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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 33 Documents
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Search results for , issue "Vol 9, No 6 (2025)" : 33 Documents clear
Speech Emotion Recognition of Indonesian Movies by Using Convolutional Neural Network Santoso, Tri Budi; Khoirotul Aini, Yulistia; Dutono, Titon
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3552

Abstract

Speech emotional recognition (SER) is one of the interesting research areas of human-computer interaction (HCI) systems. The objective of this study is to provide a basis for a basic model of Indonesian-language speech emotional recognition, which is achieved by utilizing dialogues from an Indonesian-language movie.  The process began by developing a dataset from film dialogue and grouping it into four emotion classes: angry, happy, neutral, and sad. The development of the datasheet produced 5049 data points consisting of 1202 for anger, 1228 for happy, 2075 for neutral, and 899 for sad. This study uses the Mel-frequency cepstral coefficients (MFCC) method to analyze audio features from Indonesian-language movies and employs a Convolutional Neural Network (CNN) for clustering. The process began with MFCC feature extraction. During training, an accuracy of 85.85% was achieved, and during testing, 83.35%. Based on a series of tests carried out with various improvements to the previous process, a description of this system's behavior is obtained from a confusion matrix. Angry, happy, and sad expressions are easier to recognize than neutral expressions. The behavior of neutral expressions is flat in energy levels and other features. In the future, we hope it can be developed into a cross-corpus model and applied to speakers from various cultures.
Cardio-Respiratory Motion Prediction Analysis: A Systematic Mapping Study Mohd Fuaad, Nur Atiqah; Hassan, Rohayanti; Ahmad, Johanna; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4814

Abstract

Cardio-respiratory motion prediction analysis is a crucial medical application for enhancing the precision and effectiveness of medical imaging and patient diagnosis, particularly in the cardiac and respiratory context. This systematic mapping study reviews 23 selected research papers to provide a comprehensive overview of emerging trends and future directions in the field, which also highlights challenges and limitations frequently encountered in cardio-respiratory motion prediction and identifies key machine learning, deep learning, and computational paradigm methodologies examining their application frequencies. In addition, the study analyses the number of performance metrics used alongside validation techniques, which are essential for assessing the accuracy and reliability of the predictive models. Furthermore, it explores the most utilized data types and imaging modalities in this domain, such as X-ray, CT, MRI, and ultrasound, discussing their respective advantages and limitations. Ethical considerations, including patient privacy, data security, informed consent, and the potential for bias, are also addressed. This study aims to deepen the understanding of the landscape of cardio-respiratory motion prediction, guiding future research and the development of more effective, reliable predictive models to enhance medical imaging and patient care, providing valuable insights for researchers, practitioners, and technologists in the field.
Patterned Dataset Model Optimization to Predict Bitcoin IDR Price using Long Short Term Memory Parlika, Rizky; Isnanto, R Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4036

Abstract

The goal of this study was to determine the optimal combination for optimizing the Patterned Dataset Model, particularly in patterned datasets during periods of price decline (crash).  In previous research, the Crash Patterned Dataset has been shown to predict the next Bitcoin price. In this study, an experiment was conducted using a combination of prediction models, including ARIMA, machine learning, and deep learning. This research was conducted in 3 stages. The first stage is to compare the error results from the Bitcoin pair IDR crypto asset prediction process, which are part of the stored data from the patterned dataset under crash conditions. This dataset was tested with several prediction models, and the LSTM model with 60 seconds of resampling produced the best results, with an MAPE of 0.19%. In the second stage, BTCIDR, as part of the data from the patterned dataset in crash conditions, was resampled with variants 1D, 2D, 3D, 4D, 5D, 6D, 7D, 1H, 2H, 3H, 4H, 5H, 6H, 7H, 8H, 9H, 10H, 11H, and 12H. The result is that BTCIDR with a 3H resample has the lowest MAPE, at 1.3%. In the third stage, the prediction process is carried out using the LSTM model on the BTC IDR test dataset (as part of the Patterned Dataset in crash conditions) with a 3H resample. The dataset range is from May 2022 to 2025-01-23 11:05:48. This test predicts the Bitcoin IDR price series for the next 30 days, calculates the MAPE between the predicted series and the actual BTC IDR dataset 30 days later, and evaluates the results. The MAPE value for the Bitcoin IDR price prediction was 9.27%. This indicates that the average prediction error against the actual price is around 9.27%. The main objective of this research is to more accurately predict the price of the Bitcoin-IDR pair, providing additional helpful information for trading cryptocurrencies.
Classification of Rice Disease Using Deep Learning Object Detection Yolov8 Dwi Satoto, Budi; Rosa Anamisa, Devie; Yusuf, Muhammad; Kautsar Sophan, Mohammad; Kembang Hapsari, Rinci; Irmawati, Budi; Arrova Dewi, Deshinta
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3578

Abstract

Rice plant pests and diseases are among the primary threats to agricultural production, particularly in rice-growing regions, which can result in a significant decrease in crop yields and food production. Therefore, technology is essential for accurately detecting and classifying pests and diseases. In this research, the author proposes using deep learning-based object detection for moving objects. This is because observations are made on relatively large land areas. Images are captured by drone cameras as videos, which are then used to create ground-truth markers and identification targets during training. YOLO v8 is the latest object detection model on moving media. This model offers advantages in speed and accuracy, making it well-suited for applications that require precise results on agricultural land. The dataset comprises videos of rice plants infested with pests and diseases. After completing labeling and training, the YOLO v8 model can detect and classify pests and diseases in real time using markers in the form of frames with identification labels. Farmers can identify pest and disease attacks earlier by implementing this system, enabling more effective, timely pest control measures. The study's results showed that the training accuracy was 91.5%. The F1-Confidence measurement value obtained was 0.84, the Precision-Recall Curve was 0.891, and the Recall Confidence Curve was 0.97. The trial results, based on experimental data, achieved confidence accuracy of 80% to 95%.
Comparative Study of two Region-based Detection Models, Faster R-CNN and R-FCN in detecting Smoke Region from Several Environmental Conditions Benta Hasan, Sumayea; Rahman, Shakila; Khaliluzzaman, Md; Binti Abdul Aziz, Nor Hidayati; Jakir Hossen, Md
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3764

Abstract

Detecting smoke covering different environmental situations is an explanatory task while mitigating the loss of lives or reducing the fire-based disaster. Various approaches were experimented with to tackle this task. A few of them are convenient, whereas many fail in outcomes. To that end, this research presents a comparative analysis of two region-based detection models based on deep neural networks. Faster R-CNN denotes the Region-based Convolutional Neural Network, and R-FCN, which constitutes the Region-based Fully Convolutional Network, is employed in this research to evaluate detection performance for the smoke detection task. The analysis demonstrates these models with respect to detection accuracy, precision, recall, Intersection over Union, detection speed, and resilience to challenging conditions (e.g., variations in lighting, weather, and complex backgrounds). Research results highlight Faster R-CNN's accuracy, which supports applications in fire-smoke prevention, whereas R-FCN focuses on detection speed, which is relevant to the smoke-monitoring sector. The key issue to consider is the trade-off between computational efficiency and detection accuracy. Performance in extreme environmental conditions can be enhanced through further advances with regard to data variability and typical challenging scenarios. Moreover, Faster R-CNN and R-FCN achieved Precision values of 96.72% and 95.46%, Recall values of 97.66% and 95.73%, and F1-Score values of 97.18% and 95.59%, respectively. This study assumes further assessments to ensure the safety of the living.
Towards an Integrated Decision Support System for the Evaluation Data Mining Tools in Economic Intelligence System Ali Salah, Hussein; Shihab Ahmed, Ahmed; Bhar Layeb, Safa
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3635

Abstract

Information is very important in the economic world because it affects how industries make choices in today's fast-growing economy. It's essential to learn how to find valuable knowledge for the company's operations quickly. The goal of this work is to create a database of socioeconomic intelligence, operationalize it as a system, and use the study's results to make better decisions. Building economic understanding processes requires research into economic analysis algorithms and the development of computational representations for financial systems. This information is used to construct knowledge item architecture for financial reasoning systems. This study employs data mining methods to assess and extract relationships among dataset elements. Association rules and forecasting techniques are used to quickly and accurately retrieve relevant data for the financial intelligence sector. The research examines the application of financial intelligence mechanisms via data mining methodologies. The article discusses the dataset and reveals that the suggested algorithm's classification accuracy surpasses that of the Logistic Regression (LR) technique by 2.76%. This illustrates the efficacy of the devised system in obtaining and analyzing economic intelligence data. Research on sophisticated algorithms and their use in financial intelligence platforms could improve the precision and effectiveness of data collection and analysis. The results of this study provide a basis for enhancing financial decision-making and underscore the potential for further innovation in this field.
Artificial Intelligence: Creating a Hyper-personalization Artifact Murugasu, Umapathy Sivan G; Subbarao, Anusuyah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3367

Abstract

This research aimed at synthesizing an artifact that could hyper-personalize customers’ needs using a design science framework. Artificial intelligence (AI) was applied to enable telecommunication (Telco) businesses to offer hyper-personalized products and services, based on a database of customers’ digital demography. A digital demographic database was created using data collected on attributes derived from a systematic literature review. The proof of concept (POC) was developed using the Waikato Environment for Knowledge Analysis (WEKA) software. A systematic literature review was conducted to identify documents used in creating a cross-tabulation of attributes through thematic analysis. This analysis resulted in 32 attributes. The Delphi method for consensus reaching by 10 industry experts was used to reduce to 12 attributes in 2 stages. These attributes were structured into a Google Form to collect customer usage data. Outlier data were removed using multivariable outlier detection by Mahalanobis distance available via SPSS version 21. Using the updated database, several procedures were conducted to determine the best artificial intelligence algorithm for subsequent analysis. Using the Logistic Model Tree algorithm and the customer digital demography, the Telco offering for the customers was predicted with 97.6% accuracy. The artifact created was named Hypersona. The theoretical contribution lies in the applicability of real-time identification of client requirements, targeted client classification, and the ability to offer hyper-personalized products. Implications for Further Research: This research highlights the potential of AI-driven hyper-personalization in the telecommunications sector. Future studies could explore scaling the artifact across diverse businesses.
NeuraWheel: A Synergistic Approach with Deep Learning Models and Curated Dataset Mahendrada, Vamsi sravanth; Parameswaran, Murali; Parameswaran, Seetha
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2771

Abstract

This research addresses a critical aspect of automotive safety by developing an advanced tire defect classification model to enhance tire maintenance practices and reduce tire-related accidents. The primary objective is to leverage the power of deep learning to accurately distinguish between good and defective or worn-out tires, which is vital for ensuring road safety. The study utilizes a comprehensive dataset encompassing various tire conditions to train and evaluate six prominent deep learning models—EfficientNet, InceptionNet, VGG19, DenseNet121, DenseNet201, and ResNet101—as well as three lightweight models—SqueezeNet, MobileNet V2, and MobileNet V3. Customised Neurawheel models are also introduced and specifically designed for this task. Employing state-of-the-art deep learning and image processing techniques, the models were rigorously trained and tested to ensure high accuracy in classification tasks. Among the models tested, Neurawheel-4j emerges as the top performer, achieving an impressive accuracy rate of 98.44%, significantly outperforming ResNet101 and other models. The research highlights the effectiveness of sophisticated model architectures, rigorous dataset curation, and optimized training configurations, underscoring the potential for these models to be deployed in real-world applications. The implications of this study are profound, as the deployment of such a model in real-world scenarios could dramatically reduce tire-related accidents, contributing to the broader goal of enhancing road safety. Future research should focus on expanding the dataset to include a wider range of real-world scenarios, exploring additional metrics to assess tire wear severity, and integrating the model with IoT-based systems for real-time tire monitoring. This study lays the foundation for further advancements in tire defect classification and automotive safety.
Multi Criteria Decision Making Method For Developing Smart Indonesia Program Scholarship Recipient Candidate System Supriyanta, Supriyanta; Sutanto, Yusuf; Susilo, Dahlan; Setyadi, Heribertus Ary; Syukron, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3706

Abstract

The Government of Indonesia is continuously striving to improve its education quality with the provision of scholarship programs, one of which is the Smart Indonesia Program (SIP). Students' interest in obtaining SIPs is increasing, but the selection process still relies on conventional methods. Without adequate IT support, the selection process for SIP scholarship candidates will be complex, less objective, and somewhat unfair. State Vocational High School (SVHS) 5 Surakarta was selected as a case study for this research to establish the selection process and the data collection methods used in previous years. The research aims to develop a Decision Support System (DSS) to assist in nominating students deemed eligible for SIP scholarship recommendations. The applied methods include Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA). Four criteria have been set in this DSS: card ownership status, total parental income, household income, and number of siblings. Each of which is further broken down into several sub-criteria and assigned a value for use in the AHP process. Upon comparing data from 2021 to 2023, it was found that the accuracy in 2021 was 92.9%, in 2022 it reached 94.7%, and in 2023 it recorded 92.3%. Based on the results of this system accuracy test, it can be concluded that the AHP and MOORA methods can be used to objectively produce recommendations for students eligible for SIP scholarships, based on the input criteria.
Enhancing User Experience through UI Redesign Using the UEQ+ Method Setiawan, Wahyu Fajar; Amirullah, Afif; Rochimah, Siti
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3596

Abstract

This research redesigned the User Interface (UI) of the XYZ e-wallet application, applying the User Experience Questionnaire Plus (UEQ+) testing method within the Design Thinking framework. This research contributes to the field by addressing the absence of comprehensive UI/UX evaluation in financial technology applications through an iterative design methodology. Initial UEQ+ assessment utilizing nine user experience questionnaire scales revealed significant usability issues, with intuitive use scoring 2.32 and clarity scoring 2.60, indicating substantial potential for improvement. The five stages of Design Thinking (Empathize, Define, Ideate, Prototype, Test) were systematically applied to solve the identified problems. Interactive prototyping in Figma facilitated real user testing of critical features, including the homepage, QRIS Payment, and History & Transfer notify. Post-redesign, there were significant increases in intuitive use (from 2.34 to 3.91; 67.1%), clarity (from 2.90 to 4.33; 49.3%), efficiency (from 3.25 to 4.44; 36.6%), trust metrics (from 3.41 to 4.51; 32.3%), and content quality (from 3.07 to 4.34; 41.4%). The statistical validation yielded a Cronbach’s Alpha of 0.965, indicating excellent reliability of the measurement. The high relationship among the factors (0.313-0.960) reflects a broad improvement. This study introduces the first empirically validated model that combines UEQ+ evaluation with Design Thinking for e-wallet applications, offering evidence-based UI/UX design guidelines for fintech, particularly valuable for Indonesian and similar developing markets where trust critically affects adoption.

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