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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.
Arjuna Subject : -
Articles 1,172 Documents
 112   113   114   115   116 
Attributes Classification for Elaborating the Information of Digital and Imaging Mapping Mukhaiyar, Riki; Mukhaiyar, Utriweni
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2353

Abstract

The rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world. Any information is fine catching up by updated with the latest news and even detailed information on local conditions. With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc., are required to inform and elaborate any appropriate decision needed. The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information. In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images. As the name suggests, this system captures detailed geographic information about a location or region. By inputting classified images of remote sensing results into a GIS database at regular intervals (adjusted as necessary, such as every year, every two years, every three years, etc.), the number of information sources that can be obtained increases. There are several reasons for that. First, remote sensing images are images that cover the entire surface of the Earth. Next, remote sensing images are images that contain information about the state of the earth's surface. Third, a variety of information can be obtained by performing appropriate image processing. Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome.
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.
Deep Learning-Based Early Dropout Prediction in University Online Learing Park, Hee-Sun; Yoo, Seong-Joon; Gu, Yeong-Hyeon
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.4258

Abstract

With the global transition of universities to online education due to COVID-19, the high dropout rate in online learning has become a critical challenge for higher education institutions. To address this issue, this study aims to develop a deep learning model for early dropout prediction in university online education. The proposed model was built by collecting and analyzing daily learning history data stored in the Learning Management System (LMS). Unlike previous studies that primarily relied on data collected at the end of the online learning period, this study analyzes students' behavioral data over time to more accurately identify students at risk. The research utilized data from a cyber university located in Seoul, South Korea, including approximately 30,574 student records and 12,014,610 learning history entries from the academic management system. To validate the model’s performance, data from the following academic year, which was not used for model training, was employed. The study compared the effectiveness of traditional machine learning methods with deep learning techniques (DNN and LSTM). Specifically, it proposed the LSTM-DNN model, which effectively learns both static learner information data and sequential learning history data. The results demonstrated that the LSTM-DNN model achieved a prediction accuracy of over 92%, confirming its effectiveness in providing real-time dropout risk assessments and predictive insights. Ultimately, this study proposes a novel approach to integrating real-time dropout prediction services into university Learning Management Systems (LMS), thereby contributing to student retention and academic success in online learning environments.
An Enhanced Routing Protocol For Vehicular Ad Hoc Networks With Swarm Intelligent Tareq, Mustafa; Farhan, Yasir Hadi; Nafea, Mohammed Mansoor
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3298

Abstract

A Vehicular Ad Hoc Network (VANET) is a transient network of wireless mobile nodes operating without centralized administration or pre-existing infrastructure. VANETs are a subset of Mobile Ad Hoc Networks (MANETs) designed to facilitate vehicular communication. This allows vehicles to communicate directly with roadside devices or with each other. These networks are appropriate for applications like infotainment services, traffic control, and accident avoidance since they are dynamic, decentralized, and highly flexible. However, their lack of established infrastructure presents serious difficulties, especially when preserving dependable routing and energy efficiency. Path selection in VANETs usually attempts to limit the number of intermediary nodes required to reach a destination to reduce latency and possible points of failure. However, as the distance between nodes increases, so does the required transmission power, directly impacting the network's energy consumption. As a result, energy-efficient routing is crucial to maintain network longevity and performance. This paper introduces the Bee Destination Sequenced Distance Vector Routing (B-DSDV) protocol, utilizing swarm intelligence principles via the Artificial Bee Colony (ABC) algorithm to enhance energy efficiency within a DSDV framework. This integration incorporates the Bee Algorithm into the discovery mechanism of DSDV to identify the most accessible node and the shortest route based on node distances. The algorithm assesses both the power levels of nodes and their distances to others. Route selection is optimized by considering the power consumption of intermediate nodes between the source and destination. Performance evaluation of the B-DSDV protocol is compared with established protocols, demonstrating its effectiveness in selecting high-power optimal paths and improving overall performance. The simulation results were conducted based on average throughput, average energy consumption, average end-to-end delay, and packet delivery ratio performance metrics. We conducted a simulation study using Network Simulator (NS) version 2.35 to evaluate the performance metrics of the routing protocols. Regarding energy consumption, the B-DSDV protocol achieved superior results, approximately 0.10% concerning packet size, compared to other protocols.
The Role of Information Technology in Governance Mechanism for Strategic Business Contribution: A Pilot Study Setyadi, Resad; Abd Rahman, Aedah; Subiyakto, A'ang
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1657

Abstract

Information Technology Governance (ITG) aligns IT and business transformation in schools. Private schools need to implement a method to evaluate the process of aligning IT strategy with business strategy and whether IT investment supports business objectives.  What factors influence the ITG of selected Indonesia High School (HS) aligning IT-Business strategy? Partial Least Squares Structural Equation Modeling (PLS-SEM) is a tool for analyzing the ITG model results in this study. The result is the composition of 9 variables with four independent variables (as structure mechanism variables), four dependent variables (as process and relational mechanism variables), and the ITG variable (as the final variable) shows a significant value of 0.75 at the ITG variable. This considerable value means that the ITG supporting variables of four independent and four dependent variables significantly affect the ITG variable by 75%. This study provides information if the system trust variable is increasing due to the influence of good IT strategy (independent) variables and good business (dependent) variables. The recommendation is that this ITG trust model can be used to evaluate the alignment of IT strategy with business strategy and whether IT investment supports business objectives in HS
A Review on Classifying and Prioritizing User Review-Based Software Requirements Salleh, Amran; Said, Mar Yah; Osman, Mohd Hafeez; Hassan, Sa’adah
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3450

Abstract

User reviews are a valuable source of feedback for software developers, as they contain user requirements, opinions, and expectations regarding app usage, including dislikes, feature requests, and reporting bugs. However, extracting and analyzing user requirements from user reviews is ineffective due to the large volume, unstructured nature, and varying quality of the reviews. Therefore, further research is not just necessary but crucial to effectively explore methods to gather informative and meaningful user feedback. This study aims to investigate, analyze, and summarize the methods of requirement classification and prioritization techniques derived from user reviews. This review revealed that leveraging opinion mining, sentiment analysis, natural language processing, or any stacking technique can significantly enhance the extraction and classification processes. Additionally, an updated matrix taxonomy has been developed based on a combination of definitions from various studies to classify user reviews into four main categories: information seeking, feature request, problem discovery, and information giving. Furthermore, we identified Naive Bayes, SVM, and Neural Networks algorithms as dependable and suitable for requirement classification and prioritization tasks. The study also introduced a new 4-tuple pattern for efficient requirement prioritization, which included elicitation technique, requirement classification, additional factors, and higher range priority value. This study highlights the need for better tools to handle complex user reviews. Investigating the potential of emerging machine learning models and algorithms to improve classification and prioritization accuracy is crucial. Additionally, further research should explore automated classification to enhance efficiency.
Switching On/Off Air Conditioner and Fan Alternately based on IoT Motion Detection and Room Temperature Abdul Shukora, Nurul Hidayah; Ahmad Baidowi, Zaid Mujaiyid Putra; Mohd Isa, Mohd Rizal; Darus, Mohamad Yusof; Abdullah, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2721

Abstract

The Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results.
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.
Analysis of Emission Reduction in Indonesia's Power Generation Sector for the Centennial Milestone using Grammatical Evolution and ARIMA Raharjo, Jangkung; Wijayanto, Inung; Nur Ikhsan, Rifki Rahman; Indra Wijaya, Igpo; Nugroho, Bambang Setia; Rokhmat, Mamat
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3067

Abstract

This study examines the Indonesian government's commitment to reducing electricity production, a crucial element in achieving sustainable energy. Historically, Indonesia depends on non-renewable energy sources, including coal and oil. Indonesia is presently transitioning to cleaner energy alternatives. This policy is done to align with the objective of global sustainability. This pivotal action by the Indonesian government aims to accelerate the adoption of low-carbon technology by society. Through careful planning, Indonesia aims to establish a sustainable and resilient energy framework that addresses both current and future environmental challenges. The active participation of both the state and private sectors is crucial to support this transition. For instance, investment in research and development of sustainable technology by the private sector can accelerate the improvement or creation of a more sustainable energy framework. Innovative technologies, such as solar, hydropower, and wind, can significantly contribute to reducing carbon footprints. This study conducted an extensive observation and evaluation of the contribution of Indonesia's power generation sector to achieving net-zero emissions. This study utilizes the Autoregressive Integrated Moving Average (ARIMA) and Grammatical Evolution (GE) to predict the overall electrical capacity trajectory leading up to Indonesia's Centennial in 2045. By utilizing the exponential grammar, GE outperforms ARIMA in predicting energy forecasts. This research sheds light on Indonesia's transformative efforts, contributing to a broader understanding of how to cultivate a sustainable and environmentally responsible energy future.
Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm Hananto, April Lia; Hananto, Agustia; Huda, Baenil; Rahman, Aviv Yuniar; Novalia, Elfina; Priyatna, Bayu
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1995

Abstract

Community work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers.

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