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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 1,172 Documents
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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.
Entropy Based Method for Malicious File Detection Edzuan Zainodin, Muhammad; Zakaria, Zalmiyah; Hassan, Rohayanti; Abdullah, Zubaile
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.6.4.1265

Abstract

Ransomware is by no means a recent invention, having existed as far back as 1989, yet it still poses a real threat in the 21st century. Given the increasing number of computer users in recent years, this threat will only continue to grow, affecting more victims as well as increasing the losses incurred towards the people and organizations impacted in a successful attack. In most cases, the only remaining courses of action open to victims of such attacks were the following: either pay the ransom or lose their data. One commonly shared behavior by all crypto ransomware strains is that there will be attempts to encrypt the victims’ files at a certain point during the ransomware execution. This paper demonstrates a technique that can identify when these encrypted files are being generated and is independent of the strain of the ransomware. Previous research has highlighted the difficulty in differentiating between compressed and encrypted files using Shannon entropy, as both file types exhibit similar values. Among the experiments described in this study, one showed a unique characteristic for the Shannon entropy of encrypted file header fragments, which was used to differentiate between encrypted files and other high entropy files such as archives. The Shannon entropy of encrypted file header fragments has a unique characteristic in one of the tests discussed in this study. This property was used to distinguish encrypted files from other files with high entropy, such as archives. To overcome this drawback, this study proposed an approach for test case generation by enhancing the entropy-based threat tree model, which would improve malicious file identification. The file identification was enhanced by combining three entropy algorithms, and the test case was generated based on the threat tree model. This approach was then evaluated using accuracy measurements: True Positive, True Negative, False Positive, False Negative. A promising result is expected. This method solves the challenge of leveraging file entropy to distinguish compressed and archived files from ransomware-encrypted files in a timely manner.
The Small UWB Monopole Antenna with Stable Omnidirectional Radiation Pattern Firdaus, -; Yuhanef, Afrizal; Yulindon, Yulindon; Meidelfi, Dwiny; Silvana, Meza
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.6.4.972

Abstract

Ultra Wideband (UWB) technology is an unmodulated wireless digital communication system that uses an extremely short duration pulse to transmit information bit. Because of this pulse, the UWB system needs a very wide bandwidth. Federal Communication Commission (FCC) has regulated the 3.1 – 10.6 GHz frequency spectrum for UWB. Since FCC released this frequency, many research in telecommunication have been done on UWB systems. One of them is a development of an Antenna that is suitable for UWB devices. UWB antenna characteristics require FCC band, omnidirectional radiation pattern, and compact size. In order to meet these needs, an antenna with a simple structure in the form of a monopole patch antenna with a similar patch size and ground width has been designed. The antenna is built on an FR4 – epoxy substrate material, with 4.4 dielectric constant and 1.6 mm thickness. The antenna feeding structure consists of two 100 Ω and 50 Ω lines with a wideband impedance matching scheme using tapered side and tapered transformers. The antenna design and optimization processes are conducted using electromagnetic simulation software, and measurements are carried out in an anechoic chamber. Simulation and measurement results show good agreement, and the antenna can work at frequencies 3.5 - 11.3 GHz with a gain of 1.5 – 3.25 dBi and stable omnidirectional radiation patterns. The antenna has dimensions of 27 × 8 × 1.6 mm, which are smaller than the antenna reported in the last research and suitable to be applied on various UWB devices.
The Utilization of Augmented Reality Technology for the Development of Tourism Information Media Maru, Rosmini; Nur, Ali Rahmat Muhammadiyah; Yusuf, Muhammad; Nyompa, Sukri; Rusdi, -
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.6.4.1396

Abstract

The industrial revolution 4.0 has brought tourism trends using enriched interactive technology services. Technology in the world of tourism has helped the expansion of the industry through the promotion and mediation of independent tourists to carry out all services and transactions easily, including obtaining tourist information. This study aims to develop promotion media for geography education tourism in Soppeng Regency and test the effectiveness of the media. This study adopted the Alessi & Trollip learning multimedia development model. The developed media was tested on 20 respondents with 10-item usability scale statements and 5 response options ranging from agreeing to strongly disagree. The level of effectiveness of the media developed after being tested using the usability scale (SUS) system is included in the Good category (good) with an average score of 76.5. This level of effectiveness is included in the acceptable to users or acceptable category, with a grade scale of C and an adjective rating in the Good or good category. The results of this study also indicate that users are satisfied with the developed media but are not enthusiastic so that users can switch to other, more interesting media at any time. In the end, based on the final score obtained, it was concluded that the promotion media for geography education tourism in Soppeng Regency (battle city application) was effective and acceptable to users. 
Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution Miptahudin, Apip; Suryani, Titiek; Wirawan, Wirawan
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.6.4.1530

Abstract

Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions.

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