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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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Processing Plant Diseases Using Transformer Model Marcus Lye, Hong Zheng; Ng, Kok Why
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2291

Abstract

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.
Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods Rangkuti, Abdul Haris; Hasbi Athala, Varyl; Haridhi Indallah, Farrel; Tanuar, Evawaty; Muliadi Kerta, Johan
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1359

Abstract

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.
A Prototype of Decentralized Applications (DApps) Population Management System Based on Blockchain and Smart Contract Saian, Septovan Dwi Suputra; Sembiring, Irwan; Manongga, Daniel H. F.
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1861

Abstract

The Indonesian population reached 270,20 million in 2020. Each resident is equipped with various secret identities. The COVID-19 pandemic has made all activities use technology as a basis, causing residents' identities to be stored digitally. Some applications that keep these identities experience data leaks. However, with the advent of Web3 and its emphasis on decentralization through blockchain, a new era of secure data management is possible. Blockchain, with its inherent security features, ensures that data stored is secure, difficult to damage or lose due to mutual consensus. Every transaction is recorded, making it easy to carry out the audit process. Therefore, this research will design and implement prototype dApps for secure population management, leveraging the superior security of blockchain technology. The initial stage of research is to conduct a literature study. Furthermore, it is to create designs such as system, infrastructure, and activity diagrams. Then do the development of the dApps prototype. The last is testing using OWASP ZAP and cost analysis. A dApps prototype was implemented on a blockchain. Every transaction is recorded and publicly viewable through the Etherscan platform. Other data stored on a blockchain have gone through an AES-256 encryption process with the data owner's account key so that the owner can only see the data. The results of the tests performed show that there is no high-level warning. The cost analysis results show that the most used costs are when deploying smart contracts and making new data. For further development, it is implementing permissionless blockchain and multi-accounts.
Application-Level Caching Approach Based on Enhanced Aging Factor and Pearson Correlation Coefficient Zulfa, Mulki Indana; Maryani, Sri; Ardiansyah, -; Widiyaningtyas, Triyanna; Ali, Waleed
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2143

Abstract

Relational database management systems (RDBMS) have long served as the fundamental infrastructure for web applications. Relatively slow access speeds characterize an RDBMS because its data is stored on a disk. This RDBMS weakness can be overcome using an in-memory database (IMDB). Each query result can be stored in the IMDB to accelerate future access. However, due to the limited capacity of the server cache in the IMDB, an appropriate data priority assessment mechanism needs to be developed. This paper presents a similar cache framework that considers four data vectors, namely the data size, timestamp, aging factor, and controller access statistics for each web page, which serve as the foundation elements for determining the replacement policy whenever there is a change in the content of the server cache. The proposed similarCache employs the Pearson correlation coefficient to quantify the similarity levels among the cached data in the server cache. The lowest Pearson correlation coefficients cached data are the first to be evicted from the memory. The proposed similarCache was empirically evaluated based on simulations conducted on four IRcache datasets. The simulation outcomes revealed that the data access patterns, and the configuration of the allocated memory cache significantly influenced the hit ratio performance. In particular, the simulations on the SV dataset with the most minor memory space configuration exhibited a 2.33% and 1% superiority over the SIZE and FIFO algorithms, respectively. Future tasks include building a cache that can adapt to data access patterns by determining the standard deviation. The proposed similarCache should raise the Pearson coefficient for often available data to the same level as most accessed data in exceptional cases.
Feature Minimization for Diabetic Disorders High Performances Prediction System-based on Random Forest Tree Mohammed, Sahar Jasim; Ahmed, Ali Mohammed Saleh; Mohammed, Mohammed Sami
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1868

Abstract

Human organ failure due to high blood sugar is considered a chronic disease. Early prediction might reduce or prevent complications due to such disorders, especially with recent machine-learning improvement techniques and the availability of electronic data from different sources. The number of diabetic patients roughly increased and may reach more than 600 million by twenty years. Transforming data into valuable and helpful information is an effort for researchers to improve the performance of ML techniques. This paper applies several types of sampling to predict 1000 samples with attributes and three diabetes class types (Random Forest tree, Hoeffding tree, LWL, NB updatable, and support vector Machine). This paper focused on most parameters that affected overall prediction accuracy. ML performances have been measured depending on the accuracy and mean absolute error for several cross-validation values before Feature reduction and after feature minimization by applying feature selection methods. It shows that Gender, Age, Blood Sugar Level (HbA1c), Triglycerides (TG), and Body Mass Index (BMI) are the most impact attributes applied. It is also shown that the Random Forest tree was the best method (97.7 and 98.6 %) with and without feature minimization, respectively, but it has a higher performance by omitting some unbalanced features from the diabetic dataset. Weight minimization has also been applied to techniques like SVM to obtain a better-searching plane and a robust model. In addition, this study specifies which parameters have weight minimization with the required analysis. Also, the feature selection method was applied to gain memory and reduce time.
The E-govqual and Importance Performance Analysis (IPA) Models Analysis: Review a Web Service Quality of E-government Yuhefizar, Y.; Utami, Devi; Sudiman, Josephine
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1196

Abstract

The e-government web portal serves as a crucial platform for providing information that can be easily and universally accessed. This serves as an intermediary between the municipal administration and the community, ultimately resulting in improved and streamlined public services. Several variables, including age and proficiency in using the integrated system, provide insights on how to gauge user satisfaction levels of the information system and its current users. The e-govqual and Importance Performance Analysis (IPA) models are accurate indicators of user satisfaction. This article seeks to understand the perception of users of the e-government web portal of the municipal Tanah Datar municipality. It aims to compare the servqual and the IPA model to determine the most suitable method for assessing public perceptions and identifying priority attributes to improve service quality. These two approaches share the same objective, but employ different methodologies. The user's perception of performance is designated as the independent variable (X) using a quantitative approach, while service quality expectations are designated as the dependent variable (Y). This is achieved by combining the Likert scale with five dimensions. This study uses questionnaires to gather data from 275 participants and uses two models, E-govqual and Importance Performance Analysis (IPA), to assess user satisfaction. The findings indicate that it is crucial for the government to respond quickly to user issues, provide feedback on user input, and regularly update the material on the Web portal.
Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin Zulkiflee, Nurul Najihah; Mohd Safar, Noor Zuraidin; Kamaludin, Hazalila; Jofri, Muhamad Hanif; Kamarudin, Noraziahtulhidayu; Rasyidah, -
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2704

Abstract

This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology.
Concept and Design of Anthropomorphic Robot Hand with a Finger Movement Mechanism based on a Lever for Humanoid Robot T-FLoW 3.0 Apriandy, Kevin Ilham; Ulurrasyadi, Faiz; Dewanto, Raden Sanggar; Dewantara, Bima Sena Bayu; Pramadihanto, Dadet
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1793

Abstract

This work described a concept and design of an anthropomorphic robot hand for the T-FLoW 3.0 humanoid robot, which featured a mechanism based on a lever as its finger movement. This work aimed to provide an affordable, modular, lightweight, human-like robot hand with a mechanism that minimizes mechanical slippage. The proposed mechanism works based on the push/pull of a lever attached to the finger to generate its finger flexion/extension movement. The finger’s lever is pushed/pulled through a servo horn and a rigid bar by the affordable TowerPro MG90S micro-servo. Our hand is developed only as necessary to become close to human hands by only applying five fingers and six joints, where each joint has its actuator. The combination of 3D printing technology with PLA filament accelerates and streamlines the manufacturing process, provides a realistic appearance, and achieves a lightweight, affordable, and easy maintenance product. Structural analysis simulations show that our finger design constructed with PLA material could withstand a load of about 30 N. We verified our finger mechanism by repeatedly flexing and extending the finger 30 times, and the results showed that the finger movements could be performed well. Our hand offered excellent handling for the mechanical issues brought on by finger movements, one of the issues that robot hand researchers have encountered. Our work could provide significant benefits to the T-FLoW 3.0 developers in enhancing the ability of humanoid robots involving hands, such as grasping and manipulating objects.
Asana and Trello: A Comparative Assessment of Project Management Capabilities Kamila, Jihan Syafa; Marzuq, Muhammad Falah
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2595

Abstract

Project management tools are essential for streamlining project management activities and providing a variety of functionalities to assist organizations in executing projects efficiently. The selection of an appropriate tool is crucial, given the many options available in the market. This scholarly article employs a comparative analysis methodology to scrutinize two prominent project management tools, Asana and Trello. The aim is to assist companies and organizations in making informed decisions based on their specific needs. The comparative analysis delves into the strengths and weaknesses of Asana and Trello, assessing their features, functionalities, and suitability in the context of knowledge management areas. Both tools are evaluated for their capability to address project management challenges and improve organizational processes. The study concludes that the choice between Asana and Trello hinges on factors such as project scale, organizational requirements, and the preferred level of complexity. With its comprehensive features, Asana emerges as ideal for larger, agile-oriented projects. In contrast, Trello's simplicity and user-friendly interface suit relatively smaller projects well. This analysis provides valuable insights for organizations to align their project management tools with specific project conditions, facilitating optimising project execution processes to meet their unique goals and requirements. In terms of features, Asana outshines Trello by providing a more extensive range of functionalities that effectively support the mapping of knowledge management areas.
Convolutional Neural Network Model for Sex Determination Using Femur Bones Nasien, Dewi; Adiya, M. Hasmil; Afrianty, Iis; Farkhan, Mochammad; Butar-Butar, Rio Juan Hendri
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1711

Abstract

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes.

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