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JOIV : International Journal on Informatics Visualization
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
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 11 Documents
Search results for , issue "Vol 4, No 3 (2020)" : 11 Documents clear
Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions Wasswa Shafik; Mojtaba Matinkhah; Parisa Etemadinejad; Mammann Nur Sanda
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.376

Abstract

Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designing, a model implementation based on the bottlenecks currently assessed with an evaluation of the most fashionable hands-on utility of current methods for reinforcement learning.
Will Covid-19 cases in the World reach 4 million? a forecasting approach using SutteARIMA Ansari Saleh Ahmar; R. Rusli
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.389

Abstract

The objective of this study was to determine whether Covid-19 cases in the world would have reached 4 million cases with the SutteARIMA method forecasting approach. Data from this study were obtained from the Worldometer from 1 March 2020 to 05 May 2020. Data were used for data fitting from 1 March 2020 to 28 April 2020 (29 April 2020 – 05 May 2020). The data fitting is used to see the extent of the accuracy of the SutteARIMA method when predicting data. The MAPE method is used to see the level of data accuracy. Results of forecasting data for the period from 29 April 2020 to 05 May 2020: 72,731; 84,666; 92,297; 100,797; 84,312; 81,517; 74845. The accuracy of SutteARIMA for the period 30 April 2020 – 06 May 2020 shall be 0.069%. Forecast results for as many as 4 million cases, namely from 08 May 2020 to 10 May 2020: 3,966,786; 4,047,328 and 4,127,747. The SutteARIMA method predicts that 4 million cases of Covid-19 in the world will be reported on the WHO situation report on the day 110/111 or 09 May 2020/10 May 2020.
Applications of Fiber Optic Sensor for Monitoring and Early Warning of Soil Shift on IoT based System Helmi Septaria Herlin; - Harmadi; - Febrielviyanti; - Muldarisnur
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.3.1.219

Abstract

A fiber optic sensor system has been designed to monitor ground shift using a multimode FD-620-10 step index fiber optic cable equipped with an IoT-based data transmission system. The sensor system consists of a light source in the form of a diode laser, a fiber optic cable light wave guide, and an OPT101 photodetector. Data processing is done using the Arduino Uno R3 microcontroller and data transmission to the server using the Arduino Ethernet Shield. Localhost is a webserver that displays measurement data and sends e-mail early warning notification of ground shift hazards. Optical fiber is used to measure the value of ground displacement by utilizing changes in output voltage. The change in output voltage occurs due to variations in the change in the diameter of the optical fiber arch. The results of sensor testing obtained an average measurement error of 1.53%.
Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud Istabraq M. Al-Joboury; Emad H. Al-Hemiary
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.374

Abstract

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.
Designing a Mobile Game Application for Student with Learning Disabilities Mohamad S.N.M; Nor Azmidy I; Imam B. M. K; Che Ku Nuraini C.K.M
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.404

Abstract

Learning disabilities are a problem that influences the brain's capacity to receive, process, analyze and store information. These processing issues can interfere with learning fundamental abilities, especially for learning mathematics. Fortunately, different approaches and methods in teaching and learning can improve students with learning disabilities to understand and know to count basic mathematic operations. This study focuses on the designing of mobile game applications based on the Speedline method to help students with learning disabilities to understand basic mathematical operational especially the addition and subtraction problem. Based on the findings, Speedline game was design using the puzzle game concept and the game design is fully discussed in this paper.
Security Architecture for Low Resource Devices in Smart City using Cloud Muneer Ahmad Dar
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.407

Abstract

The world is moving towards modernization with the help of smart devices used in smart cities to make the whole lot intelligent and smart. These smart devices are extensively used in smart city and are capable of doing everything which one can do with the normal desktop computer. These smart devices like Smartphone have computational limitations are not able to store a large data to be used and collected in a smart city. In this paper, we propose a novel security architecture which first uses the Elliptic Curve Diffie Helman Key Exchange Algorithms to exchange the keys between the two low power devices (Smartphone). The keys are used to encrypt the large data (images and videos etc). The data is encrypted using the private keys of a device and then send to the cloud for safe storage. The data can be only accessed by the communicating device with the same key. The proposed security architecture enables these smart devices to store the huge data collected from the smart city to store on the cloud. If another device requests the same set of data, the keys can be shared secretly and the communicating device can be allowed to download the data directly from the cloud. This architecture relieves the Smartphone from the storage limitation and also enables it to communicate with faster speed and securely.   
Press “A” for Artificial Intelligence in Agriculture: A Review Yogesh Awasthi
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.387

Abstract

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.
Affine Shape Comparison using Different Distances Khalid Aznag; Toufik Datsi; Ahmed El Oirrak; Essaid El Bachari
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.388

Abstract

In this work, we propose to compare affine shape using Hausdorff distance (HD), Dynamic Time Warping (DTW), Frechet (DF), and Earth Mover distance (EMD). Where there is only a change in resolution shape distance are computed between shape coordinates because the distance is not invariant under rotation or affinity. In case of transformation, distances are calculated not between shape coordinates but between Arc length or Affine Arc length. Arc length is invariant under rotation while Affine Arc length is invariant under affinity. The main advantage is invariance under change of resolution, rotation, and affinity.
Combining Invisible Unicode Characters To Hide Information In A Text Document N.R. Zaynalov; U.Kh. Narzullaev; A.N. Muhamadiev; I.R. Rahmatullaev; R.K. Buranov
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.406

Abstract

Steganography develops tools and methods for hiding the fact of message transmission. The first traces of steganographic methods are lost in ancient times. For example, there is a known method of hiding a written message: the slave's head was shaved, a message was written on the scalp, and after the hair grew back, the slave was sent to the addressee. From detective works, various methods of secret writing between the lines of ordinary text are well known: from milk to complex chemical reagents with subsequent processing. Digital steganography is based on hiding or embedding additional information in digital objects while causing some distortion of these objects. In this case, text, images, audio, video, network packets, and so on can be used as objects or containers. To embed a secret message, steganographic methods rely on redundant container information or properties that the human perception system cannot distinguish. Recently, there has been a lot of research in the field of hiding information in a text container, since many organizations widely use text documents. Based on this, here the MS Word document is considered as a medium of information. MS Word documents have different parameters, and by changing these parameters or properties, you can achieve data embedding. In the same article, we present steganography using invisible Unicode characters of the Space type, but with a different encoding.
Feature Selection Techniques for Selecting Proteins that Influence Mouse Down Syndrome Using Genetic Algorithms and Random Forests Fiqhri Mulianda Putra; Fadhlal Khaliq Surado; Global Ilham Sampurno
JOIV : International Journal on Informatics Visualization Vol 4, No 3 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.3.375

Abstract

Feature selection technique is a technique to reduce data dimensions which are widely used to find the set of features that best represent data. One area of science that often applies this technique is bioinformatics. An example of its application is the selection of significant proteins in the case of Down syndrome. To find out the most influential protein, experiments were carried out on normal mice with trisomy rats (down syndrome mice) totaling 1080 sample and obtained 77 levels of protein expression. The analysis carried out was divided into three groups. Each group was searched for the most influential proteins using genetic algorithms with fitness calculations using random forest algorithms. The results of the protein selection of the three data groups indicate the relationship of the selected proteins to the improvement of learning ability and memory. The results of evaluating selected protein models show a high degree of accuracy, which is above 98.7% for each data group.

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