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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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Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery Mohd Yazid Abu Sari; Yana Mazwin Mohmad Hassim; Rahmat Hidayat; Asmala Ahmad
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.5.4.742

Abstract

An effective crop management practice is very important to the sustenance of crop production. With the emergence of Industrial Revolution 4.0 (IR 4.0), precision farming has become the key element in modern agriculture to help farmers in maintaining the sustainability of crop production. Unmanned aerial vehicle (UAV) also known as drone was widely used in agriculture as one of the potential technologies to collect the data and monitor the crop condition. Managing and monitoring the paddy field especially at the bigger scale is one of the biggest challenges for farmers. Traditionally, the paddy field and crop condition are only monitored and observed manually by the farmers which may sometimes lead to inaccurate observation of the plot due the large area. Therefore, this study proposes the application of unmanned aerial vehicles and RGB imagery for monitoring rice crop development and paddy field condition. The integration of UAV with RGB digital camera were used to collect the data in the paddy field. Result shows that the early monitoring of rice crops is important to identify the crop condition. Therefore, with the use of aerial imagery analysis from UAV, it can help to improve rice crop management and eventually is expected to increase rice crop production.
A Packet Delay Assessment Model in the Data Link Layer of the LTE Ulugbek Amirsaidov; Azamat Qodirov
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.5.4.601

Abstract

The issues of modeling and evaluating the characteristics of the LTE data link layer functioning are considered. Transmitting packets in the data link layer are represented by a probabilistic-temporal graph consisting of two subgraphs. The first subgraph describes the operation of the HARQ protocol, and the second subgraph describes the operation of the ARQ protocol. The first subgraph is nested within the second subgraph. The probabilities of correct reception, non-error detection, and retransmission of packets in the MAC and RLC layers and generating functions of the packet service time based on the HARQ and ARQ protocols are determined. With the help of generating functions, the average value, variance, and coefficient of variation of the packet service time are determined. To calculate the average packet delay time in the LTE data link layer, the type of queuing system is selected, taking into account the coefficient of variation of the packet service time. The analysis of packets' delay time in the network's data link layer is carried out for different values of the intensity of packet arrival and the probabilities of a bit error in the physical layer of the network. For the sustainable functioning of the data link layer of the network, the limit values of the intensity of the arrival of packets are determined for a given probability of a bit error in the physical layer of the network.
An Efficient Approach for Uncertain Event Detection in RFID Complex Event Processing Siti Salwani Binti Yaacob; Hairulnizam Bin Mahdin; Mohammed Saeed Jawad; Nayef Abdulwahab Mohammed Alduais; Akhilesh Kumar Sharma; Aldo Erianda
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.5.4.736

Abstract

The globalization of manufacturing has increased the risk of counterfeiting as the demand grows, the production flow increases, and the availability expands. The intensifying counterfeit issues causing a worriment to companies and putting lives at risk. Companies have ploughed a large amount of money into defensive measures, but their efforts have not slowed counterfeiters. In such complex manufacturing processes, decision-making and real-time reactions to uncertain situations throughout the production process are one way to exploit the challenges. Detecting uncertain conditions such as counterfeit and missing items in the manufacturing environment requires a specialized set of technologies to deal with a flow of continuously created data. In this paper, we propose an uncertain detection algorithm (UDA), an approach to detect uncertain events such as counterfeit and missing items in the RFID distributed system for a manufacturing environment. The proposed method is based on the hashing and thread pool technique to solve high memory consumption, long processing time and low event throughput in the current detection approaches. The experimental results show that the execution time of the proposed method is averagely reduced 22% in different tests, and our proposed method has better performance in processing time based on RFID event streams.
Group Decision Support System Using AHP, Topsis and Borda Methods for Loan Determination in Cooperatives Sonatha, Yance; Azmi, Meri; Rahmayuni, Indri
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.5.4.640

Abstract

Cooperatives are one of the business units which purpose is to help the economy of small and medium-sized communities. One of the cooperatives in the city of Padang, West Sumatra, Indonesia is KPN Kapur Warna. The routine business unit managed by KPN Kapur Warna is for savings and loans. So far, the savings and loan process is still done manually, including determining the eligibility of members to receive loans. Determination of the eligibility of members is carried out less objectively, by only looking at the profile of participants in general and the decision-making process is only carried out by one person, namely the chairman of the cooperative. The process that has been carried out so far has often resulted in wrong targets, namely providing loans to members who are not appropriate, resulting in bad credit or delays in paying monthly installments of participants. Therefore, we need a group decision support system that can help solve the above problems. In this study, a group decision support system was made using the AHP, TOPSIS and BORDA methods using five main criteria. The AHP method is used to determine the priority value for each criterion and the TOPSIS method is used to rank each alternative. Each decision maker performs the same process with the two methods, and then voting is carried out using the BORDA method of combining assessments for different decision makers. This study succeeded in providing a reference in determining the eligibility of which members are entitled to receive loans from cooperatives, with results that are more subjective and can help cooperatives in their work efficiently.
Proposition for LMS Integration for Share, Exchange, and Spread of Online Lectures under Covid-19 Environment Ho, Won; Pham, Nguyen-Khang; Lee, Dae-Hyun; Kim, Yong
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.5.4.744

Abstract

There has been a movement to share and spread online lectures through OCW and MOOC systems. This movement would have been spread widely and adopted widely if those courses could be easily exchangeable with other platforms or services. If this function is available, learning activities, resources, learning outcomes can be accessed between different platforms and services. With this function, the credit exchange between different platforms or services will be easier. It also facilitates course sharing and circulation. Because the LMS is the basic platform for online classes, providing sharable and reusable learning activities, resources, and learning outcomes across the different LMSs is very demanding for online education. Analyzing LMS use in Korean universities, Moodle, Canvas, and domestic LMSs are founded to be the significant three kinds that are widely used in Korea. In this paper, a method of integrating Moodle, Canvas, and domestic LMS services is proposed. A central Moodle server is installed as the main LMS server, and the method to connect or complement with a central Moodle server is proposed for each different kind of LMS. LMS users can easily access a different kind of LMS as a form of imported course, tightly connected service, or log in as SSO. This proposition can be applied to various service fields such as KMOOC, KOCW, credit exchange, lecture exchange between universities, regional unification of online educational centers as a practical problem-solver.
Intelligence Eye for Blinds and Visually Impaired by Using Region-Based Convolutional Neural Network (R-CNN) Yee, Lee Ruo; Kamaludin, Hazalila; Safar, Noor Zuraidin Mohd; Wahid, Norfaradilla; Abdullah, Noryusliza; Meidelfi, Dwiny
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.5.4.735

Abstract

Intelligence Eye is an Android based mobile application developed to help blind and visually impaired users to detect light and objects. Intelligence Eye used Region-based Convolutional Neural Networks (R-CNN) to recognize objects in the object recognition module and a vibration feedback is provided according to the light value in the light detection module. A voice guidance is provided in the application to guide the users and announce the result of the object recognition. TensorFlow Lite is used to train the neural network model for object recognition in conjunction with extensible markup language (XML) and Java in Android Studio for the programming language. For future works, improvements can be made to enhance the functionality of the Intelligence Eye application by increasing the object detection capacity in the object recognition module, add menu settings for vibration intensity in light detection module and support multiple languages for the voice guidance.
Early Dropout Prediction in Online Learning of University using Machine Learning Hee Sun Park; Seong Joon Yoo
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.5.4.732

Abstract

Recently, most universities plan to open or open online learning courses, but the problem of  dropout of online learning  is still a problem for universities. Online learning has the advantage of being able to receive education anytime, anywhere, but it is true that the dropout rate is higher than offline classes because you have to manage and control your own study time without the help of a professor or manager. Therefore, it is very important for professors and managers to support students in a timely act to avoid the risk of dropout of university online classes. This study used the access log data recorded in the Learning Management System (LMS) and the learner's statistical information and calculated data, and aims to present predictive algorithms suitable for online learning dropout early prediction systems at universities. This study features a 7-year online learning history log data recorded in the Cyber University LMS system to overcome the data count limitations of existing studies and predict the risk of drop-out during the learning period.  The characteristics of the data you utilized were used to validate the availability of predictive models by applying learner statistical information, number of system connections, number of lectures, previous semester grade data, machine learning based decision tree, arbitrary forest (RF), support vector machine (SVM) and deep learning (DNN). Studies show that random forest (RF) algorithms have the best prediction and performance, and deep learning algorithms also apply to learning management (LMS) systems.
Neural Collaborative with Sentence BERT for News Recommender System Budi Juarto; Abba Suganda Girsang
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.5.4.678

Abstract

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.
Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks Hezam, Abdulkareem A.; Mostafa, Salama A.; Baharum, Zirawani; Alanda, Alde; Salikon, Mohd Zaki
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.5.4.733

Abstract

Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future. Even though many solutions have been developed to identify and prevent this assault, which is mainly targeted at IoT devices, the danger continues to exist and is now larger than ever. It is common practice to launch denial of service attacks in order to prevent legitimate requests from being completed. This is accomplished by swamping the targeted machines or resources with false requests in an attempt to overpower systems and prevent many or all legitimate requests from being completed. There have been many efforts to use machine learning to tackle puzzle-like middle-box problems and other Artificial Intelligence (AI) problems in the last few years. The modern botnets are so sophisticated that they may evolve daily, as in the case of the Mirai botnet, for example. This research presents a deep learning method based on a real-world dataset gathered by infecting nine Internet of Things devices with two of the most destructive DDoS botnets, Mirai and Bashlite, and then analyzing the results. This paper proposes the BiLSTM-CNN model that combines Bidirectional Long-Short Term Memory Recurrent Neural Network and Convolutional Neural Network (CNN). This model employs CNN for data processing and feature optimization, and the BiLSTM is used for classification. This model is evaluated by comparing its results with three standard deep learning models of CNN, Recurrent Neural Network (RNN), and long-Short Term Memory Recurrent Neural Network (LSTM–RNN). There is a huge need for more realistic datasets to fully test such models' capabilities, and where N-BaIoT comes, it also includes multi-device IoT data. The N-BaIoT dataset contains DDoS attacks with the two of the most used types of botnets: Bashlite and Mirai. The 10-fold cross-validation technique tests the four models. The obtained results show that the BiLSTM-CNN outperforms all other individual classifiers in every aspect in which it achieves an accuracy of 89.79% and an error rate of 0.1546 with a very high precision of 93.92% with an f1-score and recall of 85.73% and 89.11%, respectively. The RNN achieves the highest accuracy among the three individual models, with an accuracy of 89.77%, followed by LSTM, which achieves the second-highest accuracy of 89.71%. CNN, on the other hand, achieves the lowest accuracy among all classifiers of 89.50%.
Blockchain in Supermarkets: Mitigating the Problem of Organic Waste Generation Egatz Wozniak, Marcos; Valdés-González, Héctor; Reyes-Bozo, Lorenzo
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.5.4.652

Abstract

This work presents a proposal for a solution to the specific problem of organic waste generated by supermarkets and understood as t merchandise of organic and perishable composition that could not be marketed during its validity period. The goal of this research is to propose a solution based on Blockchain technology in Chile, which would allow an immutable, decentralized, and validated transaction record to be kept. Such a record would enable supermarkets to trace the life cycle of those products that make up organic and perishable merchandise in a transparent, reliable, and scalable way. To this end, the problem is modeled using the Blockchain Hyperledger Fabric platform (an open-source platform started by the Linux Foundation), which is fed with relevant information and data on the status of a representative set of organic merchandise products. At the same time, a qualitative approach is proposed to gather the opinions of executives and logistics operators through semi-structured interviews, and considering a convenience sample. With a sample of 6 executives, it is understood how the proposal is perceived and its applicability in supermarkets and distributors. The data show that both obtaining information and making decisions about it are achieved in a distributed and collaborative way, allowing for reliable and agile traceability, thereby mitigating the low quality of the information provided by the actors that make up the supply chain. This service is perceived as desirable by both customers and operators.  The model enables not only horizontal communications between suppliers, distributors, and consumers, but also vertical ones, and thus, ultimately, makes the company's income statement more efficient.

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