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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 26 Documents
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Search results for , issue "Vol 6, No 4 (2022)" : 26 Documents clear
Iris Image Watermarking Technique for Security and Manipulation Reveal Thabit, Rasha; Shukr, Saad M.
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1287

Abstract

Providing security while storing or sharing iris images has been considered as an interesting research topic and accordingly different iris image watermarking techniques have been presented. Most of the available techniques have been presented to ensure the attachment of the secret data to their related iris images or to hide a logo which can be used for copyright purposes. The previous security techniques can successfully meet their aims; however, they cannot reveal the manipulations in the iris region. This paper presents an iris image watermarking technique that can provide security and reveal manipulations in the iris region. At the sender side, the proposed technique divides the image into two regions (i.e., iris region and non-iris region) and generates the manipulation reveal data from the iris region then embeds it in the non-iris region. At the receiver side, the secret data is extracted from the non-iris region and compared with calculated data from the iris region to reveal manipulations if exist. Different experiments have been conducted to evaluate the performance of the proposed technique which proved its efficiency not only in providing security but also in revealing any manipulations in the iris region.
Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis Abadi, Aji Bijaksana; Tahcfulloh, Syahfrizal
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1013

Abstract

Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.
Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation Erfianto, Bayu; Rahmatsyah, Andrian
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.889

Abstract

Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.
A Microarray Data Pre-processing Method for Cancer Classification Hui, Tay Xin; Kasim, Shahreen; Md Fudzee, Mohd Farhan; Abdullah, Zubaile; Hassan, Rohayanti; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1523

Abstract

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.
Smartphone-based Indoor Navigation for Guidance in Finding Location Buildings Using Measured WiFi-RSSI Triyono, Liliek; Prayitno, -; Rahaman, Mosiur; Sukamto, -; Yobioktabera, Amran
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1528

Abstract

This study investigates a Wi-Fi-based indoor navigation system to determine building locations. The system was developed using the fingerprint method from the Received Signal Strength Indication (RSSI) of each Access Point (AP). The main components of a smartphone-based system use data from Wi-Fi and the Global Positioning System (GPS). The system developed for navigation is designed and implemented as an element of a dynamic, seamless mobility planning and building location route guidance application. Building map data is collected from Google Map data and enhanced by coloring the geographic location of buildings displayed on mobile devices. Navigational aids collected from sensors provide trip orientation and position updates. The approach of measuring the distance between known positions is compared to those displayed in the application with the haversine formula to measure the accuracy of the position displayed. A series of experiments were conducted in the Politeknik Negeri Semarang area, Indonesia. The experiment results showed that the Wi-Fi-based indoor positioning system was accurate within 7.050 meters of the error for that location, thus proving the system's usefulness for determining the location of buildings in the campus area. The measurement has not adopted the maximum APs placement for signal coverage and strength, only using the existing APs positions. The temperature nor humidity was neither measured in each area where the AP was installed, which is discussed later. This system can help visitors without asking, even though they have only visited once.
Facial Expression Recognition Using Convolutional Neural Network with Attention Module Khoirullah, Habib Bahari; Yudistira, Novanto; Bachtiar, Fitra Abdurrachman
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.963

Abstract

Human Activity Recognition (HAR) is an introduction to human activities that refer to the movements performed by an individual on specific body parts. One branch of HAR is human emotion. Facial emotion is vital in human communication to help convey emotional states and intentions. Facial Expression Recognition (FER) is crucial to understanding how humans communicate. Misinterpreting Facial Expressions can lead to misunderstanding and difficulty reaching a common ground. Deep Learning can help in recognizing these facial expressions. To improve the probation of Facial Expressions Recognition, we propose ResNet attached with an Attention module to push the performance forward. This approach performs better than the standalone ResNet because the localization and sampling grid allows the model to learn how to perform spatial transformations on the input image. Consequently, it improves the model's geometric invariance and picks up the features of the expressions from the human face, resulting in better classification results. This study proves the proposed method with attention is better than without, with a test accuracy of 0.7789 on the FER dataset and 0.8327 on the FER+ dataset. It concludes that the Attention module is essential in recognizing Facial Expressions using a Convolutional Neural Network (CNN). Advice for further research first, add more datasets besides FER and FER+, and second, add a Scheduler to decrease the learning rate during the training data.
Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning Sudi Mariyanto Al Sasongko; Erni Dwi Jayanti; Suthami Ariessaputra
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1532

Abstract

Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.
A Multimodal Model of ECG and Heart Sound Signal by Considering Normal and Abnormal Heart Oktivasari, Prihatin; Haryanto, Freddy; Suprijadi, -
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1220

Abstract

Analysis of the opening and closing of heart valves and the movement of blood flow in the heart are important in the domain of early detection of heart conditions. To build this correlation model, multimodal signals from electrocardiography and stethoscope are needed. Multimodal signaling was performed using primary data with the same sampling at 10 seconds by recording the PQRST heart signal in the lying position using electrocardiography and the heart sound in the sitting position using an electronic stethoscope. Experimental results showed that the number of R peaks is the same as the number of S1 sound peaks, and also the number of T peaks with the number of S2 sound peaks, so it can be concluded that there is a regular signal pattern relationship between S1-S2 and the RT wave, namely the relationship at the end of the first peak of the QRS wave. The cardiac signal due to ventricular depolarization (ventricular contraction), the appearance of an S1 heart sound, and the association of the end of the next peak of the T wave of cardiac signals indicate ventricular repolarization and the appearance of an S2 heart sound. This is consistent with the fact that electrical events in cardiac activity occur before mechanical events in normal heart conditions. Based on the study of HRV parameters, heart sound signals can be used to determine HRV parameters. The results show the same number of peaks in normal hearts, while in abnormal hearts, there are differences in results because abnormal heart conditions have an erratic rhythmic pattern.
Aircraft Flight Movement Anomaly Detection using Automatic Dependent Surveillance-Broadcast Ajhari, Abdul Azzam; Negara, I Gede Putra Kusuma
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.948

Abstract

Automatic Dependent Surveillance-Broadcast (ADS-B) is an aircraft backup radar device that transmits aircraft sensor information via radio frequency. This data can be used to detect aircraft changes that occur significantly or abnormally (anomaly). Anomaly detection in this study aims to reduce and prevent flight accidents by analyzing abnormal data on aircraft flights using the Deep Learning (DL) model. Bidirectional LSTM (Bi-LSTM) and Bidirectional GRU (Bi-GRU) models are proposed as DL models which are implemented on ADS-B data using data mining methods. The data is generated from the ADS-B device that records the plane crash incident and is stored on the Flightradar24 community server. Data containing sensor changes from anomalous aircraft movements are studied for predictability on other flight data. The class breakdown is divided into two, anomaly and normal, based on information on the time span of anomaly occurrences in the accident investigation report of each aircraft using the sliding window technique in the data mining methodology. In evaluation, the confusion matrix measurement method is used to predict predictive analysis of the tested data. The results of the model evaluation performance show that the Bi-LSTM proposed in this study has the best accuracy of 99.44% and the f1-score of 99.51% is slightly superior to the Bi-GRU model. The model in this study can be applied in the ADS-B device to detect aircraft movement anomalies and as material for reviewing technicians in periodic maintenance and measuring the accuracy of the ADS-B device used as a backup radar.
A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment Hassan, Mustafa Hamid; Mostafa, Salama A.; Baharum, Zirawani; Mustapha, Aida; Saringat, Mohd Zainuri; Afyenni, Rita
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1228

Abstract

The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy.

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