cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 36 Documents
 1   2   3   4 
Search results for , issue "Vol 7, No 1 (2023)" : 36 Documents clear
Selecting Control Menu on Electric Wheelchair Using Eyeball Movement for Difable Person Fitri Utaminingrum; I Komang Somawirata; Gusti Pengestu; Tipajin Thaipisutikul; Timothy K. Shih
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1011

Abstract

Each country's number of people with disabilities and strokes increases yearly. Hand defects and stroke make them have limitations in doing activities. It caused their hand has paralyzed. Hence, they find it difficult to do daily activities, such as running a wheelchair, choosing a menu on the screen display, and so on. One solution offered is utilizing eye movement as a navigation tool that can replace the role of the user's hand, so they can run a wheelchair independently or choose a menu selection on display by themselves through the movement of their eyes. Detection of eyeball movements in this study only utilizes a camera as a sensor mounted in front of the user. So that it is more practical and easier to use than if we have to pair an electrooculography sensor in the area around the user's eyes. This research proposed a new approach to detect the five gazes (upward, downward, leftward, rightward, and forward) of the eyeball movements by using Backpropagation Neural Network (BPNN) and Dynamic Line Sector Coordinate (DLSC). Line Sector Coordinate is used to detect the eyeball movement based on the pupil coordinate position. The eyeball movement direction was analyzed from four lengths of a line. Our proposed method can detect five gaze directions that can be used for selecting four menus on the display monitor. The mean accuracy of our proposed method to detect eye movements for each gaze is 88.6%.
The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices Suta Wijaya, I Gede Pasek; Mulyana, Heru; Kadriyan, Hamsu; Fa'rifah, Riska Yanu
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1591

Abstract

An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.
Machine Learning Algorithms Based on Sampling Techniques for Raisin Grains Classification Bisri, Achmad; Man, Mustafa
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.970

Abstract

Raisin grains are among the agricultural commodities that can benefit health. The production of raisin grains needs to be classified to achieve optimal results. In this case, the classification is carried out on two types of grains, namely Kecimen and Besni. However, inaccurate sample data can affect the performance of the model. In this study, two sampling techniques are proposed: stratified and shuffled. The proposed classification model is RF, GBT, NB, LR, and NN. This study aims to identify the performance of classification models based on sampling techniques. Classification models are applied to the seven-features dataset, and modeling is done by cross-validation. The results of the models were tested with a different amount of test data. The performance of the models was evaluated related to accuracy and AUC. The best outcomes of all models based on stratified sampling were founded on tested data of 40 percent with a mean accuracy of 85.50% and an AUC of 0.921. In comparison, models based on shuffled sampling were founded on test data of 20 percent with a mean accuracy of 88.11% and an AUC of 0.935. On the other hand, classification models based on a stratified sampling of all data splits do not all models generate an excellent category. Whereas, based on shuffled sampling, all models resulted in the excellent category. Therefore, models based on shuffled sampling are superior to stratified sampling. The result of the significant test, RF, significantly differs based on sampling techniques.
Mapping User Experience Information Overload Problems Across Disciplines Kusuma, Wahyu Andhyka; Jantan, Azrul Hazri; Abdullah, Rusli bin; Admodisastro, Novia; Mohd Norowi, Noris binti
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1588

Abstract

User Experience (UX) has been increasing linearly with the systems and digital media. UX concept describes a human factor as an experience with the life cycle of digital technology. UX increases the usability of the product in the industry more than functionality. Interest in UX has produced a huge amount of product and research articles. Moreover, this interdisciplinary topic becomes increased significantly because of the wider applications. However, this benefit become a problem due to the number of publications. The information overload problem is the result of the increasing UX topic. Several researchers solved this problem with qualitative analysis, but it cannot solve the overload problem. In this paper, we purposed bibliometric analysis and research profiling to interpret UX information on the map, with the publications from 1998-2022, a dataset compiled in RIS format to provide article metadata. As a result, the UX information map from the topic with the five clusters. Therefore, to provide information on the topic's coherence, we propose a coupling network. A related topic is shown as a link; a direct link means high coherence between topics. The analysis was carried out using the 5W1H approach (what, where, who, when, why, and how). The results show that UX is indeed an interdisciplinary field, especially with a design approach and user experience. In addition, to determine novice researchers, determining the focus of research can be done by taking into account previous research goals and maps.
Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR Wedashwara, Wirarama; Yadnya, Made Sutha; Sudiarta, I Wayan; Arimbawa, I Wayan Agus; Mulyana, Tatang
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1592

Abstract

Prevention of the effects caused by natural disasters such as earthquakes and landslides requires analysis of vibration propagation. In outdoor applications, internet sources such as WIFI are not always available, so it requires alternative data communications such as nRF24l01. The system also requires a portable power source such as solar power. This research aims to develop a vibration propagation analysis system based on the nRF24l01 wireless sensor network and solar power by implementing the fuzzy rule-based regression (FRBR) algorithm. The system consists of two piezoelectric and nrf24l01 vibration sensors. The system also uses a third node equipped with temperature and soil moisture sensors, air temperature and humidity, and light intensity as environmental variables. The evaluation results show the Quality of Services (QoS) results with a throughput of 99.564%, PDR 99.675%, and a delay of 0.0073s. The Fuzzy Association Rule (FAR) extraction results yield nine rules with average support of 0.319 and confidence of 1 for vibration propagation. The availability of solar power was evaluated with an average current value of 0.250A and a voltage of 3.266V. The results of FRBR are based on the propagation of the vibration that propagated and produced a mean square error (MSE) of 0.141 and a mean absolute error (MAE) of 0.165. The correlation matrix and FAR results show that only soil moisture has a major effect on the magnitude and duration of propagation. However, other variables can regress soil moisture with MSE 0.232 and MAE 0.287.
Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology Abdul Haris Rangkuti; Varyl Hasbi Athalaa; Farrel Haridhi Indallah; Fajar Febriansyah Febriansyah
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1032

Abstract

This study describes the optimization of hand gesture recognition on Raspberry Pi 4 technology has advanced over the past years, some computers are now able to compute much more complex problems like real-time object detection. But for small devices, optimization is required to run in real-time with acceptable performance in terms of latency and low-cost effect on accuracy. Low latency is a requirement for most technology, especially when integrating real-time object detection as input into Self-Service Technology on Raspberry Pi for the store. This research was conducted on 288 pictures with six types of chosen hand gestures for command inputs that have been configured in the Self-Service Technology as a training dataset. In the experiment carried out with 5 CNN object detection models were used, namely YOLOv3-Tiny-PRN, YOLOv4-Tiny, MobileNetV2-Yolov3-NANO, YOLO-Fastest-1.1, and YOLO-Fastest-1.1-XL. Based on the experiment after optimization, the FPS and inference time metrics have improved performance. The performance improves due to a gained average value of FPS by 3 FPS and a reduced average value of inference time by 119,260 ms. But such an improvement also comes with a reduction in overall accuracy. The rest of the parameters have a reduced score on Precision, Recall, F1-Score, and some for IoU. Only YOLO-Fastest-1.1-XL have an improved value of IoU by about 0.58%. Some improvements in the CNN and dataset might improve the performance even more without sacrificing too much on the accuracy, but it's most likely suitable for another research as a continuation of this topic.
Human Bone Age Estimation of Carpal Bone X-Ray Using Residual Network with Batch Normalization Classification Nabilah, Anisah; Sigit, Riyanto; Fariza, Arna; Madyono, Madyono
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1024

Abstract

Bone age is an index used by pediatric radiology and endocrinology departments worldwide to define skeletal maturity for medical and non-medical purposes. In general, the clinical method for bone age assessment (BAA) is based on examining the visual ossification of individual bones in the left hand and then comparing it with a standard radiographic atlas of the hand. However, this method is highly dependent on the experience and conditions of the forensic expert. This paper proposes a new approach to age estimation of human bone based on the carpal bones in the hand and using a residual network architecture. The classification layer was modified with batch normalization to optimize the training process. Before carrying out the training process, we performed an image augmentation technique to make the dataset more varied. The following augmentation techniques were used: resizing; random affine transformation; horizontal flipping; adjusting brightness, contrast, saturation, and hue; and image inversion. The output is the classification of bone age in the range of 1 to 19 years. The results obtained when using a VGG16 model were an MAE value of 5.19 and an R2 value of 0.56 while using the newly developed ResNeXt50(32x4d) model produced an MAE value of 4.75 and an R2 value of 0.63. The research results indicate that the proposed modification of the residual training model improved classification compared to using the VGG16 model, as indicated by an MAE value of 4.75 and an R2 value of 0.63.
The Reliability Analysis for Information Security Metrics in Academic Environment Ibnugraha, Prajna Deshanta; Satria, Anas; Nagari, Fabian Sekar; Rizal, Moch Fahru; NonAlinsavath, Khamla NonAlinsavath
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1593

Abstract

Today, academic institution involves digital data to support the educational process. It has advantages, especially related to ease of access and process. However, security problems appear related to digital data. There were several information security incidents in the academic environment. In order to mitigate the problem, metrics identification is required to determine the risk of incidents. There are many risks model and metrics to estimate the risk, such as DREAD, OWASP, CVSS, etc. However, specific metrics are required to obtain appropriate risk values. Therefore, this study aims to define metrics for an academic institution. The proposed metrics are obtained from The Family Educational Rights and Privacy Act (FERPA) regulation. It consists of directory information, educational information, personally identifiable information, and risk of information leakage. In order to achieve the objective, this study involves survey and reliability analysis to result in output. The survey is conducted by involving 90 respondents with various levels of education and jobs. The Cronbach's alpha and Test-retest are methods to determine this study's reliability. According to reliability analysis, the Cronbach's alpha method results in coefficients for the metrics between 0.730 - 0.911, while the Test-retest method results in coefficients between 0.630 - 0.797. These coefficients have a reliable category, so the proposed metrics are adequate for determining risk of information security incidents in academic environments. The reliable metrics will be developed as variables of the risk assessment model for the academic environment in the future study. 
Classification of Predicting Customer Ad Clicks Using Logistic Regression and k-Nearest Neighbors Dani, Yasi; Ginting, Maria Artanta
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1017

Abstract

Nowadays, conventional marketing techniques have changed to online (digital) marketing techniques requiring internet access. Online marketing techniques have many advantages, especially in terms of cost efficiency and fast information delivery to the public. Therefore, many companies are interested in online marketing and advertising on social media platforms and websites. However, one of the challenges for companies in online marketing is determining the right target consumers since if they target consumers who are not interested in buying the product, the advertising costs will be high. One use of online advertising is clicks on ads which is a marketing measurement of how many users click on the online ad. Thus, companies need a click prediction system to know the right target consumers. And different types of advertisers and search engines rely on modeling to predict ad clicks accurately. This paper constructs the customer ad clicks prediction model using the machine learning approach that becomes more sophisticated in effectively predicting the probability of a click. We propose two classification algorithms: the logistic regression (LR) classifier, which produces probabilistic outputs, and the k-nearest neighbors (k-NN) classifier, which produces non-probabilistic outputs. Furthermore, this study compares the two classification algorithms and determines the best algorithm based on their performance. We calculate the confusion matrix and several metrics: precision, recall, accuracy, F1-score, and AUC-ROC. The experiments show that the logistic regression algorithm performs best on a given dataset.
The Design of E-Commerce System to Increase Sales Productivity of Home Industry in Indonesia Nadiyasari Agitha; Ario Yudo Husodo; Royana Afwani; Faishal Mufied Al Anshary
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1589

Abstract

The household industry is the foundation of the existing home industry in Indonesia. It is categorized as a type of Small and Medium Enterprises (MSMEs) that significantly influence the Gross Domestic Product (GDP) ratio by up to approximately 10%. Since the pandemic of Covid-19, the home industry has increasingly stretched, especially in culinary and home craft products. The internet is one of the efforts to increase household industry sales. The household industry needs e-commerce to be a container for marketing its products. In this paper, we design an e-commerce system to support the sales productivity of the household industry in Indonesia. This study's e-commerce system or application is developed through some crucial stages. The stages are analysis through a questionnaire that represents needs in the field, selection of business models, namely B2B models with Virtual Storefront, marketplace concentrators, and lastly, Information Broker. Our infrastructure is determined for e-commerce development, and then strategy analysis is done using portfolio analysis, SWOT analysis, and competitor analysis. Based on the proposed strategy, we made a prototype as a designed e-commerce system that can increase sales for household businesses in cities in Indonesia. Important features in our proposed e-commerce system can accommodate many sellers or the household industry to establish relationships with many buyers based on geographical location, product search features based on closest positions or by city, and product categorization by familiar categories with household products. 

Page 1 of 4 | Total Record : 36

 1   2   3   4 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue