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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.
Arjuna Subject : -
Articles 1,172 Documents
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Feature-reduction Fuzzy c-means Clustering for Basketball Players Positioning Nataliani, Yessica
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.651

Abstract

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of players’ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball players’ position. The result shows that FRFCM can be applied to determine the basketball players’ position, where the most related physical feature is the player’s height. FRFCM gets one incorrect player’s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect player’s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new player’s position.
The Effectiveness of a Virtual Reality Marketing Video on the People Desire to Buy a Product Wijayanto, Sigit; Pratama Putra, Jouvan Chandra
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.483

Abstract

Virtual Reality technology can provide new experiences and different points of view of activities, events, or products for the users. In line with advances from VR technology, YouTube initiates to support the spread of VR videos by creating a VR feature on their platform. A hundred videos about a dangerous activity, Horror activity, and Marketing video of software or a movie product are found on the YouTube platform. Meanwhile, it is still not yet known how the effectiveness of an advertisement using VR video via the YouTube platform on the people desires to buy a product, especially in Indonesia, which then became the purpose of this study. In carrying out this study, a quantitative study was used by creating a digital questionnaire and distributed it with Google Forms. Then the data obtained will be processed by the respondent demographics and the 4 types of analysis, such as the Validity analysis, the Reliability analysis, the Ranking of VR applications on product promotions, and the Correlation analysis. Afterward, the study found that the B1 and B2 variables refer to Advertising, making it easy for us to understand the product has the most correlation coefficient. Moreover, 80% of the respondents stated that they like the VR advertisement product. It means that people are interested in trying and feel something new in the way VR technology is given to them. Ultimately, the respondents agree that VR advertising has informed them well about the product.
A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations Alwaisi, Shaimaa Safaa Ahmed; Abbood, Maan Nawaf; Jalil, Luma Fayeq; Kasim, Shahreen; Mohd Fudzee, Mohd Farhan; Hadi, Ronal; Ismail, Mohd Arfian
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.737

Abstract

The amount of data in our world has been rapidly keep growing from time to time.  In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
Mobile Application for Incident Reporting Ignaco, Mary Ann E.
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.741

Abstract

In the Philippines, reporting an incident always depends on self-reporting to the nearest law enforcer's office or calling a channel using a mobile phone. 911 is the National Emergency hotline to get assistance when an emergency occurs. However, the emergency hotline operated by the Emergency Network Philippines (ENP), cannot retrieve the reporter's location details immediately. Only when the reporters describe the exact location clearly. Yet, many circumstances that the reporters do not know when they are, or sometimes they have imprecise position information. Then, the law enforcers team may not be able to come to the right place efficiently on time.  The incident reporting application incorporates the three types of incidents, classified as public disturbance, ordinance violation, and crime incident. To report an incident the application will automatically get the latitude and longitude of the mobile user or an option to manually pinned the location on the google map include also the incident type, description, and photos will be sent to the nearest barangay responder officer. The barangay responder officer able to request a backup officer, the rescue emergency unit such as a hospital ambulance or firefighters, or transfer a report to the nearest police station. The system also manages web admin for responder locations and generates statistical reports including charts and graphs.  The positive feedback of the participants during the evaluation stage signifies that the application was accepted as tested and verified by the evaluation results.
A Portable Device of Air Pollution Measurement Due to Highway Exhaust Emissions Using LabVIEW Programming - Andrizal; - Lifwarda; Anna Yudanur; Rivanol Chadry; - Hendrick
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.697

Abstract

A multisensory gas device integrated with myRIO module to measure air pollution has been established. This device is programmed using the LabVIEW programming language and can measure CO2, CO, NOX, and HC pollution on roads due to motor vehicle exhaust emissions. The device and the display system are made separately using wireless network communication to make this tool portable. Exhaust Gas Analyzer (EGA) was chosen for device calibration, obtaining 3.62% on the average error after performing 30 tests. The tests for measuring CO, CO2, NOX, and HC gas levels were conducted in several locations in Padang City and performed in the morning, afternoon, and evening. The result showed that the system properly measured CO2, CO, NOX and HC pollution in parks and highways in real-time in parts per million (ppm). It also displayed varied gas measurement results in terms of time and test location with a range of CO gas values at 0.034 – 0.15 ppm, CO2 151.3 – 815.2 ppm, NOX 0.0001 – 0.004 ppm, and HC 0.04 – 0.65 ppm. In addition, the system could perform well in providing warnings by automatically activating the air indicator alert at several measurement places when the gas content on one of the gas elements and compounds at a particular location has exceeded the threshold for the clean air category. Thus, this device can be used as initial research to build a real-time air pollution measurement system using the Internet of Things (IoT).
Virtual Campus Tour Application through Markerless Augmented Reality Approach Liang, Ang Wei; Wahid, Noorhaniza; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.743

Abstract

Augmented Reality (AR) technology has been widely used on campus tours by universities all around the world. However, the students that stay very far away do not have a chance to visit around the campus. Also, the information that is available on the official website is static, resulting in the visitors feeling less engaged with the information. Hence, the virtual campus tour application using the markerless AR technology, namely AR-UTHM Tour is proposed to be developed on the Android mobile-based platform to visualize the buildings and facilities that are available in the university, specifically Universiti Tun Hussein Onn Malaysia (UTHM). This approach allows the users to visualize the 3D models by pointing the camera at any flat surface. Then, the feature point will be generated to generate a virtual plane. The information about the facilities was obtained from the UTHM official website and the 3D models of the buildings were referred to the floor plan and the actual images. The user acceptance test has been conducted on 30 students of UTHM using Technology Acceptance Model (TAM). The result shows that more than 50% of the respondents have successfully executed the AR session without any error. Overall results show that the users are satisfied with the AR-UTHM Tour application. In conclusion, this application is suitable to be used as a medium to introduce and promote UTHM virtually. Future improvements in terms of detailing the aesthetic of the 3D model will be taken into consideration.
Simultaneous Hydroponic Nutrient Control Automation System Based on Internet of Things Adidrana, Demi; Iskandar, Ade Rahmat; Nurhayati, Ade; Suyatno, -; Ramdhani, Mohamad; Adam, Kharisma Bani; Ardianto, Rizki; Ekaputri, Cahyantari
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.865

Abstract

Hydroponic is one of the solutions of gardening methods using water as a nutrition medium. Usually, maintaining hydroponic plant quality and water nutrients are done manually and require human efforts, such as the degree of acidity or wetness (pH), TDS (Total Dissolved Solids), and nutrient temperature. With the Internet of Things technology, we can automate hydroponic control by measuring the nutrients' TDS, pH, and temperature values and controlling water nutrition by pump nutrition needs for hydroponic plants. This research uses the NFT (Nutrient Film Technique) for the hydroponic system and uses lettuce as the nutrition parameter. The lettuce parameters are pH, TDS, and Water Temperature equal to the sensor we used in the proposed IoT system. The condition has 27 classifications, and we use this classification as a reference in decision-making, using the K-Nearest Neighbor (KNN) algorithm to activate the actuator. We improve the simultaneous actuator from previous research with specified intervals and duration to achieve ideal nutritional conditions. The other improvement is that we collect more data and more testing times. The accuracy was 91.2%, with k = 3. From the evaluation results, the accuracy of KNN is quite high and has an advantage, which has better accuracy than the other algorithms and can activate actuator simultaneously. We conclude that the hydroponic nutrient automation system using the Internet of Things method is ready for real planting use with this improvement.
Design on Novel Door Lock Using Minimizing Physical Exposure and Fingerprint Recognition Technology Jeong, Seungdo
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.858

Abstract

Digital door locks are widely used not only in general homes such as houses and apartments, but also in spaces where external intrusion must be prevented based on high security and convenience. Recently, smart door locks with additional technologies such as fingerprint recognition and Bluetooth communication have also been developed, and the door lock market is on the rise. Digital door locks are more convenient to use compared to the existing key-type door locks. However, there are often cases of exploiting security vulnerabilities such as exploiting and invading the user's trace remaining on the door lock. This paper proposes a door lock with a structure that can complement the shape of the current door lock exposed to the outside and minimize the user's fingerprint trace. In addition, a method of reinforcing security is applied using fingerprint recognition through image processing and a random pattern number arrangement. An experiment was conducted to confirm whether the door lock of this type was actually usable, and the recognition of partially damaged fingerprints was also confirmed. It was shown that the door lock structure proposed in this paper can maximize security by combining fingerprint recognition technology and random pattern numbering while minimizing external exposure.
Investigation of RGB to HSI Conversion Methods for Early Plant Disease Detection Using Hierarchical Synthesis Convolutional Neural Networks Raseeda Hamzah; Khyrina Airin Fariza Abu Samah; Muhammad Faiz Abdullah; Sharifalillah Nordin
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.852

Abstract

An early detection of disease can save the plant. One of the ways is by using eye-observation, which is time-consuming. Having a machine learning technology that can automate early detection would benefit modern and conventional farming. This study emphasizes the review of Hyperspectral Image (HSI) reconstruction using the Hierarchical Synthesis Convolutional Neural Networks (HSCNN) based method in early plant disease detection. Capturing hundreds of spectral bands during image acquisition enables the HSI capturing devices to provide more detailed information. Detection of disease with Red Green Blue (RGB) images needs to be done when it shows a notable spot or sign. However, the disease can be spotted with the correct range of spectral bands on HSI before a notable spot or sign is shown. The usage of HSI image is significantly important as it is rich in information and properties needed for image detection. Although HSI device is significantly important in early plant disease detection, the devices are expensive and require specialized hardware and expertise. Thus, reconstructing the Reg Green Blue (RGB) image to HSI is required. This research implemented two types of HSCNN-based methods, Densed network (HSCNN-D) and Rectified Linear Unit network (HSCNN-R), for HSI reconstructions. The results show that HSCNN-D outperformed the HSCNN-R with less Mean Relative Absolute Error (MRAE) of 2.15%.
Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia Rudiastuti, Aninda W.; Lumban-Gaol, Yustisi; Silalahi, Florence E. S.; Prihanto, Yosef; Pranowo, Widodo S.
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.873

Abstract

Measuring the status of achievement of the SDGs is the task and concern of many countries in the world, including Indonesia. Indicators for achieving the SDGs enclose three main pillars, namely environmental, economic, and social. The updated land use/land cover information is needed for environmental pillars. One imperative land cover information is built-up land, which acts as a detector for expanding urban areas and measuring SDGs' target indicators. Indonesia's cultural diversity affects the distribution pattern of built-up land, especially settlements. This is a challenge in the up-to-date and rapid mapping of built-up land. This research aims to analyze the ability and transferability of the Random Forest model for built-up areas and settlements using Google Earth Engine (GEE) in Banyumas, Cilacap, and Tegal. Around 19 predictors from multi-sources satellites are integrated to identify four land cover classes. Discussion on predictor composition to improve model accuracy also carried on. The results showed that the algorithm separated four land cover classes, with the highest accuracy for separating water bodies and other classes (vegetation and open land), OA above 90%. Machine confusion regarding the separation between housing classes and other buildings was still found (F1 score 0.67 - 0.69). Applying the model to the other two areas resulted in a similar statistical trend to the trained model. However, the classification method developed in this paper can assist in the rapid description of land cover if up-to-date data from official sources are not available.

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