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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 45 Documents
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Search results for , issue "Vol 7, No 2 (2023)" : 45 Documents clear
Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping Wedashwara, Wirarama; Irmawati, Budi; Wijayanto, Heri; Arimbawa, I Wayan Agus; Widartha, Vandha Pradwiyasma
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1813

Abstract

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study Yab, Li Yu; Wahid, Noorhaniza; A Hamid, Rahayu
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1509

Abstract

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.
Esports Games in Elementary School: A Systematic Literature Review Hamidulloh Ibda; Muhammad Fadloli Al Hakim; Khamim Saifuddin; Ziaul Khaq; Ahmad Sunoko
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1031

Abstract

Several studies have explored esports games, and few have examined esports games in elementary schools with a systematic literature review. This research explores articles on the concept, features, training, implementation, and impact of esports games in elementary schools. The SLR and PRISMA methods were applied in this research with the stages of identification, screening, eligibility, and inclusion assisted by the Publish or Perish 7, VOS viewer, and NVIVO 12 Plus applications. There were 521 Scopus-indexed articles found. Furthermore, the articles were filtered according to the theme into 50 pieces. The findings of relevant topics are esports, esports games, the concept of esports games, elementary school, etc. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described. The findings of this study state that esports games are digital innovations in online video competitions, such as tournaments developing in education. The features of esports games in elementary school are manual sports integrated with digital augmentation, multiplayer and competitive, digitalization of physical sports, new digital-based features, and educational games, such as LoL (MOBA), Battle Royal, FIFA EA Sports, Mobile Legend, WISE game, and others. Training esports games through socialization, education, workshops, GDLC, curriculum development, and multimedia esports games. Implementation of esports games through competition, entertainment, game-based multimedia, SE, and TGfU, has positive and negative impacts. This research has limitations in that it only collects information from current literature, reviews esports at the elementary school level, and is not a field study. Future research needs to examine esports games according to the times in elementary school.
Hemp-Alumina Composite Radar Absorption Reflection Loss Classification Muhlasah Novitasari Mara; Budi Basuki Subagio; Efrilia M Khusna; Bagus Satrio Utomo
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1169

Abstract

The Radar Absorption Material (RAM) method is a coating for reducing the energy of electromagnetic waves received by converting the electromagnetic waves emitted by radar into heat energy. Hemp has been studied to have the strongest and most stable tensile characteristics of 5.5 g/den and has higher heat resistance compared to other natural fibers. Combining the characteristics of hemp with alumina powder (Al2O3) and epoxy resin could provide a stealth technology system that is able to absorb radar waves more optimally, considering that alumina has light, anti-rust and conductive properties. The electromagnetic properties of absorbent coatings can be predicted using machine learning.  This study classifies the reflection loss of Hemp-Alumina Composite using Random Forest, ANN, KNN, Logistic Regression, and Decision Tree. These machine learning classifiers are able to generate predictions immediately and can learn critical spectral properties across a wide energy range without the influence of data human bias. The frequency range of 2-12 GHz was used for the measurements.  Hemp-Alumina composite has result that the most effective structure thickness is 5mm, used as a RAM with optimum absorption in S-Band frequencies of -15,158 dB, C-Band of -16,398 dB and X-Band of -23,135 dB. The highest and optimum reflection loss value is found in the X-Band frequency with a thickness of 5mm which is equal to -23.135 dB with an absorption bandwidth of 1000 MHz and efficiencyof 93.1%. From this result, it is proven that Hemp-Alumina Composite is very effective to be used as a RAM on X-Band frequency.  Based on the results of the experiments, the Random Forest Classifier has the highest values of accuracy (0.97) and F1 score (0.98). The F1 score and accuracy of Random Forest are 0.96 and 0.97, respectively, and do not significantly differ from KNN. 
Using Various Convolutional Neural Network to Detect Pneumonia from Chest X-Ray Images: A Systematic Literature Review Darnell Kikoo; Bryan Tamin; Stephen Hardjadilaga; - Anderies; Irene Anindaputri Iswanto
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1015

Abstract

Pneumonia is one of the world's top causes of mortality, especially for children. Chest X-rays serve an important part in diagnosing pneumonia due to the cost-effectiveness and quick advancement of the technology. Detecting Pneumonia through Chest X-rays (CXR) is a challenging and time-consuming process requiring trained professionals. This issue has been solved by the development of automation technology which is machine learning. Moreover, Deep Learning (DL), a machine learning specification that uses an algorithm that resembles the human brain, can predict more accurately and is now dependable enough to predict pneumonia. As time passes, another Deep Learning improvement has been made to produce a new method called Transfer Learning, that is done by extracting specific layers from some pre-trained network to be used on other datasets, which reduces the training time and improves the model performance. Although numerous algorithms are already available for pneumonia identification, a comprehensive literature evaluation and clinical recommendations are still small in numbers. This research will assist practitioners in choosing some of the best procedures from the recent research, reviewing the available datasets, and comprehending the outcomes gained in this domain. The reviewed papers show that the best score for predicting pneumonia using DL from CXR was 99.4% accuracy. The exceptional techniques and results from the reviewed papers served as great references for future research.
Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System Masruroh, Siti Ummi; Syahid, Muhammad Fikri; Munthaha, Firman; Muharram, Asep Taufik; Putri, Rizka Amalia
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1605

Abstract

Around 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting.
Analysis of Resilience of Education System in Higher Education Due to Covid-19 Pandemic in Indonesia: A Systematic Literature Review Widiartha, Ida Bagus Ketut; Hwang, Jun-seok; Yoon, Hyoen-yeong; Pratiwi, Oktariani Nurul
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1814

Abstract

This study discusses learning strategies resilience that can be used to improve learning outcomes during the current pandemic circumstances, which have limitations in face-to-face learning. Online learning has many limitations compared to offline one. However, it must keep running because one of the strategies against the SAR-Cov2 virus is to inhibit its spread by limiting direct contact with other people. The literature review is carried out with a protocol involving text mining tools to find the most widely used keywords and their relationships, which is then carried out by a snowball literature review to deepen these keywords. There are several findings from this study, namely (1) Three critical components that play a significant role in improving learning outcomes in the distance learning method, namely the role of students, lectures, and technology. (2) A framework must ensure that the other three components perform their functions properly and provide an effective learning environment. (3) Reward and punishment play a vital role in ensuring the framework is implemented as it should be. Integrating an effective learning environment with remuneration programs and teaching grants will encourage improvements in the learning process and increase the number of positive contents on the Internet. This learning environment can also be a model that supports independent learning activities - an independent campus, the Ministry of Education and Culture of the Republic of Indonesia's flagship program, and digital commercialization in the educational sphere.
Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System Setiawan, Ari; Sigit, Riyanto; Rokhana, Rika
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1546

Abstract

These days, the application of image processing in computer vision is becoming more crucial. Some situations necessitate a solution based on computer vision and growing deep learning. One method continuously developed in deep learning is the Convolutional Neural Network, with MobileNet, EfficientNet, VGG16, and others being widely used architectures. Using the CNN architecture, the dataset consists primarily of images; the more datasets there are, the more image storage space will be required. Compression via the discrete cosine transform technique is a method to address this issue. We implement the DCT compression method in the present research to get around the system's limited storage space. Using DCT, we also compare compressed and uncompressed images. All users who had been trained with each test 5 times for a total of 150 tests were given the test. Based on testing findings, the size reduction rate for compressed and uncompressed images is measured at 25%. The case study presented is face recognition, and the training results indicate that the accuracy of compressed images using the DCT approach ranges from 91.33% to 100%. Still, the accuracy of uncompressed facial images ranges from 98.17% to 100%. In addition, the accuracy of the proposed CNN architecture has increased to 87.43%, while the accuracy of MobileNet has increased by 16.75%. The accuracy of EfficientNetB1 with noisy-student weights is measured at 74.91%, and the accuracy of EfficientNetB1 with imageNet weights can reach 100%. Facial biometric authentication using a deep learning algorithm and DCT-compressed images was successfully accomplished with an accuracy value of 95.33% and an error value of 4.67%.
Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping Zahrotun, Lisna; Linarti, Utaminingsih; Suandi As, Banu Harli Trimulya; Kurnia, Herri; Sabila, Liya Yusrina
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1204

Abstract

One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.
A Framework for Malay Computational Grammar Formalism based-on Enhanced Pola Grammar Hassan Mohamed; Nur Aisyah Abdul Fataf; Tengku Mohd Tengku Sembok
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1172

Abstract

In the era of IR4.0, Natural Language Processing (NLP) is one of the major focuses because text is stored digitally to code the information. Natural language understanding requires a computational grammar for syntax and semantics of the language in question for this information to be manipulated digitally. Many languages around the world have their own computational grammars for processing syntax and semantics. However, when it comes to the Malay language, the researchers have yet to come across a substantial computational grammar that can process Malay syntax and semantics based on a computational theoretical framework that can be applied in systems such as e-commerce. Hence, we intend to propose a formalism framework based on enhanced Pola Grammar with syntactic and semantic features. The objectives of this proposed framework are to create a linguistic computational formalism for the Malay language based on theoretical linguistic; implement templates for Malay words to handle syntax and semantic features in accordance with the enhanced Pola Grammar; and create a Malay Language Parser Algorithm that can be used for digital applications. To accomplish the objectives, the proposed framework will recursively formalise the computational Malay grammar and lexicon using a combination of solid theoretical linguistic foundations such as Dependency Grammar. A Malay parsing algorithm will be developed for the proposed model until the formalised grammar is deemed reliable. The findings of this indigenous Malay parser will help to advance Malay language applications in the digital economy.

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