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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.
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Articles 20 Documents
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Search results for , issue "Vol 6, No 1 (2022)" : 20 Documents clear
Simultaneous Hydroponic Nutrient Control Automation System Based on Internet of Things Demi Adidrana; Ade Rahmat Iskandar; Ade Nurhayati; - Suyatno; Mohamad Ramdhani; Kharisma Bani Adam; Rizki Ardianto; Cahyantari Ekaputri
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.6.1.865

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 Seungdo Jeong
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.6.1.858

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 Aninda W. Rudiastuti; Yustisi Lumban-Gaol; Florence E. S. Silalahi; Yosef Prihanto; Widodo S. Pranowo
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.6.1.873

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.
Tree-based Filtering in Pulse-Line Intersection Method Outputs for An Outlier-tolerant Data Processing Cahya Damarjati; Karisma Trinanda Putra; Heri Wijayanto; Hsing-Chung Chen; Toha Ardi Nugraha
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.6.1.861

Abstract

Pulse palpation is one of the non-invasive patient observations that identify patient conditions based on the shape of the human pulse. The observations have been practiced by Traditional Chinese Medicine (TCM) practitioners since thousands of years ago. The practitioners measure the patient’s arterial pulses in three points of both patient wrists called chun, guan, and chy, then diagnose based on their knowledge and experience. Pulse-Line Intersection (PLI) method extract features of each pulse from the observed pulse wave sequence. PLI is performed by summing the number of intersections between the artificial line and the pulse wave. The method is proven in differentiating between hesitant with moderate pulse waves. As the method implemented in Clinical Decision Support System (CDSS) related to pulse palpation, some outlier data might emerge and affect the measurement result. Thus, outlier filtering is needed to prevent unnecessary prediction processes by machine learning (ML) models inside CDSS. This study proposed an outlier filtering model using a decision tree algorithm. This concept is designed by analyzing pulse features values and the chance of odd values combination. Then inappropriate values are excepted using several rules. Every pulse feature list that did not pass the filtering rule is categorized as outliers and were not included for further process. The proposed model works more efficiently than ML models dealing with outliers since this procedure is unsupervised learning with a small number of parameters. Overall, the proposed filtering method can be used in pulse measurement applications by eliminating outlier data that might decrease the performance of ML models.
The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification Khyrina Airin Fariza Abu Samah; Nur Farhanah Amirah Misdan; Mohd Nor Hajar Hasrol Jono; Lala Septem Riza
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.6.1.879

Abstract

Online reviews are crucial for business growth and customer satisfaction. There is no exception for the airlines’ company, which places third as the biggest contributor to Malaysia’s Gross Domestic Product. Customer opinions play an important role in maintaining the reputation and improving the quality of service of the airlines. However, there is no specific platform for online review. Most online ratings obtain English, leading to inaccurate results as not all reviews regarding different languages are considered. Airlines currently have no specific platform for online reviews despite being critical for business growth, performance, and customer experience improvement. Hence, this paper proposed implementing a web-based dashboard to visualize the best Malaysian airline companies. The airline companies involved are AirAsia, Malaysia Airlines, and Malindo Air. We designed and developed the proposed study through the bilingual analysis of Twitter sentiment using the Naïve Bayes algorithm. Naïve Bayes algorithm is a machine learning approach to do classification. The tweets extracted were analyzed as metrics that advance airline companies’ online presence. Testing phases have shown that the classifier successfully classified tweets’ sentiment with 93% accuracy for English and 91% for Bahasa. Every feature in the web-based dashboard functions correctly and visualizes a detailed analysis of sentiment. We applied the System Usability Scale to test the study’s usability and managed to get a score of 94.7%. The acceptability score ‘acceptable’ result concluded that the study reflects a good solution and can assist anyone in understanding the public views on airline companies in Malaysia.
An Android Malware Detection System using a Knowledge-based Permission Counting Method Sun-A Lee; A-Reum Yoon; Ji-Won Lee; Kwangjae Lee
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.6.1.859

Abstract

As the number of cases of damage caused by malicious apps increases, accurate detection is required through various detection conditions, not just detection using simple techniques. In this paper, we propose a knowledge-based machine learning method using authority information and adding its usage counting features. This method is classifying training apps and malicious apps through machine learning using permission features in manifest.xml of Android apps. As a result of the experiment, accuracy, recall, precision, F1 score are 99.01%, 97.70%, 100.0%, 99.01%, respectively. Since Recall is higher than other indicators, it accurately predicts malicious apps as malicious. In other words, the proposed system is effective in preventing the distribution of malicious apps.
Image Captioning with Style Using Generative Adversarial Networks Dennis Setiawan; Maria Astrid Coenradina Saffachrissa; Shintia Tamara; Derwin Suhartono
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.6.1.709

Abstract

Image captioning research, which initially focused on describing images factually, is currently being developed in the direction of incorporating sentiments or styles to produce natural captions that reflect human-generated captions. The problem this research tries to solve the problem that captions produced by existing models are rigid and unnatural due to the lack of sentiment. The purpose of this research is to design a reliable image captioning model that incorporates style based on state-of-the-art SeqCapsGAN architecture. The materials needed are MS COCO and SentiCaps datasets. Research methods are done through literature studies and experiments. While many previous studies compare their works without considering the differences in components and parameters being used, this research proposes a different approach to find more reliable configurations and provide more detailed insights into models’ behavior. This research also does further experiments on the generator part that have not been thoroughly investigated. Experiments are done on the combinations of feature extractor (VGG-19 and ResNet-50), discriminator model (CNN and Capsule), optimizer (Adam, Nadam, and SGD), batch size (8, 16, 32, and 64), and learning rate (0.001 and 0.0001) by doing a grid search. In conclusion, more insights into the models’ behavior can be drawn, and better configuration and result than the baseline can be achieved. Our research implies that research in comparative studies of image recognition models in image captioning context, automated metrics, and larger datasets suited for stylized image captioning might be needed for furthering the research in this field.
Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method Suci Aulia; Dadi Rahmat
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.6.1.864

Abstract

Magnetic Resonance Imaging (MRI) in diagnosing brain cancers is widespread. Because of the variety of angles and clarity of anatomy, it is commonly employed. If a brain tumor is malignant or secondary, it is a high risk, leading to death. These tumors have an increased predisposition for spreading from one place to another. In detecting brain abnormality form such as a tumor, from a magnetic resonance scan, expertise and human involvement are required. Previous, the image segmentation of brain tumors is widely developed in this field. Suppose we could somehow use an automatic brain tumor detection technology to identify the presence of a tumor in the brain without requiring human intervention. In that case, it will give us a leg up in the treatment process. This research proposed two stages to identify the brain tumor in MRI; the first stage was the image enhancement process using Clip Limit Adaptive Histogram Equalization (CLAHE) to segment the brain MRI. The second one was classifying the brain tumor on MRI using Visual Geometry Group-16 Layer (VGG-16). The CLAHE was used in some instances, there were CLAHE applied in FLAIR image on green color, and CLAHE applied in Red, Green, Blue (RGB) color space. The experimental result showed the highest performance with accuracy, precision, recall, respectively 90.37%, 90.22%, 87.61%. The CLAHE method in RGB Channel and the VGG-16 model have reliably on predicted oligodendroglioma classes in RGB enhancement with precision 91.08% and recall 95.97%.
SD-Honeypot Integration for Mitigating DDoS Attack Using Machine Learning Approaches Fauzi Dwi Setiawan Sumadi; Alrizal Rakhmat Widagdo; Abyan Faishal Reza; - Syaifuddin
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.6.1.853

Abstract

Distributed Denial of Services (DDoS) is still considered the main availability problem in computer networks. Developing a programmable Intrusion Prevention System (IPS) application in a Software Defined Network (SDN) may solve the specified problem. However, the deployment of centralized logic control can create a single point of failure on the network. This paper proposed the integration of Honeypot Sensor (Suricata) on the SDN environment, namely the SD-Honeypot network, to resolve the DDoS attack using a machine learning approach. The application employed several algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), and Random Forest (RF)) and comparatively analyzed. The dataset used during the emulation utilized the extracted Internet Control Message Protocol (ICMP) flood data from the Suricata sensor. In order to measure the effectiveness of detection and mitigation modules, several variables were examined, namely, accuracy, precision, recall, and the promptness of the flow mitigation installation process. The Honeypot server transmitted the flow rule modification message for blocking the attack using the Representational State Transfer Application Programming Interface (REST API). The experiment results showed the effectiveness of CART algorithm for detecting and resolving the intrusion. Despite the accuracy score pointed at 69-70%, the algorithm could promptly deploy the mitigation flow within 31-49ms compared to the SVM, which produced 93-94% accuracy, but the flow installation required 112-305ms. The developed CART module can be considered a solution to prevent the attack effectively based on the analyzed variable.

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