JOIV : International Journal on Informatics Visualization
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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<![CDATA[Social Media Content and Data Analysis of Audience Engagement in the Tour and Travel Industry ]]>
<![CDATA[Kurniawan, Yohannes]]>;
<![CDATA[Harry, -]]>;
<![CDATA[Oktavianus, Kelvin]]>;
<![CDATA[Anwar, Norizan]]>;
<![CDATA[Cabezas, Diego]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.912
<![CDATA[Social media has become the most popular area where the primary users are the youth generation. Social media marketing has become the best promotion for many companies regardless of the private or public sector, medium or large companies, including Tour and Travel companies. The companies must survive this pandemic by using social media technology to promote their services by creating promotional content that can attract customers' attention on social media, which may help to increase company revenue. This study analyzes the engagement and interaction of promotion content in the context of marketing on social media, such as Instagram, during the Covid-19 pandemic. This research underlines the customer perception about tour and travel content on Instagram for the companies and content characteristics to know the best strategy for doing promotional activities on Instagram through the questionnaire as supporting material. This quantitative research method uses data analytics tools and content analysis methods. This study also obtained data from the official website, journals, books, and articles. This research also utilizes surveys as supporting material focusing on the Instagram data analysis using content analysis. The future research is presumed to describe a similar research strategy but investigates other social media platforms. In addition, this research dictated several factors that can affect the level of engagement on Instagram. Hopefully, future research will examine additional factors that can influence a company's marketing to achieve its marketing objectives and implementation over a more extended time.]]>
<![CDATA[Smart Campus Governance Design for XYZ Polytechnic Based on COBIT 2019 ]]>
<![CDATA[Ryan Adhitya Nugraha]]>;
<![CDATA[Ratih Syaidah]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.1257
<![CDATA[Technological developments drive growth in the industrial revolution and digital transformation era. Technological developments during the industrial revolution 4.0 affect characteristics, especially in work. In responding to the change in technological developments in employment, the Ministry of Public Works has the task of conducting public works affairs in the government environment in an orderly manner to support the president in administering state government. XYZ Polytechnic is a state university as a new pilot under the Ministry of Public Works, Republic of Indonesia. As a basis for future development as well as towards a smart campus and then getting policy directions for the development of smart campus governance at the XYZ Polytechnic, it is necessary to design IT governance, especially in the reconstruction of adaptive and responsive policies and the development of structured governance with structured information systems. This study uses COBIT 2019 as the framework for governance design. With the method from the field preparation stage, interviews then assessed and evaluated existing policies and conditions of field activities to create governance designs according to COBIT 2019. The research results contained a technology governance management design with 17 processes. Based on the capability assessment and gap analysis results, recommendations were made for XYZ Polytechnic, as discussed in the results section. The suggestions are in the form of recommendations related to people, processes, and the use of technology. The recommendations act as evaluation material to improve organizational performance by providing good smart campus governance to students and internal members of XYZ Polytechnic.]]>
<![CDATA[Ultra-wide-field Fundus Image Synthesis Using Various GAN Models ]]>
<![CDATA[Ara Ko]]>;
<![CDATA[Jungwon Cho]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.1256
<![CDATA[Many people lose sight due to diabetic retinopathy. The reason that diabetic retinopathy is dangerous is that it cannot return to its pre-onset state after the disease's onset. Most patients take fundus images that capture the retina, and the doctor uses the fundus images to determine the presence of disease. Existing fundus images could only identify a narrow range, making it difficult to diagnose the disease accurately. However, with technological advances, ultra-wide-field fundus images that allow the wider retina to be seen have emerged. However, in deep learning research, many studies use existing fundus images due to the lack of new data. In the case of new technologies such as ultra-wide-field fundus images, it was often difficult to obtain data, so deep learning research could not be done properly. In the case of ultra-wide-field fundus images, research was conducted using data from hundreds to ten thousand sheets, but compared to large-scale data sets, the deep learning performance is inevitably inferior compared to large-scale data sets. In this study, synthetic data were created using ultra-wide-field fundus images and various GAN models to solve this problem. As a result of the study, BEGAN was derived similarly to the real image in qualitative and quantitative evaluation. However, it fell into mode collapse and showed the same output even when a new input came in. Mode collapse in BEGAN could be appeared depending on the amount and size of data, so various studies using BEGAN are needed.]]>
<![CDATA[User Motivation Level Analysis of SME Collaboration Gamification ]]>
<![CDATA[Marisa, Fitri]]>;
<![CDATA[Uda, Tonich]]>;
<![CDATA[Maukar, Anastasia Lidya]]>;
<![CDATA[Andarwati, Mardiana]]>;
<![CDATA[Wardhani, Arie Restu]]>;
<![CDATA[Handini, Mia]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.791
<![CDATA[One of the problems of SME is the low motivation to collaborate; the lack of research on exploring the motivation to collaborate is an issue that needs to be focused on solving. Objectives. This research aims to explore the level and type of motivation that influences SME's interest in collaborating to provide new insights for SME managers to apply appropriate treatment in developing collaborative activities. This study analyses six octalysis core drives that affect user interest in the use of SME collaboration gamification applications involving 293 SME respondents in the East Java Province. The research method is descriptive and quantitative, using Smart PLS, with a path analysis and analysis model. This study formulates six hypotheses to determine the effect of six core drives on using the collaborative gamification system. The results showed that the four constructs had a p-value less than 0.05 and a T-Statistic value greater than 1.96, while the other two constructs produced the opposite value. This finding reveals that four core drives (Epic Meaning, Development, Social Influence, and Avoidance) affect user interest in using collaborative gamification applications. In contrast, two core drives (Ownership and Unpredictable) do not affect it. The implication of this study is a recommendation for developers of collaboration-gamification systems to consider the results of this hypothesis, especially the role of core-drive catalysis as a reference in revising or developing collaborative gamification systems. Future work could apply the TAM model to analyze the technology acceptance rate of this system.]]>
<![CDATA[Student Engagement Mechanism of Online Learning: The Effect of Service Quality on Learning Management System ]]>
<![CDATA[Prabowo, Hartiwi]]>;
<![CDATA[Yuniarty, Yuniarty]]>;
<![CDATA[Bramulya Ikhsan, Ridho]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.1263
<![CDATA[Since typical classrooms do not include discussions, collaborative learning, or interactive learning activities, engagement is a major challenge in distant learning. Online learning satisfaction levels should be measured as evaluation material for future implementation. Although online learning has many advantages, a high dropout rate remains a significant challenge. This study investigates how higher education students' engagement and satisfaction with online learning are enhanced by information, system, and service aspects. The research design was quantitative research, and we used a questionnaire to collect data. The questionnaire was designed on a five-rating interval scale. The sampling technique was simple random sampling. The target minimum sample was counted using the Slovin method, and 206 undergraduate students taking online courses were surveyed online. The model was tested using structural equation modeling partial least squares (SEM PLS). This method is useful for investigating the relationship between constructs. The model was tested with the application of the SmartPLS program. The results revealed a positive and significant effect of system quality, information quality, service quality to student engagements, and their impact on student satisfaction, both direct and indirect. This study answers the literature gap and verifies the importance of online learning quality factors on students’ satisfaction and engagement. These results are expected to help to improve online learning in higher education settings, specifically on students' engagement and satisfaction, leading to perseverance and success.]]>
<![CDATA[Enhanced Technology for Logistics Courier Delivery Using RFID Label to Minimize Processing Time ]]>
<![CDATA[Novitasari, Nia]]>;
<![CDATA[Anwar, Nashirudin]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.1229
<![CDATA[Courier services have become a sector that has experienced a growth spurt during the Covid-19 pandemic. The soaring growth of courier services is due to e-commerce in Indonesia. Increased people's digital activities show this during the pandemic, including online or online shopping. Data from the Indonesian Ministry of Finance shows that purchase transactions via e-commerce increased 18.1 percent to 98.3 million, with a total transaction value of 9.9 percent to Rp20.7 trillion. Fast and efficient delivery and pick-up of goods is the core operation of courier services. The biggest challenge for courier service providers is how to compete with other companies that offer the same type of service. Service users are increasingly demanding the security and reliability of delivery services so that they can meet the expectations of service users. The expectations of service users used as targets for company achievement are (1) reliability (on time, accuracy, integrity), (2) convenience (collecting units, delivery coverage, operating hours), (3) services, and (4) cost. Based on the activities in courier services, the potential for errors or inefficiencies in processing time is in the pre-delivery activities. In the pre-delivery activity is also the initial activity used to input the data base, collect goods, distribute goods and so on. This research proposes that RFID Label technology can overcome errors and increase process time efficiency in shipping goods on courier services, especially in pre-delivery and delivery activities.]]>
<![CDATA[Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System ]]>
<![CDATA[Rahmatulloh, Alam]]>;
<![CDATA[Muhammad Ramadhan, Galih]]>;
<![CDATA[Darmawan, Irfan]]>;
<![CDATA[Widiyasono, Nur]]>;
<![CDATA[Pramesti, Dita]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.1262
<![CDATA[The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack.]]>
<![CDATA[Academic Document Authentication using Elliptic Curve Digital Signature Algorithm and QR Code ]]>
<![CDATA[Wellem, Theophilus]]>;
<![CDATA[Nataliani, Yessica]]>;
<![CDATA[Iriani, Ade]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.62527/joiv.6.3.872
<![CDATA[Paper-based documents or printed documents such as recommendation letters, academic transcripts, and diplomas are prone to forgery. Several methods have been used to protect them, such as watermarking, security holograms, or using paper with specific security features. This paper presents a document authentication system that utilizes QR code and ECDSA as the digital signature algorithm to protect this kind of document from counterfeiting. A digital signature is a well-known technique in modern cryptography used for providing data integrity and authentication. The idea proposed herein is to put a QR code in the printed documents where the QR code includes a digital signature. The signature can later be authenticated using the proposed system by uploading the document for authentication or scanning the document's QR code. The proposed system is particularly developed for digital signature generation and verification of students' final project approval documents as the case study. In traditional settings, the approval form is typically signed directly by the student's advisor dan co-advisor using handwritten signatures. However, using the conventional handwritten signature, the signature on the approval form can be falsified. Therefore, a digital signature generation and verification system is implemented herein to avoid handwritten signature falsification. The advisors can use this system to sign the approval form using a digital signature instead of a handwritten one. The signature is stored in a QR code and is generated using ECDSA with SHA-256 as the hash function. The proposed system is evaluated using documents (i.e., approval forms) with genuine and forged QR codes. The evaluation results showed that the system could verify the authenticity of the approval forms, which contain genuine QR codes. The approval forms that contained forged QR codes were correctly identified.]]>
<![CDATA[Exploration of The Impact of Kernel Size for YOLOv5-based Object Detection on Quadcopter ]]>
<![CDATA[Rissa Rahmania]]>;
<![CDATA[Felix Corputty]]>;
<![CDATA[Suryo Adhi Wibowo]]>;
<![CDATA[Dany Eka Saputra]]>;
<![CDATA[Annisa Istiqomah]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.6.3.898
<![CDATA[Drones or quadcopters have been widely used in various fields based on deep learning, especially object detection. However, drone vision characteristics such as occlusion and small objects are still being explored for performance in terms of accuracy and speed detection. The YOLO architecture is very commonly used for cases requiring high-speed detection. To overcome the limitations of drone vision, in this paper, we explore the size of the YOLOv5s backbone kernel in the shallowest convolutional layer to achieve better performance. The kernel is a filter that has a main role in the feature map, and it defines the size of the convolution matrix, and the resulting features in the shallowest convolutional layer are more representative of the case of object detection and recognition. The techniques can be divided into three major categories: (1) data preprocessing, which involves augmentation and normalization of the data, (2) kernel size exploration in the shallowest convolutional layer of the YOLOv5s, and (3) model implementation in the real environment using the quadcopter. The dataset consisted of four classes representing dragon fruit, snake fruit, banana, and pineapple, with a total of 8000 data. Exploration results with kernel size give promising results. Kernel sizes 5 and 7 give an mAP of 0.988. Through these results, modification of the kernel size provides an opportunity for more in-depth investigations, such as with the epoch parameter, padding scheme, and other optimization techniques.]]>
<![CDATA[Convolutional Neural Network featuring VGG-16 Model for Glioma Classification ]]>
<![CDATA[Agus Eko Minarno]]>;
<![CDATA[Sasongko Yoni Bagas]]>;
<![CDATA[Munarko Yuda]]>;
<![CDATA[Nugroho Adi Hanung]]>;
<![CDATA[Zaidah Ibrahim]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022) ]]>
Publisher : <![CDATA[Politeknik Negeri Padang ]]>
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DOI: 10.30630/joiv.6.3.1230
<![CDATA[Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%. ]]>
