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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 13 Documents
Search results for , issue "Vol 3, No 4 (2019)" : 13 Documents clear
Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks Mehdi Safari; Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.726 KB) | DOI: 10.30630/joiv.3.4.254

Abstract

Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well-known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED). The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.
Big Data Environment for Realtime Earthquake Data Acquisition and Visualization Louis Nashih Uluwan Arif; Ali Ridho Barakbah; Amang Sudarsono; Renovita Edelani
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3512.439 KB) | DOI: 10.30630/joiv.3.4.320

Abstract

Indonesia is a country that has the highest level of earthquake risk in the world. In the past 10 years, there have been ± 90,000 earthquake events recorded and always increasing along with the explosion of earthquake data occurs at any time. The process of collecting and analyzing earthquake data requires more effort and takes a long computational time. In this paper, we propose a new system to acquire, store, manage and process earthquake data in Indonesia in real-time, fast and dynamic by utilizing features in the Big Data Environment. This system improves computational performance in the process of managing and analyzing earthquake data in Indonesia by combining and integrating earthquake data from several providers to form a complete unity of earthquake data. An additional function is the existence of an API (Application Programming Interface) embedded in this system to provide access to the results of earthquake data analysis such as density, probability density function and seismic data association between provinces in Indonesia. The process in this system has been carried out in parallel and improved computing performance. This is evidenced by the computational time in the preprocessing process on a single-core master node, which requires 55.6 minutes, but a distributed computing process using 15 cores can speeds up with only 4.82 minutes.
Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia Renovita Edelani; Ali Ridho Barakbah; Tri Harsono; Louis Nashih Uluwan Arif
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1760.156 KB) | DOI: 10.30630/joiv.3.4.319

Abstract

Indonesia is one of the largest archipelagic countries in the world that has the highest risk of an earthquake. The major causes of earthquakes in this country are plate movements and volcanic activity. Earthquakes in Indonesia has a cause and effect relationship between each province. This disaster caused severe damage including a lot of people to get killed, injured and lose their money and property. We must minimize the impact of the earthquake by forming earthquake risk mapping. The risk of seismicity in Indonesia can vary each year, so it needs to be analyzed how the changes in risk are each addition of earthquake data. This paper proposes an earthquake risk mapping system with Associative Mining based on incremental earthquake data that have the highest values of confidence rates from the seismic association between provinces in Indonesia. The system uses the Incremental Association rule method to see the trend in the value of changes in confidence for each addition of earthquake data every 5 years. This system proposes 3 main features, which are (1) Data Retrieval and Preprocessing, (2) Association Rule Mining, (3) Incremental Associative Mining based risk mapping. For the experimental study, the system used data from 1963-2018. The results show that the provinces of Maluku, North Maluku, Nusa Tenggara Timur, North Sulawesi, and Papua have an incremental association risk of an earthquake.
Evaluation of Visual Based Augmented Reality (AR) Learning Application (V-ARA-Dculia) for Dyscalculia Learners Kohilah Miundy; Halimah Badioze Zaman; Aliimran Nosrdin; Kher Hui Ng
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (959.762 KB) | DOI: 10.30630/joiv.3.4.321

Abstract

The rapid growth in technology have affected processes in various domains such as business, healthcare, agriculture and education. Computer related applications used in these domains are available so easily, that it is impossible to imagine a situation without them. Technologies that were available but hardly commonly used a few decades ago such as Virtual Reality (VR) and Augmented Reality (AR) have now become technologies that are fast gaining interests in most fields including service related1 fields such as healthcare and education. The basic idea of AR is to superimpose sense enhancements over a real-world environment. It is a perfect solution for learners with learning difficulties as it combines the advantages of multi senses of the learners, helps them to understand learning better when the integration of both virtuality and reality is embedded in their learning applications. AR is mostly effectively used when computer generated visual enhancements are integrated into real life applications. Thus, this paper highlights the evaluation of the visual-based AR learning application to investigate its plausible assistive functions that can help dyscalculia learners learn Mathematics in a more meaningful way. Findings of the study showed that the students who had difficulties on memory, abstraction, sequencing processing, motor and visual perception, found the visual-based Augmented Reality (AR)  technology embedded in an application, a positive assistive learning application that can help dyscalculia learners learn mathematics more effectively.   
Efficient processing of GRU based on word embedding for text classification Muhammad Zulqarnain; Rozaida Ghazali; Muhammad Ghulam Ghouse; Muhammad Faheem Mushtaq
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1066.464 KB) | DOI: 10.30630/joiv.3.4.289

Abstract

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM, the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate.
Development of Inventory Information System Using Enterprise Architecture Planning Method Muhammad Sobri; Poppy Indriani; Mohamad Taha Ijab; Isnawijayani Isnawijayani; Marlindawati Marlindawati
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1129.938 KB) | DOI: 10.30630/joiv.3.4.228

Abstract

Universitas Bina Darma is a university that has a vision to become an international standard university driven by innovations in Information and Communication Technology (ICT). The aim of this research is to support the vision of Universitas Bina Darma by creating an inventory information system for the university-wide implementation. The research uses Enterprise Architecture Planning (EAP) which defines the data architecture, application architecture, technology architecture, and its implementation. EAP consists of four levels, where by the top level is planning initiation, the second level consists of two stages namely business modeling, and current systems and technology, the third level consists of three stages of data architecture, application architecture and technology architecture. Lastly, the bottom level is implementation or migration plans. The results of this research is in the form of the Inventory Information Systems that has several menus, among others: procurement menu, maintenance menu, move transfer menu, space menu, loan space menu, asset menu and request menu. This Inventory Information System facilitates the faster processing of inventory data so as to produce quality report  that is timely available when needed by the leadership, as well as facilitates the supervision of existing inventory.
Blockchain and Cryptocurrencies Technology: a survey Bashar Ibrahim Hameed
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1321.522 KB) | DOI: 10.30630/joiv.3.4.293

Abstract

Blockchain  and Cryptocurrency has gotten wider considerations as of late. The decentralized digital Cryptocurrency  and its underlying “Blockchain ” technology has created much excitement in the technology community. The financial technology sector sees high potential value in Cryptocurrency Blockchain  protocols, or distributed-ledger technology. The key advantage of this technology lies in the fact that it enables the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources. In this paper, we presented a systematic investigation of Blockchain  and Cryptocurrencies with explained simply in a way that Cryptocurrency is a form of digital currency that is being used to make transactions using a ledger known as Blockchain  which is a decentralized system of banking in which there is no centralized authority and all the control lies on an algorithm and its controlling users. Blockchain , a financial tool that can potentially play an important role in the sustainable development of the global economy. The new technology is expected to bring massive benefits to consumers, to current banking system and to the whole society in general. 
Improving the security of LSB image steganography Jamil Al-Azzeh; Ziad Alqadi; Belal Ayyoub; Ahmad Sharadqh
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1080.235 KB) | DOI: 10.30630/joiv.3.4.233

Abstract

Steganography is the technique of hiding secret data (message) within any media such as digital color image. In this paper we will merge steganography process with cryptography process in order to increase the security of the proposed method. The steganography process will based on LSB method, while the cryptography process will based on generating a huge private key and selecting a special function for encryption decryption. The proposed method will be implemented in order to calculate some performance parameters to prove the efficiency of the proposed method.
Breast Cancer Prediction Using a Hybrid Data Mining Model Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (972.825 KB) | DOI: 10.30630/joiv.3.4.240

Abstract

Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.
Tag Clouds for Software Documents Visualization Ra'fat Ahmad Al-msie'deen
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (978.943 KB) | DOI: 10.30630/joiv.3.4.285

Abstract

Legacy software documents are hard to understand and visualize. The tag cloud technique helps software developers to visualize the contents of software documents. A tag cloud is a well-known and simple visualization technique. This paper proposes a new method to visualize software documents, using a tag cloud. In this paper, tags visualize in the cloud based on their frequency in an alphabetical order. The most important tags are displayed with a larger font size. The originality of this method is that it visualizes the contents of JavaDoc as a tag cloud. To validate the JavaDocCloud method, it was applied to NanoXML case study, the results of these experiments display the most common and uncommon tags used in the software documents.

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