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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 52 Documents
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Search results for , issue "Vol 9, No 1 (2025)" : 52 Documents clear
A New Feature Extraction Approach in Classification for Improving the Accuracy of Proteins Damayanti, -; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Sutyarso, -; Susanto, Gregorius Nugroho; Megawaty, Dyah Ayu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2589

Abstract

Proteins play a vital role in life as essential macromolecules, consisting of linear heteromeric biopolymers formed by amino acids covalently bonded through peptide bonds. They contribute to cell development and bolster the body's defense mechanisms. Post-translational modification processes, such as glycosylation, are necessary for proteins to function optimally. Glycosylation involves adding sugar groups to proteins, playing a critical role in various protein folding processes. Dysregulation of protein glycosylation can lead to diseases like Alzheimer's and cancer. Manual classification of glycosylated proteins is time-consuming, necessitating a faster approach. This study aims to expedite glycosylated protein classification using novel methods like AAindex, CTD, SABLE, hydrophobicity, and PseAAC for increased accuracy, comparing them with existing approaches. The dataset comprises protein sequences sourced from the openly accessible UniProt database. Results demonstrate that glycosylated protein prediction achieved 100% accuracy, surpassing previous approaches. Several features contributed to this improvement, with Hydrophobicity making a significant contribution at 24%, and PseAAC making the most significant contribution at 40% among the five extraction methods developed.
A Framework for Integrated E-notary Services Based on Blockchain for Civil Law Notaries: The Case of Indonesia Putra, Panca O. Hadi; Muda, Iskandar; Bakry, Mohammad Ryan; Yusuf, Chandra; Santosa, Irwan
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3170

Abstract

The trend of digitalization has called for electronic notary services that are both efficient and secure. This study proposes a framework for an integrated e-notary system using blockchain technology based on Indonesia's civil laws and regulations. To accomplish this objective, this study uses a methodology combining both normative legal and information systems methods. This study starts with a search of existing laws and regulations conducted on the Indonesian government regulation database (peraturan.go.id). Subsequently, laws and regulations are analyzed to elicit system components and functional requirements. The findings are visualized using a rich picture, resulting in a framework for an integrated e-notary system. The system entails a blockchain network in which Indonesian registered notaries act as nodes. The proposed system is integrated with other e-government systems to facilitate notarial services as required by laws and regulations, such as document validity checks, electronic recording and storage of notarial deeds, document legalization, and notary protocol archiving. To support the proposed blockchain-based e-notary system, this study suggests several regulatory adjustments based on legal gaps identified using Kostruba’s approach. Regulatory adjustments include creating technical regulations on the establishment of the blockchain network operated by the Indonesian Notary Association (INI) and also the creation and storage of notarial deeds electronically.  The findings imply that the proposed e-notary system has the potential to enhance notary services’ security and efficiency in Indonesia, though successful implementation of such a system may hinge upon the readiness of the stakeholders.
Optimization of Herbal Plant Classification Using Hybrid Method Particle Swarm Optimization With Support Vector Machine Amriana, Amriana; Ilham, Amil Ahmad; Achmad, Andani; Yusran, Yusran
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2576

Abstract

The classification process applied in this study helps identify the many kinds of herbal plants. Herbal plant leaf features are used based on color, shape, and texture. Particle Swarm Optimization and Support Vector Machine (PSO-SVM) hybridization are applied in the classification process to increase classification and identification accuracy. A well-liked metaheuristic approach for solving optimization issues is Particle Swarm Optimization (PSO). Particles look around the search area for the best responses.  A particle swarm is initially initialized randomly within the search area via the PSO algorithm. Every particle's mobility is determined by both its own experience and the experiences of the other particles in the swarm. Each particle keeps track of the best solution it has ever found and the swarm's most extraordinary remedy that has so far been discovered. The Hybrid approach concurrently selects features for the SVM and optimizes its parameters. The kernel function's gamma value non-linearly maps an input space to a high-dimensional feature space. At the same time, the C parameter determines the trade-off between fitting error minimization and model complexity. The Gaussian kernel parameter is set to determine the optimal parameter value of the RBF kernel function. Feature selection solves the issue by eliminating redundant, associated, and irrelevant features. A confusion matrix is utilized in the evaluation to gauge the system's performance. The results demonstrated an improvement in accuracy, with the hybrid PSO-SVM using test data achieving an accuracy of 98% compared to the SVM method, achieving a 91% accuracy.
Design of Automatic Irrigation System For Post-Mining Land Reclamation Sihombing, Ruspita; Azizah, Amiril; Arifin, Zainal; Sari, Wahyuni Eka; Oscar, Agus Wiramsya; Putra, Pandhu Rochman Suosa
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2950

Abstract

post-mining land reclamation poses a challenge in restoring degraded land's ecological function and productivity, requiring optimal rehabilitation to make it productive and environmentally friendly. A key challenge in reclamation is the availability of efficient water sources to support the revegetation process. Conventional irrigation systems are inefficient and require intensive monitoring. Therefore, an innovative solution in the form of an automatic irrigation system is needed to optimize water use and support sustainable plant growth. This study aims to design and develop a technology-based automatic irrigation system that combines soil moisture sensors, water pumps, sprinklers, solar panels, solenoid valves, and microcontrollers to regulate irrigation efficiently and on time. The methodology includes hardware and software design, integration of soil moisture sensors, a microcontroller as the control unit, and system field testing. The system is designed to activate irrigation based on real-time soil moisture levels automatically, ensuring water is only applied when needed. The system is expected to reduce excess water use and improve irrigation effectiveness across large and diverse areas. Results show that this automatic irrigation system can reduce water consumption by 34.2% compared to conventional methods. In addition, farmers can remotely manage irrigation via the Internet or mobile apps, reducing irrigation time by 75 minutes. This system holds the potential to be an innovative and sustainable solution for post-mining land reclamation, ushering in a new era of efficient and sustainable agriculture.
Two-Way Thesis Supervisor Recommendation System Using MapReduce K-Skyband View Queries Dasri, Dasri; Annisa, Annisa; Haryanto, Toto
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2800

Abstract

Timely graduation is an important indicator of the quality of higher education. Yet, many students struggle to complete their studies on time due to challenges in finding relevant research topics and suitable supervisors. This study developed a two-way supervisor recommendation system that considers the preferences and expertise of both students and supervisors. The main contribution of this research is the comparison of Block Nested Loop (BNL) k-skyband and MapReduce k-skyband algorithms. The recommendation model developed in this study uses course syllabi to obtain research topics and academic grades to determine students' interests in research topics. A total of 239 research topics were obtained from 37 courses. Optimal recommendations were achieved with a k value of 16. Implementing the MapReduce algorithm in this recommendation model demonstrated a computation speed 8 times faster than the BNL k-skyband approach, making it effective in handling large datasets. The proposed recommendation system received positive feedback from students, with scores of 3.5 for relevance, 3.7 for topic diversity, 3.4 for serendipity, and 3.5 for novelty. These findings suggest that the proposed recommendation system can support students in their research endeavors and improve the overall supervision process in academic settings, with potential for widespread implementation across various study programs. Thus, contributing to the overall improvement of higher education quality.
Application of Digital Teaching Materials Based on Flipped Learning Model in Civics Education in Elementary School Waldi, Atri; Supendra, Dedi; Rivelia, Katherine Putri; Anggraeni, Aisyah; Febriani, Rika
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2229

Abstract

This research aims to improve students' understanding of Pancasila Student Profile Values through the implementation of Flipped Learning by combining it with digital teaching materials according to the characteristics of students in elementary schools. In addition, this research also aims to create practical digital teaching materials for elementary school students in Padang City on Civic Education learning. This research is a development research using the 4D development model (Define, Design, Develop and Disseminate). This study involved a sample of elementary school students in Padang City who measured the practicality of the developed teaching materials assessed through a structured evaluation process. The results showed a high practicality score of 96%, which categorized the digital teaching materials as very practical for use in the classroom. In addition, researchers also measured the impact of the implementation of these teaching materials on student learning outcomes by obtaining significant results; 87% of students achieved scores above the threshold of completeness, with an average score of 88. The findings suggest that the integration of Flipped Learning with digital teaching materials not only facilitates a deeper understanding of Pancasila values but also positively affects students' overall performance. The implications of this study highlight the potential for further research to explore the long-term effects of digital teaching materials and Flipped Learning on different subjects and levels of education. Future research could also investigate the scalability of these materials in different educational contexts and their effectiveness in fostering critical thinking and civic engagement among students.
A Model for Classification Usability Testing Practically from the Agile Methodology Aspect Salman, Fouad; Baluch, Bakhtawar; Bakar, Zuriana Abu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2459

Abstract

Usability is a crucial feature in the success of software products. Development practitioners know that to preserve and improve the quality of the product and usability has to be carefully considered throughout the development process. The tendency toward empowering users in software development drives the ongoing pursuit of solutions to reconcile agile and usability goals. In this paper, the authors aim to develop a model for classifying usability testing methods according to aspects of agile methodologies. This model can enable agile practitioners to obtain end-user feedback when implementing usability tests at the appropriate time and place during development and thus produce useful and usable software. Mixed methods (qualitative and quantitative) were used in this research to collect primary and secondary data. This research adopted the convenience non-probability sampling technique for evaluating the model.  The evaluation determines whether it could provide valuable information supporting consistent usability tests. The method of performance profiles is also applied in this evaluation to gain accurate results and avoid any biases that might unnecessarily affect the outcomes. The evaluation results were encouraging, and the model showed beneficial effects in integrating usability work into an agile approach, especially since all attributes showed high importance among participants' accepted satisfaction, representing the least essential scale. The developed model must be applied practically to the other integration models in future work. Furthermore, several observation techniques are required to thoroughly cover the integration by software development teams from diverse organizations. 
Robust and Automatic Algorithm for Palmprint ROI Extraction Yousif, Noor A.; Qassir, Samar Amil; George, Dena Nadir
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2801

Abstract

The ridges, creases, wrinkles, and minutiae on the palmprint region of interest (ROI) are important features. These features are employed to confirm or identify an individual. One inevitable issue in the realization of palmprint recognition systems is the extraction procedure of this region under unrestricted environments. The variety in palm sizes, postures, lighting conditions, and backgrounds, however, certainly presents a significant issue. Finding and extracting the palm's area of interest (ROI) will be our main goal. This research introduces a robust automated algorithm based on square construction and each YCbCr color space features. After reading the image of the colored hand, this algorithm goes through two stages. Firstly, convert to the YCbCr color space. This stage guarantees precise locating of the hand region in addition to deleting irrelevant information from the image. Secondly, determining ROI is based on applying three steps: locating three key references, utilizing these key references to construct the main line, and finally, constructing the ROI square. The total color hand images (230) were used to test and evaluate the newly introduced algorithm; 30 were collected from the internet; and 200 were chosen from the Birjand University Mobile Palmprint Database (BMPD). The hand images include two orientations, left and right, varying sizes and backgrounds, uneven illumination, shadows, and some hand images have items on the finger(s). The experimental findings demonstrate that the introduced algorithm effectively attained 100% and 99.565% sensitivity and accuracy, respectively.
Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks Prabowo, Dwi Puji; Rohman, Muhammad Syaifur; Megantara, Rama Aria; Pergiwati, Dewi; Saraswati, Galuh Wilujeng; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar; Andono, Pulung Nurtantio
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2726

Abstract

Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential due to the unique characteristics of these images. The methodology includes testing different augmentation methods, epoch parameters, and CNN parameters on a coral reef image dataset. Five optimization algorithms (AIWPSO, GA, GWO, PSO, and FOX) are compared. The highest accuracy, 95.64%, is achieved at the 10th epoch. AIWPSO and GA show the highest average accuracies, 93.44%, and 93.50%, respectively, with no significant performance differences among the algorithms. Statistical analysis using the Wilcoxon test indicates a significant difference between training and validation accuracy (p-value = 0.0020). These findings underscore the importance of selecting augmentation methods that align with the characteristics of each optimization algorithm to enhance classification performance. The results provide valuable insights into improving the quality and diversity of input data for classification algorithms in underwater image analysis. They highlight the necessity of matching augmentation methods to specific optimization algorithms to boost accuracy and effectiveness significantly. Future research should explore additional augmentation methods and optimization algorithms further to enhance the robustness and accuracy of underwater image classification.
Adaptive Deep Convolution Neural Network for Early Diagnosis of Autism through Combining Personal Characteristic with Eye Tracking Path Imaging Kesavan, Revathi; Palanichamy, Naveen; Haw, Su-Cheng; Ng, Kok-Why
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3046

Abstract

Autism is a large set of illnesses related to brain development, also referred to as autism spectrum disorder (ASD). According to WHO reports, 1 in 100 children is expected to have ASD. Numerous behavioral domains are affected, including linguistic, interpersonal skills, stereotypical and repetitive behaviors which represent an extreme instance of a neurodevelopmental abnormality. Identifying ASD can be difficult and exhausting because its symptoms are remarkably identical to those of many other disorders of the mind. Medical professionals can improve diagnosis efficiency by adapting deep learning practices. In clinics for autism spectrum disorders, eye-tracking scan pathways (ETSP) have become a more common instrument. This approach uses quantitative eye movement analysis to study attentional processes, and it exhibits promising results in the development of indicators that can be used in clinical studies for autism.   ASD can be identified by comparing the abnormal attention span patterns of children’s having the disorder to the children’s who are typically developing. The recommended model makes use of two publicly viable datasets, namely ABIDE and ETSP imaging. The proposed deep convolutional network consists of four hidden convolution layers and uses 5-fold cross-validation strategy. The performance of the proposed model is validated against multilayer perceptron (MLP) and conventional machine learning classifiers like decision tree (DT), k-nearest neighbor (KNN) and Random Forest (RF) using metrics like sensitivity, specificity and area under curve (AUC). The findings demonstrated that without the need for human assistance, the suggested model is capable of correctly identifying children with ASD.

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