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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 54 Documents
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Search results for , issue "Vol 7, No 4 (2023)" : 54 Documents clear
Teachers’ Acceptance and Readiness to Use Augmented Reality Book to Teach English Vocabulary in Kindergartens Muryanti, Elise; Pransiska, Rismareni; Novrianti, -; Arkanasya Ummayah, Yasifa; Noer Azhari Azman, Mohamed
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: 10.62527/joiv.7.4.2168

Abstract

English is currently introduced in several Kindergartens in West Sumatra, Indonesia. The focus of introducing English in Kindergartens is introducing simple instruction and vocabulary. The literature review and initial observations show that teaching materials and learning resources to introduce English are still limited. This study aims to explore kindergarten teachers' perspectives on storytelling and using Augmented reality books. This research used a mixing method. The participants in this subject were 57 Kindergarten teachers in West Sumatra, Indonesia. The data of this research was collected through questionnaires and interviews. First, the questionnaire was distributed to explore teachers' acceptance and readiness to use augmented reality storybooks in teaching English vocabulary in a classroom. To validate the data from the questionnaire and interview, they were triangulated. The result shows that most teachers do not have a background in English qualification. They believe that AR can be useful in teaching English vocabulary. Regarding readiness to implement the AR book, the teacher feels optimistic about using the AR stories in the classroom. The teachers believe that AR can motivate children to learn. The respondent teachers accept AR and believe that AR books can benefit children in learning English vocabulary. The ease of using AR and the benefits of AR for teaching vocabulary encourage teachers' readiness to utilize AR storybooks for teaching English vocabulary to Kindergartners. Based on the result of this research, the AR storybook is designed to teach English to Children in Indonesia Kindergarten.
Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall Huda, Ilham; Achmad Suhendra, Agus; Arif Bijaksana, Moch
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: 10.62527/joiv.7.4.2414

Abstract

The classification of churn is driven by the potential risks e-commerce companies face, such as losing customers who discontinue their service usage or churn. Marketing specialists have shifted their efforts from acquiring new customers to retaining existing ones in order to mitigate customer churn. Predictive models are created using data mining techniques to identify customer churn patterns. This study proposes a data mining model aimed at predicting customer behavior, with the processed results utilized as suggestions for improvements and company strategies in customer retention through segmentation and classification. Segmentation and classification involve several variables: Session, Interaction with Application, Actions taken during the interaction, purchasing, claim, and discount. This study employs a clustering technique based on the Recency, Frequency, and Monetary (RFM) model, which considers factors such as the time since the last visit, the number of visits, and the total amount spent by the customer. The classification algorithm model was evaluated by comparing three classification algorithms: decision tree and Support Vector Machine (SVM). The decision tree algorithm had the highest accuracy, achieving an impressive 87% accuracy rate in customer classification. Factors influencing customer churn include purchasing behavior, session activity, claim feature utilization, adding products to cart, and discounts. Improving stock management is crucial to prevent stock shortages, likely to cause churn. Additional measures like sending emails/notifications and offering vouchers/loyalty points can be implemented for customers who added products to their carts but didn't complete the purchase, with a focus on popular products.
Processing Plant Diseases Using Transformer Model Marcus Lye, Hong Zheng; Ng, Kok Why
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: 10.62527/joiv.7.4.2291

Abstract

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.
Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods Rangkuti, Abdul Haris; Hasbi Athala, Varyl; Haridhi Indallah, Farrel; Tanuar, Evawaty; Muliadi Kerta, Johan
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: 10.62527/joiv.7.4.1359

Abstract

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.
Convolutional Neural Network Model for Sex Determination Using Femur Bones Nasien, Dewi; Adiya, M. Hasmil; Afrianty, Iis; Farkhan, Mochammad; Butar-Butar, Rio Juan Hendri
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: 10.62527/joiv.7.4.1711

Abstract

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes.
Implementation of Big Data Information System Using Open-Source Metabase for Civil Registration and Vital Statistics Data Visualization in Surabaya Budiarti, Rizqi Putri Nourma; Sukaridhoto, Sritrusta; Zuhdi, Ubaidillah; Rasyid, Alfandino; Sonhaji, Agus Imam
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: 10.62527/joiv.7.4.1722

Abstract

Civil registration involves the mandatory and continuous documentation of important life events of a country's population under local legal requirements. In many countries, these documents are required to access government services such as education, healthcare, social services, formal employment, insurance benefits, and inheritance rights. Indonesia should prioritize building a comprehensive Civil Registration and Vital Statistics (CRVS) using a big data information system to ensure every individual has a legal identity, can access government services, and collect accurate and reliable statistics on vital events through geospatial maps. Surabaya, a city in Indonesia, still needs a comprehensive Civil Registration and Vital Statistics (CRVS) system. We produce many informative visualizations from the query and modeling processes in Metabase. Based on the PIECES framework, this application's importance level is 4.56 or 91.25%, meaning the application is important, and the satisfaction level is 4.29 or 85.76%, meaning the application is satisfied for the respondents. This research provides a brief overview of how Metabase works and how it can be used to generate visualizations of job-type data. It demonstrates the ease with which visualizations can be changed and customized. It had a good affordability point, making its implementation easier and more beneficial. It also emphasizes the importance of having a powerful tool like Metabase for data analysis and decision-making, especially for the dispendukcapil as a civil registration agency.
Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms Gunawan, Wawan; Wiradiputra, Raychal Ababil; Puspita Sari, Anggi; Prayama, Deddy; Rikardo Nainggolan, Esron
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: 10.62527/joiv.7.4.1514

Abstract

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, developers must have sufficient fundamentals, and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. Using Naïve Bayes and C 4.5 algorithms into a dataset with nine programming languages related to mobile app development. Results obtained in This research show Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native app frameworks is a technology that would have the opportunity in the future with an accuracy level above 90% with Naïve Bayes and C 4.5 algorithms. These results are obtained by testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. This research Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology.
Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control Anandika, Arrya; Ferdian, Rian; Eriyandha, Alivia; Suwandi, Rifki; Hafidz, Muhammad
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: 10.62527/joiv.7.4.1745

Abstract

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages
Optimization of General Threshold Value for Preprocessing in Plasmodium Parasites Detection Nugroho, Hanung Adi; Nurfauzi, Rizki
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: 10.62527/joiv.7.4.1285

Abstract

The high mortality rate of malaria makes it a severe disease that spreads throughout all-region by infected female Anopheles mosquitoes, especially in tropical countries. Accurate early malaria detection is one of the ways to reduce the mortality rate. Microscopy-based malaria examinations are still considered the gold standard. Due to numerous large malaria patients with limited parasitologists, an automated detection system is needed as a second opinion to assist parasitologists. This study proposed an optimization method for finding an optimal global threshold value for pre-processing parasite detection. There were three stages of the proposed method. The first is to pre-process digital microscopic images using color channel selection, contrast stretching, and morphological operation. The second is to find the global threshold value using multiple modified Otsu’s. The third is to determine the optimum global threshold value. In the last stage, predicted threshold values are generated using a pattern recognition approach to determine the optimum global threshold value. The proposed method evaluated 468 microscopic images captured from hundreds of thin smear blood slides. The slides are provided by the Department of Parasitology-UGM and the Eijkman Institute for Molecular Biology. The set image contains 691 malaria parasites in all types and life stages of malaria parasites. The proposed method obtained a sensitivity of 99.6 % and the smallest FPs number compared to without the optimization.  It indicates that the proposed method has the potential to be implemented in the initial stages of the malaria detection system.
The Extension of the UTAUT2 Model: A Case Study of Indonesian SMEs Acceptance of Social Commerce Arista, Artika; Tjahjanto, Tjahjanto; Ernawati, Iin; Purabaya, Rudhy Ho; Abdullah, Engku Fadzli Hasan Syed
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: 10.62527/joiv.7.4.1847

Abstract

An entirely updated e-commerce platform referred to as Social Commerce was developed in response to the rise in social media use. Social commerce integrates interactions between buyers and sellers made possible by social media platforms and Web 2.0 technology. It is frequently seen as a subfield of e-commerce. Social commerce has been successfully introduced in developing countries. Many businesses around the world are small and medium enterprises (SMEs). For instance, SMEs in Indonesia can contribute up to 60.34% of the country's GDP and have a substantial labor pool. Using social commerce as an e-commerce platform can significantly improve the operational efficiency of small and medium-sized enterprises (SMEs). However, little empirical research has specifically examined how SMEs embrace social commerce. Given the high level of concern, further research is required. Therefore, the current study experimentally examined how local SMEs in rural Indonesia introduced social commerce. The study was modeled using the Unified Theory of Technology Acceptance and Use (UTAUT) 2 model and several previous studies. SmartPLS 4 software was used to model and evaluate data using partial least squares structural equation modeling (PLS-SEM). The findings of the 114 samples showed that relative advantage, social support, facilitation conditions, and the government's support of social commerce influenced behavioral intention to use social commerce. Behavioral intention to use social commerce influences the actual use of social commerce. The findings of this study can help local governments and policymakers develop social trade promotion regulations to help potential SMEs and entrepreneurs gain long-term business support.

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