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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 55 Documents
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Search results for , issue "Vol 8, No 2 (2024)" : 55 Documents clear
Lightweight Image Encryption Based on A Hybrid Approach Jabbar Altaay, Alaa A.; N. Hasoon, Jamal; Kassim Albahadily, Hassan
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.8.2.2757

Abstract

A secure image could be achieved by encryption, a technique for securing images over different media transmission lines with privacy and keeping them safe for the receiver. This paper proposes an image encryption approach to achieve excellent security by combining a lightweight encryption algorithm with the chaotic Peter De Jong map. The Lilliput algorithm, lightweight encryption, uses the Peter De-Jones map to produce keys. The suggested approach achieved a suitable level of complexity that matched the historical demands for transmission images. Two methods were used to conduct the tests on a standard image collection: an encrypted image and a generated key. Standard metrics find the similarity between the input and output images to achieve an accurate proposal performance. The encrypted image's entropy was assessed and discovered that it matched the original image values exactly. The results were satisfactory regarding obtaining a precise correlation rate between the original and encrypted photos. The decryption and reconstruction of the image were completed quickly and steadily, with a high success rate and excellent outcomes. The proposed approach was evaluated on a dataset of well-known test photos with unique features, including varying degrees of lightness and shade to create the perfect test.
Web and Android-based Test Application Development and its Implementation on Final Semester Examination Ambiyar, -; Panyahuti, -; Devega, Army Trilidia; Islami, Syaiful
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.8.2.2120

Abstract

This research aims to revolutionize the examination process in vocational schools by developing the FlyExam application, an Android-based test platform derived from improvements to the TCExam interface. The core goal was to create a powerful, easy-to-use, and effective tool for semester assessment. Following a Research and Development (R&D) approach, this research uses a 4D model: Define, Design, Develop, and Disseminate. Validation procedures require expert evaluation of the technical aspects and usability of the application. At the same time, practicality is assessed through engagement with students and teachers, and effectiveness is measured by student performance. Expert reviews and user feedback confirm the validity and practicality of the application. During implementation, the LAN network topology proved to be a conducive environment for conducting semester exams, increasing the efficiency and reliability of the testing process. The integration of TCExam and FlyExam on mobile devices shows the potential of transitioning from traditional paper-based exams to digital platforms, offering greater flexibility and accessibility. Future research efforts could explore FlyExam's scalability and adaptability in various educational contexts and its long-term impact on assessment practices and academic outcomes. Additionally, ongoing improvements based on user feedback can lead to further improvements and the incorporation of new features, ensuring FlyExam remains relevant and effective in meeting evolving vocational education needs. In summary, the development of FlyExam represents significant progress in the modernization of assessment methodology, with the potential to simplify the process and improve the learning experience in vocational schools.
Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach Putra, Karisma; Prasetyo Kusumo, Mahendro; Prayitno, Prayitno; Wicaksana, Darma; Arrayyan, Ahmad Zaki; Pratama, Sakca Garda; Al-Kamel, Mujib Alrahman; Chen, Hsing-Chung
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.8.2.2047

Abstract

Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.
Exploring Technology Integration in Education: Lecturers Perspective on Outcomes-Based Education Platforms Kasih, Julianti; Wasis, Galih Wicaksono; Bunyamin, 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.8.2.2691

Abstract

Informatics education is evolving rapidly through the adoption of Outcome-Based Education (OBE), necessitating a rigorous investigation into the effectiveness of the implementation. This study was conducted using the advanced Unified Theory of Acceptance and Use of Technology (UTAUT)-3 model to assess the potential of OBE systems in enhancing teaching and learning processes. The study integrated a comprehensive set of nine variables to measure the acceptance level of OBE systems among lecturers at Maranatha Christian University Bandung and Universitas Muhammadiyah Malang. UTAUT-3 provides a more explicit understanding by incorporating Hedonic Motivation (H.M.), Habit (H), and Personal Innovativeness (P.I.). The Model also integrated the core constructs of Performance Expectancy (P.E.), Effort Expectancy (E.E.), Social Influence (S.I.), Facilitating Conditions (F.C.), Behavioral Intention (B.I.), and Users Behavior (U.B.). The result showed that B.I. was a central determinant of U.B., suggesting users' preparedness to engage with OBE systems.Furthermore, the routine use of technology as Habit (H) was closely related to Behavioral Intension (B.I.), showing that familiarity with technology facilitated the intention to adopt OBE systems. The result showed that UTAUT-3's comprehensive framework was superior in evaluating educational technology adoption due to its ability to account for users' engagement as Hedonic Motivation (H.M.), dispositional tendencies toward Personal Innovativeness (P.I.), and the critical role of established habits. Consumers' actual experiences and technological proficiency significantly influence adoption rather than individual characteristics. Therefore, UTAUT-3 was a more effective tool for predicting and understanding the Acceptance of OBE systems, guiding educational institutions toward successfully integrating information systems in learning environments.
Systematic Literature Review on Augmented Reality with Persuasive System Design: Application and Design in Education and Learning Nasirudin, Mohd Asrul; Md Fudzee, Mohd Farhan; Senan, Norhalina; Che Dalim, Che Samihah; Witarsyah, Deden; Erianda, Aldo
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.8.2.2702

Abstract

Augmented Reality (AR) is an innovative technology that has gained significant scholarly attention. It uses computer-generated sensory inputs like visuals, sounds, and touch to enhance how we perceive the real world, providing a transformative impact on human sensory experiences. Motivated by the possibilities of augmented reality (AR) in the realm of the educational learning environment, this research aims to document the evolving landscape of augmented reality (AR) applications in education and training, with a specific emphasis on the incorporation of persuasive system design (PSD) elements. The study also explores the diverse technologies and methodologies for developing these applications. A systematic literature review was conducted, analyzing 44 articles following the protocol for PRISMA assessments. Four research questions were formulated to investigate trends in AR applications. Between 2016 and 2023, publications on AR applications doubled, with a significant focus on the educational field. Marker-based AR methods dominated (68.49%), while markerless methods constituted 31.51%. Unity and Vuforia were the most used platforms, accounting for 77.27% of applications. Most research papers assessed application effectiveness subjectively through custom-made questionnaires. University students were identified as the primary target users of AR applications. Only a few applications integrated persuasive elements, even for adult users. This highlights the need for further studies to fully grasp the possibilities of combining persuasive system design with augmented reality applications in education
Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model Mohd Sani, Fareisya Zulaikha; Mohamad Zin, Ahmad Arif Izudin; Mohd Nor, Elya; Kamarudin, Nur Diyana; Makhtar, Siti Noormiza
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.8.2.2177

Abstract

The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.
Predictive Wireless Received Signal Strength Using Friis Transmission Technique Rawi, Roziyani; Mohd Isa, Mohd Rizal; Ismail, Mohd Nazri; Abu Bakar Sajak, Aznida; Yahaya, Yuhanim Hani
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.8.2.2178

Abstract

A good WLAN performance is crucial in determining the quality of experience (QoE) among the campus community. Proper WLAN planning and design should be done beforehand to ensure good WLAN performance. Various studies have discussed different methods of conducting WLAN planning to predict WLAN's best performance, including using artificial intelligence and mathematical approaches. One of the processes involved in performing WLAN planning is measuring performance parameters. Signal strength is one of the vital parameters to be measured in determining the excellent performance of WLAN in a particular area. When deploying a WLAN design in two different environments, the signal strength outcomes can differ due to various factors, including obstacles and path loss propagation issues within the deployment area. Higher Learning Institutions (HLIs) present a unique challenge as their building designs vary to accommodate student needs. As a result, the selection of materials used will also be different, affecting the WLAN performance. A detailed study should investigate the effect of path loss propagation and the type of obstacle that affects WLAN performance in HLI. Thus, this study focuses on predicting received signal strength using Friis Transmission and studying the effect of path loss propagation on WLAN performance. The simulated model significantly affects signal strength when the signal passes through different types of building material (non-LOS) and line-of-sight (NLOS), where concrete walls substantially affect the received signal strength between transmitters. The proposed model can assist network planners in designing robust WLAN infrastructure by improving signal strength, particularly in the HLI WLAN environment. 
Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards Ajhari, Abdul Azzam; Priambodo, Dimas Febriyan; Yulianti, Henny
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.8.2.2017

Abstract

The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.
Implementation of Multi Extension in Blockchain-Based IoT Platform for Industrial IoT Devices Prayudi, Agus; Sukaridhoto, Sritrusta; Udin Harun Al Rasyid, Muhammad; Hakim, Oktafian Sultan; Yohanie Fridelin Panduman, Yohanes; Putri Nourma Budiarti, Rizqi
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.8.2.2401

Abstract

The rise of the Internet of Things (IoT) has led to the creation of technologies to improve human life. IoT involves integrating the Internet with the physical world, spanning applications like smart homes, industries, supply chains, academia, and more. By the end of 2020, around 212 billion IoT devices were globally deployed, presenting substantial opportunities for manufacturers and diverse applications. There have been numerous implementations of IoT across various fields, including Blockchain IoT (B-IoT), Artificial Intelligence of Things (AIoT), Digital Twin, and new communication protocols like the Matter protocol. We conducted a comprehensive testing of the blockchain (B-IoT) extension system on various bandwidths and scenarios, such as blockchain API execution time, speed, retention performance, and smart contract vulnerability testing. Our testing has been successful, and several messaging systems were used. Kafka was recommended to overcome the pending transaction problem caused by unprocessed messages. Our smart contract exhibited high severity. The Artificial Intelligence of Things extension, tested on real environments for person and vehicle counters, has shown successful results. Digital Twin, integrated into the IoT platform to perform and control 3D assets such as the postgraduate PENS building, has demonstrated efficient performance. Matter protocol achieved an average task execution speed of 0.48 tasks per second. Matter P2P communication was also successfully tested in this research by implementing the Access Control List (ACL) command.
Big Mart Sales Data Visualization and Correlation Arista, Artika; Theresiawati, Theresiawati; Seta, Henki Bayu
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.8.2.1780

Abstract

The amount of unprocessed data available every day is growing. This massive amount of data needs to be effectively assessed to give results that are extremely useful. In the present day, it is crucial for inventory management and demand forecasting to collect sales data for commodities or things, together with all their numerous dependent or independent parts. In a Big Mart Company, the use of sales forecasting is to estimate numerous goods that are readily available and supplied at multiple retailers in different towns. As the number of products and outlets increased drastically, it became increasingly difficult to forecast them manually. As a result, it is necessary to see to what extent the relationship between several variables, including price, popularity, time of day, outlet type, outlet location, etc., affects the appeal of a product. In this research, a data cleaning process was carried out, and data visualization using scatter plots, as well as finding Pearson correlations. The raw processing the data with study of a case big mart sales data is taken from the Kaggle website [https://www.kaggle.com/datasets/sandeepgauti/bigmart-sales]. The Pearson correlation test determines a lack of connection between the two Item_Weight and Item_Outlet_Sales variables. There is a strong but negative correlation where if Item_Visibility decreases, Item_Outlet_Sales also decreases. Positive relationships exist between the two Item_MRP and Item_Outlet_Sales variables. In addition to the correlation test, descriptive statistical analysis is also performed here. With this simple data processing, the raw data will be better organized and easier to analyze, read, and use.

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