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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 35 Documents
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Search results for , issue "Vol 7, No 4 (2023)" : 35 Documents clear
The Analysis of Organizational Changes using Structural Equation Modelling with Mediating Readiness to Change in Higher Education Rindang Ayu; Nurul-Azza Abdullah; Wan Shahrazad Wan Sulaiman; Mohd Nasir Bin Selamat
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02252

Abstract

This research study demonstrates that the readiness to change moderates the association between supervisor support and commitment to organizational change. The variable "readiness to change" fulfills this moderating function. The State University in West Sumatra, a prestigious institution of higher education in Indonesia, boasts a faculty comprising 260 committed teaching personnel. The formulation of the questionnaire was grounded upon a predetermined set of criteria. The data analysis process involved utilizing Structural Equation Modelling (SEM) and SmartPLS 3.0 software. The results obtained from using structural equation modeling (SEM) were consistent with recognized metrics such as Cronbach alpha, composite reliability, mean-variance extracted, and evaluation criteria for both measurement and structural models. Furthermore, it also showcased the soundness and reliability of the measurement instruments. The study suggests that the mediating factor of preparation for change plays a role in the association between the provision of high-quality support and the level of commitment towards organizational change. This study contributes to the field of management and organizational leadership by providing insights on how to develop robust change strategies through enhancing employees' readiness for transition. This study makes a valuable contribution to the field of change management by emphasizing the role of readiness to change as a mediating factor in the relationship between supervisor support and organizational change commitment. This research additionally aids organizations in developing grooming and training programs aimed at equipping employees with the necessary skills and knowledge to adapt to change.
A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems Jitendra Singh Yadav; Narendra Singh Yadav; Akhilesh Kumar Sharma
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01692

Abstract

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities.
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model Anggit Dwi Hartanto; Yanuar Nur Kholik; Yoga Pristyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01740

Abstract

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.
Implementation of 5G Telecommunication Network Services in Indonesia based on Techno-economic Analysis Siti Hajar Komariah; Rd. Rohmat Saedudin; Rizki Yantami Arumsari; Umar Yunan KSP Yunan KSP
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02447

Abstract

The 2300 MHz spectrum is a medium band that telco operators will not pay much attention to when they deploy 5G. They are more comfortable at 2.6 GHz, 3.5 GHz, 26 GHz, and 28 GHz, in addition to 700 MHz for the breadth of coverage. The performance of cellular telecommunications services based on 5G technology is possible for new operators, although it will be carried out as stand-alone services. This opportunity will be taken by looking at internet subscriber data/data communication from existing operators as active internet users, which is quite large and has a potential of over 250 million users. There has been no previous study regarding the feasibility of deploying this 5G technology-based Broadband Wireless Access (BWA) Network. Based on the experience of implementing previous generations of telecommunication service technology, the government and operators need to be careful in determining the right moment to deploy this 5G technology service, which is predicted to be able to provide broadband services with streaming capabilities of 10 to 100 times the streaming speed of 4G technology. It should be noted that the lack of success of 3G performances in 2006 from 2G, 2.5G, and 2.75 G. Almost all operators who were expected to be very lucky turned out to be not optimal; even now, only 4 operators are playing on 3G. where they have not been able to force users of the 2G generation to switch to 3G, including in big cities where the performance of the 3G network is not yet optimal and evenly distributed. Still, many areas are blank spots from 3G networks and services. From this experience, scientific studies are needed to ensure the feasibility of the upcoming 5G BWA business and identify business opportunities that can be implemented. The feasibility analysis must be viewed from various aspects, namely aspects of technical readiness, market aspects, and financial aspects in terms of the techno-economics of the operators who will provide 5G telecommunications services by calculating several essential parameters as a measure of business feasibility, namely Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PBP).
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal Phie Chyan; Andani Achmad; Ingrid Nurtanio; Intan Sari Areni
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02026

Abstract

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01498

Abstract

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.
The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge Cahyo Crysdian
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01727

Abstract

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research. 
Design and Development of Sound and Rhythm Perception Assessment Application for Students with Hearing Impairment - Damri; - Safaruddin; - Marlina; Jon Efendi; Elsa Efrina
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02223

Abstract

Technology use is becoming increasingly popular in life, including in educational aspects. Some widely used applications in education include metaverse, blended learning, game learning, cloud-based learning, mobile applications, and social media learning. Apps are generally in the form of software applications or programs designed to run on smartphones. In this study, we propose using applications in assessing children with hearing impairments at school. Design and Development of the Sound and Rhythm Perception Assessment Application uses the ADDIE development model of Analysis, Design, Development, Implementation, and Evaluation. The test subjects in this study were validation test subjects consisting of 3 experts to test the feasibility of the application. Data was collected through a questionnaire in the form of a tool tested for validity and reliability with a score of 90.1% for learning design, 88.9% for layout, and 94.7% for software. Validation was carried out through focus group discussions. The application was tested on four teachers who teach students with hearing impairments. The results of the main field experiment show that teachers can use the application to help them assess students with hearing loss. With availability, the accuracy of the Design and Development of the Sound and Rhythm Perception Assessment Application can be further improved by conducting training with more teachers who teach children with hearing impairments at school.
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Syafrial Fachri Pane; Adiwijaya Adiwijaya; Mahmud Dwi Sulistiyo; Alfian Akbar Gozali
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.02415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use Eri Wahyudi; Dwiny Meidelfi; Nofrizal -; Zulfan Saam; Juandi -
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01873

Abstract

During the current pandemic, smartphones have become a means of learning for all students in Indonesia, including high school students. Students use smartphones to send assignments, learn via video calls, and conduct online exams. The prolonged use of smartphones, from the beginning of learning hours in the morning to study hours in the evening, has a terrible impact on the ear health of high school students in Padang. Excessive smartphone use caused a decrease in the student's hearing function. Therefore, this study aims to group the audiometry results of high school students in Padang who have a hearing loss function. The audiogram result is only performed as the result of a frequency test of the subject's hearing in both the left and right ear. Conventionally, an otolaryngologist concluded the final decision of hearing loss ability. This research proposed an automatic classification of audiometry results using machine learning methods. The K-Medoids clustering was selected to classify the audiometry data in this research. Of 210 audiometry data, 91 data is confirmed by an otolaryngologist as valid data. By using the K-Medoids clustering, 93 data is classified into Normal hearing, Mild Hearing loss, and Moderate Hearing loss. The proposed model successfully grouped the audiometry data into three categories. The confusion matrix is applied to measure the model performance, which has 28,3% accuracy, 64,3% precision, and 21,4% recall. 

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