cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 26 Documents
 1   2   3 
Search results for , issue "Vol 6, No 4 (2022)" : 26 Documents clear
A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia Herho, Sandy H. S.
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.6.4.953

Abstract

Kali Kupang plays an important role in the life of the people of Pekalongan and its surrounding areas. However, until recently, not many hydrological studies have been carried out in this area. This study presents how Extreme Value Analysis (EVA) can predict future extreme hydrological events and how a dynamic-programming-based change point detection algorithm can detect the abrupt transition in discharge events variability. Using the annual block maxima, we can predict the upper extreme discharge probability from the generalized extreme value distribution (GEVD) that best fits the data by using the Markov Chain Monte Carlo (MCMC) algorithm as a distribution fitting method. Metropolis-Hasting (MH) algorithm with 500 walkers and 2,500 samples for each walker is used to generate random samples from the prior distribution. As a result, this discharge data can be categorized as a Gumbel distribution (  = 6.818,  = 3.456, and  = 0). The recurrence intervals (RI) for this discharge data can be calculated through this distribution. The changepoint location of the annual standard deviation of this discharge data in the mid-1990s is detected by using the pruned exact linear time (PELT) algorithm. Despite some shortcomings, this study can pave the way for using data-driven algorithms, along with more traditional numerical and descriptive approaches, to analyze hydrological time-series data in Indonesia. This is crucial, considering an increasing number of hydro climatological disasters in the future as a consequence of global climate change.
Comparison of Feature Selection Methods for DDoS Attacks on Software Defined Networks using Filter-Based, Wrapper-Based and Embedded-Based Kurniawan, M.T.; Yazid, Setiadi; Sucahyo, Yudho Giri
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.6.4.1476

Abstract

The development of internet technology is growing very rapidly. Moreover, keeping internet users protected from cyberattacks is part of the security challenges. Distributed Denial of Service (DDoS) is a real attack that continues to grow. DDoS attacks have become one of the most difficult attacks to detect and mitigate appropriately. Software Defined Network (SDN) architecture is a novel network management and a new concept of the infrastructure network. A controller is a single point of failure in SDN, which is the most dangerous of various attacks because the attacker can take control of the controller so that it can control all network traffic. Various detection and mitigation methods have been offered, but not many consider the capacity of the SDN controller. In this research, we propose a feature selection method for DDoS attacks. This research aims to select the most important features of DDoS attacks on SDN so that the detection of DDoS on SDN can be lightweight and early. This research uses a dataset [1] generated by a Mininet emulator. The simulation runs for benign TCP, UDP, and ICMP traffic and malicious traffic, which is the collection of TCP SYN attacks, UDP Flood attacks, and ICMP attacks. A total of 23 features are available in the dataset, some are extracted from the switches, and others are calculated. By using three methods, filter-based, wrapper-based, and embedded-based, we get consistent results where the pktcount feature is the highest feature importance of DDoS attacks on SDN.
Classification of Tempeh Maturity Using Decision Tree and Three Texture Features - Istiadi; - Faqih; Aviv Yuniar Rahman; Dean Ariesta Aziz; April Lia Hananto; Sarina Sulaiman; Candra Zonyfar
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.6.4.983

Abstract

Tempe is an average food from Indonesia, eaten in Indonesia. Even today, tempe is around the world, and vegans around the world use tempeh as a meat substitute. This study plans to work on the accuracy of tempe characterization by utilizing the three-element extraction technique and the choice tree arrangement strategy. This research uses a decision tree method with three texture features in its classification. The results obtained indicate that this method has the highest Gabor channel level, including extraction, which is 71% accuracy, the split proportion is 10;90 and the lowest is 60% with parted balance of 90:10. The most important level value of GCLM extraction precision is 86% with a split proportion of 90;10 and the lowest price level and 60% level with a split ratio of 10;90 for Wavelet including the highest extraction rate price is 77%. It can be said that from the extraction of three elements, GLCM is the element extraction with the highest value from Gabor and Wavelet, including extraction at a split proportion of 10:90 by 86%. The test shows the Featured Tree highlight designation. The extraction technique was superior to different strategies for interaction characterization of tempe development quality. In the next research, improve the accuracy performance so that it can reach 100% using the CNN deep learning method. Then you can also add Support Vector Machine (SVM) and Naive Bayes methods based on the GLCM Extraction feature.
Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network - Mambang; Finki Dona Marleny
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.6.4.923

Abstract

Learning content can be identified through text, images, and videos. This study aims to predict the learning content contained on YouTube. The images used are images contained in the learning content of the exact sciences, such as mathematics, and social science fields, such as culture. Prediction of images on learning content is done by creating a model on CNN. The collection of datasets carried out on learning content is found on YouTube. The first assessment was performed with an RMSProp optimizer with a learning rate of 0.001, which is used for all optimizers. Several other optimizers were used in this experiment, such as Adam, Nadam, SGD, Adamax, Adadelta, Adagrad, and Ftrl. The CNN model used in the dataset training process tested the image with multiple optimizers and obtained high accuracy results on RMSprop, Adam, and Adamax. There are still many shortcomings in the experiments we conducted in this study, such as not using the momentum component. The momentum component is carried out to improve the speed and quality of neural networks. We can develop a CNN model using the momentum component to obtain good training results and accuracy in later studies. All optimizers contained in Keras and TensorFlow can be used as a comparison. This study concluded that images of learning content on YouTube could be modeled and classified. Further research can add image variables and a momentum component in the testing of CNN models.
A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification Berutu, Sunneng Sandino; Chen, Yeong-Chin; Wijayanto, Heri; Budiati, Haeni
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.6.4.1529

Abstract

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 
Determining Forest Fire Position from UAV Photogrammetry using Color Filtration Algorithm Muid, Abdul; Evita, Maria; Aminah, Nina; Budiman, Maman; Djamal, Mitra
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.6.4.956

Abstract

Forest fires frequently happen worldwide, especially in the dry season. A forest fire early warning system (EWS) is needed to prevent this disaster. The main part of EWS is the hotspot detection system. On the other side, Unmanned Aerial Vehicle (UAV) technology offers an alternative solution to detect the hotspot for poor satellite image processing accuracy. Remote sensing techniques with UAV working drones are progressively challenging. Drones can provide results in 2D and 3D images with high resolution and real-time. Therefore, in this research, we have used a photogrammetry application from the number of images collected by a UAV with an optimum flight plan for the mission to determine the location of the forest fire. This paper describes remote sensing experiments using drones to detect land fires. The experiment was carried out using a quadcopter drone of the DJI Phantom 4 Pro. The photos are processed using Agisoft Metashape Professional image processing software and become a 2D image. These images captured a fire simulation in a known location. After a high resolution (GSD – Ground Sampling Distance – 0.87cm/px) orthophoto had been generated, a color filtration algorithm detected a hotspot to detect a fire at the exact location. The results are almost zero deviation of longitude and latitude from the real location with 1.44 m2 and 1.06 m2 fire area from 2 experiments. This algorithm program has TPR and FPR are 0,78 and 0, respectively. Further research can develop an EWS with a combination of UAV and Wireless Sensor Networks.

Page 3 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue