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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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A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka; Kurnianingsih, Kurnianingsih; Hashimoto, Takako
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1171

Abstract

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
Saudi Learners' Perception of Infographics in Education: A Survey Alyahya, Suzan
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1521

Abstract

Learners' learning experiences diverge and undergo rapid shifts due to trends in technology, and the transfer of knowledge in varying formats and styles is ubiquitous. Infographics find its application in instructional design and technology that drives education with state-of-the-art tools and applied methods. Being a format of data, Infographics or visual images presents information or knowledge using visuals. This study aims to present the student perceptions of learning via infographics at Princess Nourah bint Abdurrahman University (PNU) in Saudi Arabia. The study employs a survey questionnaire consisting of 13 close-ended questions posed to assess the learners' responses at PNU. Applied Likert's scale-based questions present a conversion of user data input to quantitative figures, which present the level of understanding and the role of infographics in education. The survey involved 45 undergraduate female students pursuing undergraduate degree courses at PNU. Using survey research design methodology, the study investigated learners' perceptions of infographics to add to their learning experiences and provides a quantitative analysis of observed responses to the survey questionnaire. The study conducts an online survey and classifies participants of different age groups into five categories for assessment. The study findings reveal that PNU learners perceive a positive role of infographics in their learning. However, learners showcase varied perceptions of a) the acceptance of assignments based on infographics and b) the use of static versus animated infographics. The study guides research scholars toward the intuition of infographics in learning environments and reports the two research problems to be addressed in future works.
Web-based E-learning in Elementary School: A Systematic Literature Review Purnama, Herwulan Irine; Wilujeng, Insih; Abdul Jabar, Cepi Safruddin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1203

Abstract

This article presents literature review on web-based e-learning in elementary school in the latest literature. SLR method and PRISMA protocol with the stages of identification, screening, eligibility, inclusion, and abstraction, data analysis assisted by the Publish or Perish 7 application, VOSviewer, and NVIVO 12 Plus. The results of searching for articles on Scopus through the Publish or Perish 7 application are 507. Then the articles were filtered according to compatible themes into 50 articles. The topic findings are web-based e-learning, elementary school, the impact of web-based e-learning and web-based e-learning concept, academic performance, teaching/learning strategies, online learning, Covid-19, HPC database, web-based applications, distance learning, 3D visualization, automation, strategic learning, semantic web, technology, education, linguistic content, big data architecture, learning setting, e-readiness, linguistic content, STEM, etc., that are directly or indirectly connected. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described according to the research questions. The findings in this article explain that web-based e-learning integrates pedagogy and technology and becomes part of digital multimedia implemented in e-learning, blended learning, and face-to-face that impacts elementary school students and teachers directly or indirectly. Future research needs to explore web-based e-learning in schools that is current, safe, and needed by students and teachers.
Skew Correction and Image Cleaning Handwriting Recognition Using a Convolutional Neural Network Uyun, Shofwatul; Rahardyan, Seto; Anshari, Muhammad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1712

Abstract

Handwriting recognition is a study of Optical Character Recognition (OCR) which has a high level of complexity. In addition, everyone has a unique and inconsistent handwriting style in writing characters upright, affecting recognition success. However, proper pre-processing and classification algorithms affect the success of pattern recognition systems. This paper proposes a pre-processing method for handwriting image recognition using a convolutional neural network (CNN). This study uses public datasets for training and private datasets for testing. This pre-processing consists of three processes: image cleaning, skew correction, and segmentation. These three processes aim to clean the image from unnecessary ink streaks. In addition, to make angle corrections to characters in italics in their writing. The model testing process uses image test data of handwriting that are not straight. There are three images based on the inclination angle: less than 45 degrees, equal to 45 degrees, and more than 45 degrees. Picture cleaning removes unnecessary strokes (noise) from the image using a layer mask, whereas skew correction changes the handwriting to an upright posture based on the detected angle. The pre-processing model we propose worked optimally on handwriting with a skew angle of fewer than 45 degrees and 45 degrees. Our proposed model generally works well for handwriting with fewer than 45 degrees skew with an accuracy of 88,96%. Research with a similar scope can continue to improve optimization with a focus on algorithms related to analysis layout studies. Besides that, it can focus more on automation in the segmentation process of each character.
Verification of Ph.D. Certificate using QR Code on Blockchain Ethereum Noorhizama, Nur Khairunnisa; Abdullah, Zubaile; Kasim, Shahreen; A Hamid, Isredza Rahmi; Mat Isa, Mohd Anuar
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1584

Abstract

One of the major challenges the university faces is to provide real-time verification of their student's degree certification upon request by other parties. Conventional verification systems are typically costly, time-consuming and bureaucratic against certificate credential misconduct. In addition, the forgery of graduation degree certificates has become more efficient due to easy-to-use scanning, editing, and printing technologies. Therefore, this research proposes verifying Ph.D. certificates using QR codes on the Ethereum blockchain to address certificate verification challenges. Blockchain technology ensures tamper-proof and decentralized management of degree certificates as the certificates stored on the blockchain are replicated across the network. The issuance of certificates requires the use of the issuer's private key, thus preventing forgery. The system was developed using Solidity for the smart contract, PHP, HTML/CSS for the web-based implementation, and MetaMask for blockchain integration. User testing confirmed the successful implementation and functionality of the system. Users can add, update, and delete certificates, generate and scan QR codes, and receive instant verification feedback. The verification system effectively meets all requirements, providing a robust solution for validating Ph.D. certificates. Future research may focus on scalability and adoption, privacy and data protection, user experience, and integration with existing systems. Other researchers can optimize the verification system for widespread adoption and utilization by exploring these areas. This research contributes to securing and efficiently verifying academic certificates using QR codes on the Ethereum blockchain. Ultimately, this work advances the field of certificate verification and promotes trust in academic credentials.
Modified LeNet-5 Architecture to Classify High Variety of Tourism Object: A Case Study of Tourism Object for Education in Tinalah Village Wijaya, Antonius Bima Murti; Murti, Desideria Cempaka Wijaya; Handoko, Victoria Sundari
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2095

Abstract

This research aims to modify a CNN (Convolutional Neural Network) based on LeNet-5 to reduce overfitting in a Tinalah Tourism Village dataset object detection. Tinalah Tourism Village has many objects that can be identified for tourism education and enhanced tourist experience. While these objects, spread across the different sites of Tinalah do vary, some share similarities in their histogram patterns. Visually, if the size of a picture is reduced in the LeNet-5 ‘preferred size’ feature, it will inevitably lose some of its information, making pictures too similar reducing accuracy. In order to learn and classify objects, this research performs a modification on LeNet-5 architecture to provide a better performance geared toward larger input imaging. The previous state-of-the-art architecture showed an overfitting performance where the training accuracy performed too much better than the testing accuracy in our dataset. We brought in a dropout layer to reduce overfitting, increase the dense layer's size, and add a convolution layer. We then compared the modified LeNet-5 with other state-of-the art architecture, such as LeNet-5 and AlexNet. Results showed that a modified LeNet-5 outperformed other architectures, especially in performing accuracy for testing the Tinalah dataset, reaching 0.913 or (91,3 %). This research discusses the dataset, the modified LeNet-5 architecture, and performance comparison between state-of-the-art CNN architecture. Our CNN architecture can be developed by involving a transfer learning mechanism to provide greater accuracy for further research.
Geometry Representation Effectiveness in Improving Airfoil Aerodynamic Coefficient Prediction with Convolutional Neural Network Zikri, Arizal Akbar; Defianti, Hanni; Hidayat, Wahyu; Purqon, Acep
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1577

Abstract

Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and heat transfer. Each airfoil has different aerodynamic coefficients. Obtaining the aerodynamic coefficients is a must to optimize the airfoil design. Engineers use various methods to get the airfoil aerodynamic coefficients. A prediction method is an approximation approach that effectively reduces time and cost. This article uses convolutional neural networks (CNN) to get approximation values of those coefficients. In CNN, we collect 8920 aerodynamic coefficients for 223 NACA 4 as labels in datasets by using XFOIL at  and  with varying angles of attacks starting  to  with increment of . The simulation results are compared to the experiment using E387 airfoil for validation. Then, airfoil geometries as part of input datasets were transformed into Grayscale and RGB images using the signed distance function (SDF) and mesh algorithm. Each airfoil representation was trained using an 80% dataset and tested using a 20% dataset with Adam as an optimizer to generate each prediction model using modified LeNet-5. We use three different layer depths in modified LeNet-5 to obtain the optimal layer number. There is no remarkable improvement when varying the depth layers, so four layers are used instead. Simulation results show that using an SDF with Fast Marching Method on CNN predicts the most effective for the airfoil’s lift, drag, and pitch moment coefficient with varying angles of attack simultaneously. One can extend the method by using SDF to recognize different flow conditions.
Composition Model of Organic Waste Raw Materials Image-Based To Obtain Charcoal Briquette Energy Potential Saptadi, Norbertus Tri Suswanto; Suyuti, Ansar; Ilham, Amil Ahmad; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1682

Abstract

Indonesia needs new renewable energy as an alternative to fuel oil. The existence of organic waste is an opportunity to replace oil because it is renewable and contains relatively less air-polluting sulfur. Previous research that has been widely carried out still utilizes coconut shell raw materials, which are increasingly limited in number, so other alternative raw materials are needed. A model is needed to make a formulation that can optimize the composition of organic waste raw materials as a basic ingredient for making briquettes. The research objective was to determine the best raw material composition based on digital image analysis in processing organic waste into briquettes. An artificial intelligence approach with a Convolutional Neural Network (CNN) architecture can predict an effective object detection model. The image analysis results have shown an effective model in the raw material composition of 60% coconut, 20% wood, and 20% adhesive to produce quality biomass briquettes. Briquettes with a higher percentage of coconut will perform better in composition tests than mixed briquettes. The energy obtained from burning briquettes is useful for meeting household fuel needs and meeting micro, small, and medium business industries.
Customer Loyalty Prediction for Hotel Industry Using Machine Learning Approach Hamdan, Iskandar Zul Putera; Othman, Muhaini; Mohmad Hassim, Yana Mazwin; Marjudi, Suziyanti; Mohd Yusof, Munirah
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1335

Abstract

Today, machine learning is utilized in several industries, including tourism, hospitality, and the hotel industry. This project uses machine learning approaches such as classification to predict hotel customers’ loyalty and develop viable strategies for managing and structuring customer relationships. The research is conducted using the CRISP-DM technique, and the three chosen classification algorithms are random forest, logistic regression, and decision tree. This study investigated key characteristics of merchants’ customers’ behavior, interest, and preference using a real-world case study with a hotel booking dataset from the C3 Rewards and C3 Merchant systems. Following a comprehensive investigation of prospective preferences in the pre-processing phase, the best machine learning algorithms are identified and assessed for forecasting customer loyalty in the hotel business. The study's outcome was recorded and examined further before hotel operators utilized it as a reference. The chosen algorithms are developed utilizing Python programming language, and the analysis result is evaluated using the Confusion Matrix, specifically in terms of precision, recall, and F1-score. At the end of the experiment, the accuracy values generated by the logistic regression, decision tree, and random forest algorithms were 57.83%, 71.44%, and 69.91%, respectively. To overcome the limits of this study method, additional datasets or upgraded algorithms might be utilized better to understand each algorithm's benefits and limitations and achieve further advancement. 
Analyzing Coverage Probability of Reconfigurable Intelligence Surface-aided NOMA Widodo, Agung Mulyo; Wijayanto, Heri; Wijaya, I Gede Pasek Suta; Wisnujati, Andika; Musnansyah, Ahmad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2054

Abstract

Along with the explosive growth of wireless communication network users who require large frequency bands and low latency, it is a challenge to create a new wireless communication network beyond 5G. This is because installing a massive 5G network requires a large investment by network providers. For this reason, the authors propose an alternative beyond 5G that has better quality than 5G and a relatively lower investment value than 5G networks. This study aims to analyze the downlink of the cooperative non-orthogonal multiple access (NOMA) network, which is usually used in 5G, combined with the use of a reconfigurable intelligence surface (RIS) antenna with decode and forward relay mechanisms. RIS is processed with a limited number of objects utilizing Rayleigh fading channels. The scenario is created by a user who relays without a direct link for users near the base station and with a direct link for users far from the base station. Under the Nakagami-m fading channel, the authors carefully evaluated the probability of loss for various users as a function of perfect channel statistical information (p-CSI) utilizing simply a single input-output (SISO) system with a finite number of RIS elements. As a key success metric, the efficiency of the proposed RIS-assisted NOMA transmission mechanism is evaluated through numerical data on the outage probability for each user. The modeling outcomes demonstrate that the RIS-aided NOMA network outperforms the traditional NOMA network

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