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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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Development of Intelligent Marine Logistics Models Using Machine Learning Nguyen, Minh Duc; Nguyen, P. Quy Phong; Luong Ha, Chuc Quynh; Dinh, Xuan Manh; Cao, Van Sam; Dat Do, Hoang
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.9.1.3666

Abstract

This study looks into the development of intelligent maritime logistics models that use machine learning approaches to forecast crucial metrics like fuel usage and port delays. A comprehensive dataset assessed five machine learning models: Linear Regression, Decision Tree, Random Forest, XGBoost, and AdaBoost. Predictive capacities were assessed using key performance measures such as R², MSE, and MAPE. The results show significant heterogeneity in model performance, with Linear Regression attaining a modest test R² of 0.6845 for fuel prediction and 0.8831 for port delay prediction but suffering from high MSE (58745.23) and MAPE (26.90% for fuel). The Decision Tree showed significant overfitting, with a perfect R² (1.000) on training but decreasing to 0.7743 for fuel and 0.9880 for port delay on testing. Random Forest demonstrated balanced performance, with test R² values of 0.7598 for fuel and 0.9548 for port delay. MAPE values were also lower (23.66% for fuel and 5.66% for port delay). The best-performing model was XGBoost, with near-perfect test R² values of 0.7439 for fuel and 0.9880 for port delay, as well as a low MSE (39579.79 for fuel and 0.23 for port delay). AdaBoost produced comparable but somewhat lower results, with test R² values of 0.7188 for fuel and 0.9485 for port delay. These findings demonstrate XGBoost's strength in capturing nonlinear interactions and making solid predictions, whereas ensemble approaches outperform simpler models such as Linear Regression.
Predicting Different Classes of Alzheimer's Disease using Transfer Learning and Ensemble Classifier Tamim, Mubasshar-Ul-Ishraq; Malik, Sumaiya; Sneha, Soily Ghosh; Mahmud, S M Hasan; Goh, Kah Ong Michael; Nandi, Dip
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3038

Abstract

Alzheimer's disease (AD), the most prevalent cause of dementia, affects over 55 million individuals globally. With aging populations, AD cases are expected to increase substantially, presenting a pressing public health challenge. Early diagnosis is crucial but remains challenging, particularly in the mild cognitive impairment stage before extensive neurodegeneration. Existing diagnostic methods often fall short due to the subtle nature of early AD symptoms, highlighting the need for more accurate and efficient approaches. In response to this challenge, we introduce a hybrid framework to enhance the diagnosis of Alzheimer's Disease (AD) across four classes by integrating various deep learning (DL) and machine learning (ML) techniques on an MRI image dataset. We applied multiple preprocessing techniques to the MRI images. Then, the methodology employs three pre-trained convolutional neural networks (CNNs): VGG-16, VGG-19, and MobileNet - each undergoing training under diverse parameter settings through transfer learning to facilitate the extraction of meaningful features from images, utilizing convolution and pooling layers. Subsequently, for feature selection, a decision tree-based RFE method was employed to iteratively select the most significant features and enable more accurate AD classification. Finally, an XGBoost classifier was used to classify the multiclass types of AD under 5-fold cross-validation to assess the performance of our proposed model. The proposed model achieved the highest accuracy of 93% for multiclass classification, indicating that our approach significantly outperforms state-of-the-art methods. This model could apply to clinical applications, marking a significant advancement in AD diagnostics.
Identification Critical Success Factors of Geographic Information System Development in Indonesia with AHP Approach Aini, Nur; Kurniawan, Wawan; Payani, Agnes Sondita; Go, Ratna Yulika
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2033

Abstract

An Indonesian government agency in the field of research is developing a Geographic Information System (GIS) to distribute remote sensing data to customers. To prevent project failure, it is crucial to understand the success criteria related to project objectives and the critical success factors (CSFs), which drive project success. This research identifies these CSFs, enabling organizations to prioritize project success factors. The Analytic Hierarchy Process (AHP) ranks project success criteria and CSFs. The mixed research methodology incorporates qualitative elements through discussions with the project manager to validate the AHP hierarchy structure and quantitative aspects through questionnaires used to calculate weighted priorities using AHP. Results show stakeholder satisfaction and objective achievement as the top-ranked success criteria. The top 5 CSFs identified are team commitment and participation, clear roles and responsibilities, leadership, knowledge management, appropriate tools, infrastructure, and resources.  Based on the success criteria ranking, development should enhance system functionality to maintain user satisfaction and achieve project objectives. Meanwhile, prioritizing human resources and providing adequate resources are crucial based on the identified top 5 CSFs, contributing to increased development success. This outcome aims to assist firms in improving project management and identifying the most critical success elements for GIS development. Furthermore, this research will likely be a learning experience for other government organizations seeking to enhance their information system development efforts.
Understanding Search Behavior in the Simulated Kalman Filter Algorithm Abdul Aziz, Nor Hidayati; Jing Hao, Ooi
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.9.1.3538

Abstract

In computational optimization, metaheuristic algorithms are crucial for solving complex and dynamic problems. It is important to fully understand how an algorithm searches, as it helps to improve the algorithm and its applications in various domains. This paper provides a detailed analysis of how the Simulated Kalman Filter algorithm searches for optimal solutions. The SKF algorithm is an optimization method inspired by the Kalman filter estimation techniques. The algorithm was introduced in 2015 to address unimodal problems. Since its inception, the SKF algorithm has undergone improvements and is used to solve a range of optimization problems. Our study aims to bridge the gap in existing research by investigating how SKF effectively balances the search space exploration and known solution exploitation. Through systematic experimentation using the Brown function as a benchmark, we explored the social dynamics and movement style of the SKF algorithm, in addition to the convergence efficiency and accuracy. When we applied the same approach as suggested in the referenced paper, we gained insights into SKF’s unique strengths and limitations of SKF when compared to other algorithms. The findings illustrate SKF’s unique capabilities in handling the exploration-exploitation trade-off. This study helps to set the foundation for creating more advanced algorithms and optimization strategies in the future. Future research will examine how enhancements to the SKF algorithm impact and enhance its search behavior.
Syllable Segmentation with Vowel Detection on Verse Quranic Recitation Setiyaningsih, Timor; Azmi, Mohd Sanusi; Draman, Azah Kamilah
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2663

Abstract

In speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes.  This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition.
Development of Augmented Reality Based Interactive Learning Media on Electric Motor Installation Subjects Muskhir, Mukhlidi; Luthfi, Afdal; Sidiq, Hazmi; Fadillah, Rahmat
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2256

Abstract

Interactive learning media play an essential role in the learning process. In today's digital era, interactive learning media is one of the best choices for improving the quality of learning by making it more effective and efficient. This study aims to develop augmented reality (AR)--based interactive learning media for electric motor installation subjects. The method used in this research is the research and development (R&D) method with the 4-D development model. This research consists of several stages: defining, designing, developing, and disseminating. Based on the research results, it can be concluded that this augmented reality learning media development research has produced a valid and practical augmented reality learning medium for electric motor installation subjects. The media and material validation results consist of two media validators and two material validators. Obtained a media validation value of 0.58 and a material validation value of 0.62 using the validity interval category ≥ 0.4. Thus, the media and materials developed were declared valid. The augmented reality learning media practicality test results obtained 86.63% by using the practicality interval category > 75%–100%. Thus, AR-based learning media in electric motor installation have been considered practical. Implications for further research could include developing AR media for other subjects or applying AR technology in various learning contexts, including integrating more complex interactive features or adapting the media for broader educational needs. In addition, this research could encourage exploring AR in distance learning scenarios and increase student engagement through gamification or further simulation in skills-based learning.
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.

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