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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
Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms Kusnawi, Kusnawi; Ipmawati, Joang; Asadulloh, Bima Pramudya; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Rahardi, Majid
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.8.2.2453

Abstract

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.
Adoption of Industry 4.0 with Cloud Computing as a Mediator: Evaluation using TOE Framework for SMEs Abu Bakar, Muhammad Ramzul; Mat Razali, Noor Afiza; Ishak, Khairul Khalil; Ismail, Mohd Nazri; Tengku Sembok, Tengku Mohd
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.8.2.2205

Abstract

Industry 4.0 represents a significant shift in production processes, necessitating the integration of humans, products, information, and robots into digitalized workflows. While this transformation offers numerous benefits, its adoption, particularly among small and medium enterprises (SMEs), is hindered by various challenges such as financial constraints, maintenance costs, and a lack of digital culture and awareness. This study examines the adoption of Industry 4.0, specifically through cloud computing technologies, within the manufacturing and service sectors of SMEs in Malaysia. Cloud computing is economical, straightforward, and easily implemented for SMEs. We propose a conceptual model based on an extended Technology-Organisation-Environment (TOE) model, integrating refined constructs and considering digital organizational culture as a moderator, with cloud computing acting as a mediator to enhance firm performance. The study investigates the relationship between these constructs and addresses overlooked factors influencing adoption. Utilizing a structured questionnaire with 54 items derived from previous research, we employ partial least squares structural equation modeling (PLS-SEM) to analyze data collected from a pilot study. Our findings confirm the reliability and validity of the proposed conceptual model, meeting established criteria for composite reliability, average variance extracted (AVE), Cronbach's alpha, and discriminant validity (HTMT Criterion). Furthermore, this study presents empirical findings on technological, organizational, and environmental influences on adopting cloud computing. The insights gained from this research offer valuable guidance to enhance the performance of SMEs in the Industry 4.0 landscape.
Application of Cognitive Load Theory in VR Development and Its Impact on Learning: A Perspective on Prior Knowledge, Learning Interest, Engagement, and Content Comprehension Sulisworo, Dwi; Erviana, Vera Yuli; Robiin, Bambang
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.8.2.2467

Abstract

This research examines the utilization of Virtual Reality (VR) and its implications for the learning process, specifically focusing on learning interest, prior knowledge, learning engagement, and content comprehension. The central objective is to establish a comprehensive model that unravels the intricate interplay between these factors within the context of VR-based learning. The study also aims to shed light on the impact of integrating Cognitive Load Theory into VR development and its effects on the learning experience. Adopting an observational design, this study elucidates the intricate relationships among learning interest, prior knowledge, learning engagement, and content comprehension in VR-based education. The VR technology employed in this research has previously undergone rigorous feasibility testing. The VR application was designed following cognitive load theory principles. Its immersive content offers users a lifelike immersion into the natural habitats of diverse animal species across various global regions. By leveraging VR technology, elementary school students engage in a more profound and authentic learning journey. A total of 85 participants, encompassing fourth and fifth-grade elementary school students, were involved in the study. These students were drawn from schools situated in rural areas in particular regions in Indonesia and had moderate to low economic backgrounds. The variables under examination include Prior Knowledge, Learning Interest, Engagement, and Content Comprehension as learning outcomes. Data analysis was conducted utilizing a blend of linear regression and path analysis techniques, with a confidence level of 95%. The Guttman scale questionnaire was used, and total scores were transformed into a ratio scale through a conversion process. The study reveals a positive correlation between learning interest and learning outcomes, highlighting that a strong interest in a subject contributes to improved learning results. Additionally, both learning interest and prior knowledge influence learning engagement. Students with higher learning interests and prior knowledge are more likely to actively engage in the learning process actively, underscoring internal factors' role in motivating participation. Learning engagement moderates the relationships between learning interest, prior knowledge, and learning outcomes. By enhancing the effect of learning interest and prior knowledge on learning outcomes, engagement enables more comprehensive and practical information processing.
Conceptualizing Digital Readiness, Strategic Foresight, and Strategic Flexibility as Drivers of Digitalization and Performance of Small and Medium Enterprises Alqam, Hanin S; Razzak, Mohammad; Al-Busaidi, Adil; Al-Riyami, Said
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.8.2.2230

Abstract

The drivers of digitalization and small and medium enterprises (SMEs) performance have been primarily examined through resource-based theories. Hence, this study presents an alternative perspective based on such organizations' readiness and dynamic capabilities through a conceptual framework. A conceptual framework is developed by drawing upon the digital readiness theory (DRT) along with the dynamic capabilities view (DCV) to propose an integrated framework that posits a set of propositions linking constructs that reflect both digital readiness as well as the dynamic capabilities of an organization as possible drivers of business process digitalization (BPD) and performance. The empirical literature based on the DRT suggests that digital readiness will likely drive BPD and performance. Whereas leveraging the premise of the DCV indicates that the ability to sense opportunities and threats is reflected by strategic foresight. In contrast, the ability to seize and transform is reflected through strategic flexibility. The propositions posit that all three factors influence performance directly and through the mediating effect of BPD. The framework developed in this study may provide clues to practitioners and policymakers related to SME development regarding potential drivers of digitalization and performance. Growing scholarly publications on antecedents of digitalization and the performance of SMEs have focused primarily on resources. The current study offers an alternate perspective by integrating the two theories based on such organizations' readiness and dynamic capabilities.   
A Robust License Plate Detection System Using Smart Device Bin Mohamad Azhar, Muhammad Darwish; Goh, Kah Ong Michael; Check Yee, Law; Connie, Tee
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.8.2.2287

Abstract

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

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