar, Nataliia; Abdulkhaleq Ali, Ammar
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.4380

Abstract

As the pace of transition to sustainable energy systems is accelerating, there is an urgent need for creative and all-inclusive approaches capable of expediting the development and deployment of technologies. This study presents crowdsourcing and collective intelligence as strategic means to improve efficiency in innovation and stakeholder engagement in the renewable energy context. Using a multi-methods approach in which structured surveys, multi-case analysis, and algorithmic model building are combined, this study assesses the operational and leading indicators of crowdsourcing effectiveness. Data was gathered from 250 respondents and five real-life energy projects and supported by two algorithmic models: the Participation-Weighted Solution Prioritization (PWSP) model and the Dynamic Implementation Success Estimator (DISE). The results show that it is possible to achieve significant reductions in development time and energy consumption while increasing the quality of the solution and the percentage of implementations. Finally, the synthesis of survey and algorithmic results demonstrated a significant alignment between perceived importance and predictive relevance of features, like submission quality and review protocols. While the work identifies clear advantages, it also surfaces ongoing challenges around data availability, algorithm scalability, and inclusivity in terms of who gets to contribute. These findings highlight the role of smart evaluation mechanisms and adaptive platform governance to enhance the contribution of crowdsourcing to energy innovation. The authors argue that, when guided by strong design and ethics, crowdsourcing can be a potent tool for speeding sustainable energy transitions and democratizing innovation.
Classification of Vegetation Land Cover Area Using Convolutional Neural Network Galib, Galan Ramadan Harya; Santoso, Irwan Budi; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3050

Abstract

The decrease and reduction of vegetation land or forest area over time has become a serious and significant problem to be considered. Increasing the Earth’s temperature is a consequence of deforestation, which can contribute to climate change. The other issues that researchers face concern diversity and various objects in satellite imagery that may be difficult for computers to identify using traditional methods. This research aims to develop a model that can classify vegetation land cover areas on high-resolution images. The data used is sourced from the ISPRS (International Society for Photogrammetry and Remote Sensing) Vaihingen. The model used is a Convolutional Neural Network (CNN) with a VGG16-Net Encoder architecture. Tests were conducted on eight scenarios with training and test data ratios of 80:20% and 70:30%. The classifier method that we employed in this research is argmax and threshold. We also compared the performance of Neural Networks with two hidden layers and three hidden layers to investigate the impact of adding another layer on the Neural Network's performance in classifying vegetation land cover areas. The results show that using the threshold classifier method can save training time compared to the argmax method. By increasing the number of hidden layers in the neural network, model performance improves, as shown by increases in recall, accuracy, and F1-score metrics. However, there is a slight decrease in the precision metric. The model achieved its best performance with a precision (Pre) of 99.5%, accuracy (Acc) of 83.3%, and F1-score (Fs) of 70.3%, requiring a training time (T-time) of 16 minutes and 41 seconds and an inference time (I-time) of 0.1535 seconds.
Visualization Tools for Backward Elimination Technique in Multiple Regression Time Series Modelling of CO2 Emissions in Malaysia Mansor, Mahayaudin M.; Ibrahim, Nurain; Zakaria, Roslinazairimah; Suhaila, Jamaludin; Miswan, Nor Hamizah; Shaadan, Norshahida
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3012

Abstract

Understanding multiple regression time series modelling is crucial because the procedures involve intricate statistical methods. This study incorporates a flowchart that clearly illustrates the steps for modelling a response variable affected by several explanatory variables via the backward elimination technique. The first objective of this study is to utilise ten graphical tools, comprising charts and tables, for visual assessment to support formal evaluations in model diagnostics using R programming. The aim is to provide comprehensive insights and improve the overall understanding of the modelling procedures. The visualisation tools include criteria for multicollinearity, goodness-of-fit, and underlying assumptions of normality, homoscedasticity, zero serial correlation, and volatility in the residuals. The second objective involves implementing modelling procedures to obtain a well-specified model in a real-world context, demonstrating its practical value and implications. In this instance, the selected response variable is carbon dioxide (CO2) emissions, significantly contributing to global warming. In Malaysia, CO2 emissions increased continuously from 1990 to 2022, with an alarming average annual growth rate of 4.9%. The visual diagnostics have helped guide the elimination of some explanatory variables in the initial model and refined the models, resulting in a well-specified final model that is parsimonious and explains 98.6% of the variability in CO2 emissions. The final model suggests that high fossil fuel use and GDP per capita are contributing factors to increased CO2 emissions in Malaysia. The study recommends government action and investment in renewable energy to reduce CO2 emissions by 45% by 2030 and achieve net-zero emissions by 2050.

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