cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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.
Arjuna Subject : -
Articles 62 Documents
 3   4   5   6   7 
Search results for , issue "Vol 8, No 1 (2024)" : 62 Documents clear
A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament Ruslan, Nur Amira Sariaty; Zainol, Zuraini; Abdul Rauf, Ummul Fahri
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2198

Abstract

Tennis is one of the world's most played sports, attracting many spectators to participate in the game. One of the most essential strokes in a tennis match is serve performance. This research is intended to determine the most critical strokes in tennis serve performance in predicting the tennis match outcome. This research focuses on the Grand Slam Tournaments of the Australian Open, French Open, Wimbledon, and US Open. The data are collected on the tennis serve performances such as Percentage First Serve In (PFSI), Percentage First Serve Won (PFSW), Percentage First Serve Return Won (PFSRW), Aces, and many more. For one tournament, it consists of 254 observations. This study applied feature selection methods available in R programming, such as Correlation Matrix, Relative Importance Metrics, Boruta, MARS, and cForest. Selecting the most essential and correlated variables with the match status can improve the model and help produce better results. This might help the practitioners to apply this method to obtain the closest result to the actual outcome when we include the most correlated variables in the model. From the result obtained, variables of first and second serve, either win on serve or return serve, are identified as the most critical attributes in the tennis match. As a future implication, we suggest that these are all the factors the players need to pay extra attention to in winning the tennis match. 
Coordination of The Apprenticeship Industrial Program with The Siakama Application Yustisia, Henny; Andreas, Laras Oktavia; Apdeni, Risma; Heriyadi, Bambang; Weriza, Jusmita
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2245

Abstract

This research aims to examine the implementation of the SIAKAMA application in the Apprenticeship Industrial Program. This program was created as a SIAKAMA application to overcome hurdles during the monitoring and evaluation stages. At the monitoring stage, supervising lecturers and field supervisors can use the SIAKAMA application to monitor all Apprenticeship Industrial program student activities in the field, resulting in a good and smooth communication and coordination system. At the evaluation stage, the supervising lecturer and field supervisors in the SIAKAMA application can conduct assessments based on student activities in the field, including daily evaluations and final assessments after the Apprenticeship Industrial Program has been finished. This study employs a quantitative descriptive technique, the Research & Development method, and the 4D development model. A sample of Apprenticeship Industrial Program students from five departments of the Faculty of Engineering, Padang State University, was used in this study. The SIAKAMA application was found to be valid with a value of 0.876, practical with a value of 78.67, and effective with a value of 81.22% after data analysis using SPSS 25. This suggests that implementing the SIAKAMA application to enhance the work competency of Apprenticeship Industrial Program students is viable. The Apprenticeship Industrial Program model represents a modification of the Three Set of Actor development model, yet it hasn't been incorporated with the Industrial Revolution 4.0. Engaging in this Program enables students to acquire 4C skills, including Creativity and Innovation, Critical Thinking and Problem Solving, Communication, and Collaboration.
Continuous Training of Recommendation System for Airbnb Listings Using Graph Learning Chan, Yun Hong; Ng, Kok Why; Haw, Su Cheng; Palanichamy, Naveen
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2315

Abstract

Recommender systems are getting increasingly important nowadays as they can boost user engagement and benefit businesses. However, there remain some unsolved problems. This paper will address two key performance issues. First, the limited ability to identify and leverage intrinsic relationships between data points. Second, the inability to adapt to new data. The first issue is proposed to be addressed through a Graph Neural Network (GNN) to curate better recommendations. GNN will be trained with Airbnb’s review data to utilize its outstanding expressive power to represent complex user-listing interactions at scale, followed by generating embeddings to compute the relevant recommendations to the users. With the generated embeddings, the recommender system will compute a recommendation list to every user based on the embedding similarity between the user and listings or the user’s first-ever reviewed listing and listings. The second issue is proposed to be resolved by incorporating Continuous Training. The proposed recommender system employs GraphSAGE with a customized Rating-Weighted Triplet Ranking Loss function, which outperformed unsupervised GraphSAGE. Offline simulation validated the recommender system's ability to learn from the latest data and improve over time. Overall, the proposed user-to-item (U2I) recommendation rating-weighted GraphSAGE substantially increased by 99.88% in hit-rate@5 and 98.15% in coverage. This offers an effective solution for enhancing the recommender system for Airbnb listings. This research validates the efficacy of GNN-based recommendations in capturing user-item relationships to aid in predicting relevant recommendations, thus significantly driving up the adoption of GNN-based recommender systems.
Personalized Learning Models Using Decision Tree and Random Forest Algorithms in Telecommunication Company Wiratman, Alexander Bryan; Wella, Wella
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1905

Abstract

In response to the rising popularity of online training, this study addresses the crucial need for effective assessment methods at PT XYZ. The research focuses on developing a comprehensive solution through a data visualization dashboard and a machine learning model. The data visualization dashboard, created using Tableau, provides an interactive platform for exploring training data. It offers valuable insights into employees learning progress and needs, empowering them to monitor their advancement and identify areas for improvement effectively. Simultaneously, a machine learning model was developed using Python and Google Collab, employing decision trees and random forest algorithms. The model exhibited promising results with an accuracy rate of 69% for decision trees and 70% for random forests, indicating its proficiency in predicting skill groups. Furthermore, the study rigorously evaluated the dashboard and machine learning model using a 20% holdout dataset, affirming their effectiveness. The dashboard, deployed on a web server, ensures accessibility to all PT XYZ employees, enhancing user experience and engagement. Notably, the dashboard's user-friendly interface allows employees to actively participate in their learning journey, while the machine learning model generates personalized training recommendations based on their progress and needs. In summary, this research provides a practical and innovative solution to the challenge of online training assessment at PT XYZ. By combining data visualization techniques and machine learning algorithms, the developed tools significantly enhance the efficiency and effectiveness of training programs. These findings contribute valuable insights into online training assessment methodologies and pave the way for improved learning experiences in the digital age.
Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data Badriyah, Tessy; Syarif, Iwan; Hardiyanti, Fitriani Rohmah
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2149

Abstract

High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).  Comparison of algorithms conducted using the same datasets.  The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds.
Modeling and Application of Credit Scoring Based on A Multi-Objective Approach to Debtor Data in PT. Bank Riau Kepri Sugianto, -; Widyasari, Yohana Dewi Lulu; Wardhani, Kartina Diah Kusuma
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1493

Abstract

The development of information technology in Indonesia, marked by the start of Industry 4.0, is very rapid. With the development of technology, many companies use technology to develop their business, one of which is banking, which analyses the process of prospective customers. New employees find it challenging to interpret and tend to agree more easily with prospective customers because they only see the fulfillment of general requirements. This research aims to find an overview of the primary and additional factors to analyze prospective credit customers using The Cross-Industry Standard Process for Data Mining (CRISP-DM). Develop a model in this study using data variables of prospective customers in health insurance as a moderating variable. This model tested the Decision Tree algorithm with an accuracy value of 92.49%, the Random Forest with an accuracy value of 81.72%, the Support Vector Machine (SVM) with an accuracy value of 91.25%, and K-Nearest Neighbor (K-NN) with an accuracy value. 90.58%, Gradient Boosting with an accuracy value of 90.69%, and XGBoost with an accuracy value of 93.27%. The algorithm uses a cross-validation technique at the validation stage by changing the K value to 2, 4, 6, 8, and 10. The results show that the XGBoost Algorithm accuracy is 93.27% with a K value of 8. As the highest model accuracy, this model was implemented using the XGBoost Algorithm.
Development of an IoT-Based Egg Incubator with PID Control System and Web Application Prabowo, Muhamad Cahyo Ardi; Sayekti, Ilham; Astuti, Sri; Nursaputro, Septiantar Tebe; Supriyati, Supriyati
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2044

Abstract

The rapid development of technology significantly impacts various aspects of life, including the field of livestock farming. The advancement of technology is expected to enhance the rate and effectiveness of production, particularly in the hatching of chicken eggs or chick breeding. The existing technology relies on manual on/off systems and manual monitoring, hindering successful egg-hatching rates and percentages. Therefore, this research aims to explain the development of an automated egg incubator using a Proportional Integral Derivative (PID) control system with hypertuning parameters, as well as temperature and humidity monitoring, along with a protection system based on voltage sensors, all integrated with the Internet of Things (IoT). The PID control is employed to regulate the temperature of the egg incubator, ensuring stability according to the predetermined set point temperature. The IoT system in this study comprises an ESP32 node as a microcontroller connected to a sensor, using Firebase and User app for monitoring the egg incubator. The study employed PID control with parameter values Kp=10, Ki=3, and Kd=8. The research yielded time-efficient egg incubation and prevention of turning delays. The DHT21 sensor achieved a 90% success rate in detecting room temperature (38°C) and humidity (77%-84%) within the incubator, while PID control effectively maintained the target temperature. The ACS712 sensor accurately detected current in the heater, power supply, and motor. The Kodular application can display sensor readings. The future implication is developing a more adaptive PID method toward changes and nonlinear dynamics. 
Elevated Novice Developer Productivity and Self-efficacy by Promoting UX Journey in Software Requirement Elicitation Kusuma, Wahyu Andhyka; Jantan, Azrul Hazri; Admodisastro, Novia Indriaty; Norowi, Noris
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2171

Abstract

This study explores the effectiveness of the UX Journey methodology in increasing developer productivity and self-efficacy. Materials: The UX journey, consisting of around 30 activities, offers a user-centric approach to developing solutions, with 86 volunteer respondents from 505 populations. Method: Through a comparative analysis of developer productivity metrics and the General Self-Efficacy Scale questionnaire, this study investigates the impact of UX Journey on self-efficacy before and after implementation. Results: The study's findings reveal a significant positive correlation between UX Journey and increased productivity and an association between self-efficacy variables. By incorporating a comprehensive set of activities and a user-centric approach, the UX Journey enables developers to navigate the design process efficiently while gaining a deeper understanding of user needs. The positive correlation between the UX Journey and increased productivity, as well as the relationships between self-efficacy variables, emphasize the value of this methodology in fostering practical design thinking. Implication for Further Research: While this study has limitations regarding sample size and contextual specificity, it provides valuable insight into the benefits of UX Journey and paves the way for further research. In addition, the study focused on specific design projects within a particular context, which might restrict the broader applicability of the results. Significant results indicate that the proposed method is as effective as the elicitation method in general, with the advantage that the developer can understand the needs and empathy of the users. UX journeys can enhance the design process and foster a deeper understanding of users' needs across multiple domains.
Distributed Aerial Image Stitching on Multiple Processors using Message Passing Interface Ramadhan, Alif Wicaksana; Aulia, Fira; Dewi, Ni Made Lintang Asvini; Winarno, Idris; Sukaridhoto, Sritrusta
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1890

Abstract

This study investigates the potential of using Message Passing Interface (MPI) parallelization to enhance the speed of the image stitching process. The image stitching process involves combining multiple images to create a seamless panoramic view. This research explores the potential benefits of segmenting photos into distributed tasks among several identical processor nodes to expedite the stitching process. However, it is crucial to consider that increasing the number of nodes may introduce a trade-off between the speed and quality of the stitching process. The initial experiments were conducted without MPI, resulting in a stitching time of 1506.63 seconds. Subsequently, the researchers employed MPI parallelization on two computer nodes, which reduced the stitching time to 624 seconds. Further improvement was observed when four computer nodes were used, resulting in a stitching time of 346.8 seconds. These findings highlight the potential benefits of MPI parallelization for image stitching tasks. The reduced stitching time achieved through parallelization demonstrates the ability to accelerate the overall stitching process. However, it is essential to carefully consider the trade-off between speed and quality when determining the optimal number of nodes to employ. By effectively distributing the workload across multiple nodes, researchers and practitioners can take advantage of the parallel processing capabilities offered by MPI to expedite image stitching tasks. Future studies could explore additional optimization techniques and evaluate the impact on speed and quality to achieve an optimal balance in real-world applications.
Modeling, Implementing and Evaluation a Decision Support System Used for Choosing the Best HVAC System in The Buildings, Case Study in Iraq Ahmed, Ahmed Shihab; Ali Salah, Hussein
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1947

Abstract

The life cycle cost of a building is affected by the heating, ventilation, and air conditioning (HVAC) system chosen by the Life Cycle Costs (LCC). Quality, constructability, appearance of the structure's interior and exterior, HVAC size and weight, and LCC are some of the criteria influencing the choice. Methods: To monitor a project's progress based on energy savings, standard measures such as cost variance (CV) and schedule variation have used an idea when tracking the performance of intelligent buildings. Also, as described in the article, this research compared the decision-making limits of Building Information Modelling (BIM) and (MCDM). Analysis: The conventional approach cannot reveal information regarding divergence from the expected level of performance. Based on the outcomes of the construction cost variables, the key finding was the observation of 12 efficient elements. Finding and Novelty: According to the R, a building's most valuable features are its (Energy Saving Features, Warranties, Budget, Protect Your Unit, SEER Ratings, and Home Square Footage). The findings of Actual value (AV) and planned value (PV) were significantly different, as noted by the Multi-Criteria Decision Maker (MCDM). The new method also makes it possible to track project costs and timetables more accurately. The paper will characterize the HVAC Decision Support System's architecture (HVACDSS). Also, a case study of action modeling is provided, and the preliminary findings are addressed. Six criteria characteristics are used by the HVACDSS technique by an analysis of building construction conducted using the WEKA mining tool (decision tree).

Page 6 of 7 | Total Record : 62

 3   4   5   6   7 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue