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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 55 Documents
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Search results for , issue "Vol 8, No 2 (2024)" : 55 Documents clear
Understanding User Engagement Strategies for Podcasts Videos on Youtube in Indonesia: A Study on Content Creation Kurniawan, Yohannes; Halim, Enrico; Jennifer, Elisa; Pribadi, Fazha Aqsa; Bhutkar, Ganesh; Anwar, Norizan
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2123

Abstract

COVID-19 has transformed human life by utilizing technology to obtain information. Based on Katadata.com, Indonesia ranks second in the world's highest number of podcast listeners in the third quarter of 2021, accounting for 35.6% of the total internet users. Based on YouTube user statistics from Global Media Insight, Indonesia also ranks fourth globally for the highest number of YouTube users in 2023, totaling 139 million. Thus, this study aims to examine the factors that can influence the strategy to attract the right audience in building podcast content and provide recommendations for appropriate user engagement by comparing the genres of current issues and business & finance podcasts on YouTube Indonesia. The research method used is descriptive analytics, using the open-source Netlytics tool to analyze text and automatically summarize and visualize public online conversations on YouTube. The results of this study indicate that current issue genres are more prevalent in Indonesian society, with one of the most influential factors being the topic and guests to currently viral podcasts. This study also analyzes other factors that influence user engagement. Therefore, the findings of this research can be utilized as an opportunity for companies/institutions to enhance their branding/promotion through YouTube video podcasts. This research can also serve as a reference for other podcast content creators in building and improving user engagement on their YouTube channels to attract more interest from Indonesian society.
Utilization of WebGIS for Visualization of the Distribution of Tourist Destination Religious Objects in Nagari Batuhampar of Lima Puluh Kota Regency, West Sumatera Province Susetyo, Bigharta Bekti; Purwaningsih, Endah; Sutriani, Widia; Purnamasari, Eva; Bagus, Muhamad Ikhwan
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2281

Abstract

Nagari Batuhampar has several tourist attractions planned as object tourist destinations in the strategic plan. However, publication and presentation on social media are less effective in promoting the unique characteristics of tourist attractions. This research aims to identify the distribution of tourist destination objects in   Nagari Batuhampar, followed by comprehensive information. The type of research used is descriptive survey research with the waterfall method, which consists of requirement analysis, system analysis, system implementation, system testing, system evaluation, operation, and maintenance. Data collecting techniques include observation using GPS and documentation, interviews to obtain information for web development, and questionnaires. Furthermore, the built-in data application QGIS 3.32.3” Lima” is open source. WebGIS, built using the Database Management System (DBMS) approach, is designed as software to manage big data. Big data is meant to be a collection of lots of data tailored to the project being carried out, such as mapping the distribution of public facilities and village potential. In this research, DBMS focuses on spatial data and religious and supporting tourism attributes. This is focused on data on religious and supporting tourism attributes. The result found that historical religious tourist attractions dominated the distribution of attractions in Nagari Batuhampar. The WebGIS of Tourist Destination Object was constructed using a waterfall method that was effectively created. This development was conducted through system evaluation tests, resulting in most respondents being satisfied with the process's performance. 
A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction Santoso, Hadi; Hanif, Ilham; Magdalena, Hilyah; Afiyati, Afiyati
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1943

Abstract

The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.
Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques Tong-Ern, Tong; Su-Cheng, Haw; Kok-Why, Ng; Al-Tarawneh, Mutaz; Gee-Kok, Tong
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2305

Abstract

The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF). 
Cluster Mapping of Waste Exposure Using DBSCAN Approach: Study of Spatial Patterns and Potential Distribution in Bantul Regency Fauzan, Achmad; Fadillah, Ganjar; Fitria, Annisa; Adriana, Hannura; Bariklana, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2475

Abstract

High level of plastic waste production is a common issue in various places, including the Special Region of Yogyakarta province. It is proven that the Piyungan Integrated Waste Disposal Site (TPST) or Final Disposal Site (TPA) in Bantul Regency has been closed several times due to capacity exceeding the quota and some blockages from residents around the TPA. Issues related to microplastic contamination resulting from discarded plastic waste are fascinating to study, considering the long-term impact of microplastic contamination on human health. This research aims to map the distribution of locations with the potential for waste accumulation to reduce the negative consequences of microplastic contamination. The population used included TPS and markets in Bantul District, with the sample being the distribution of TPS and market points in Bantul District in 2023. The results of checking the point distribution pattern using the quadrant and nearest neighbor method showed that the distribution of waste accumulation points had a clustered design; thus, it could be continued with cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Meanwhile, the number of clusters was determined iteratively from a combination of DBSCAN parameters (MinPts and Epsilon). The best method was evaluated based on the Silhouette Coefficient (SC) value, which in this case was 0.78 (MinPts 7 and Eps 1500) and included in the strong category. Subsequently, exploration was carried out by reducing the MinPts value and the lowest limit value of strong SC.
High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor Abdul Rashid, Raghdah Rasyidah; Milleana Shaharudin, Shazlyn; Filza Sulaiman, Nurul Ainina; Zainuddin, Nurul Hila; Mahdin, Hairulnizam; Mohd Najib, Summayah Aimi; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2700

Abstract

Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors.
Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models Mohd Yazed, Muhammad Syukri; Mohd Yunus, Mohd Amin; Ahmad Shaubari, Ezak Fadzrin; Abdul Hamid, Nor Aziati; Amzah, Azmale; Md Ali, Zulhelmi
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2756

Abstract

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.
CNN-LSTM for Heartbeat Sound Classification Aji, Nurseno Bayu; Kurnianingsih, Kurnianingsih; Masuyama, Naoki; Nojima, Yusuke
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2115

Abstract

Cardiovascular disorders are among the primary causes of death. Regularly monitoring the heart is of paramount importance in preventing fatalities arising from heart diseases. Heart disease monitoring encompasses various approaches, including the analysis of heartbeat sounds. The auditory patterns of a heartbeat can serve as indicators of heart health. This study aims to build a new model for categorizing heartbeat sounds based on associated ailments. The Phonocardiogram (PCG) method digitizes and records heartbeat sounds. By converting heartbeat sounds into digital data, researchers are empowered to develop a deep learning model capable of discerning heart defects based on distinct cardiac rhythms. This study proposes the utilization of Mel-frequency cepstral coefficients for feature extraction, leveraging their application in voice data analysis. These extracted features are subsequently employed in a multi-step classification process. The classification process merges a convolutional neural network (CNN) with a long short-term memory network (LSTM), forming a comprehensive deep learning architecture. This architecture is further enhanced through optimization utilizing the Adagrad optimizer. To examine the effectiveness of the proposed method, its classification performance is evaluated using the "Heartbeat Sounds" dataset sourced from Kaggle. Experimental results underscore the effectiveness of the proposed method by comparing it with simple CNN, CNN with vanilla LSTM, and traditional machine learning methods (MLP, SVM, Random Forest, and k-NN).
Expert Analysis on the User Interface of an Academic E-Supervision Application Based on Vocational Education Character Syukhri, -; Ganefri, -; Tasrif, Elfi; Hidayat, Hendra
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2109

Abstract

Designing information systems to accommodate the unique needs of various users receives a lot of attention in contemporary software engineering. One such distinctive requirement in the educational context is academic supervision. Academic supervision encompasses activities to assist educators in enhancing their skills for managing the learning process and achieving educational objectives. In vocational high schools, the primary educational goals are to prepare students for the job market and empower them to initiate their businesses. These goals can only be realized if teachers incorporate technical and entrepreneurial skills into learning. The main goal of this study is to assist school supervisors and principals in assessing and directing teachers as they incorporate ideas of vocational education into the teaching and learning process. The research methodology used in this study is research and development, which includes several stages: initial needs analysis and assessment of the current state of academic supervision; development, which involves the creation of a conceptual system model; system interface design; model validation and revision; and evaluation, which requires system testing, implementation, and deployment. Based on the initial investigation and analysis of academic supervision, particularly in the context of vocational education, this research presents a conceptual model and system interface design. The outcomes of this research encompass the interface, system architecture, and user guide for the academic e-supervision system. An expert analysis of the user interface design indicates that the interface received positive evaluations from experts, with an overall average rating of 88%.
Artificial intelligence applications in solar energy Le, Thanh Tuan; Le, Thi Thai; Le, Huu Cuong; Dong, Van Huong; Paramasivam, Prabhu; Chung, Nghia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2686

Abstract

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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