cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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
 114   115   116   117   118 
Extraction Model for Musical Elements of Javanese Traditional Songs from Gendhing Music Sheets based on Kepatihan Notation Kurniawati, Arik; Arrova Dewi, Deshinta; Satria Erlangga, Bima; Damayanti, Fitri; Oktavia Suzanti, Ika
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.9.6.3573

Abstract

Traditional Javanese gamelan music, particularly its songs, is an integral part of Indonesian culture and identity. However, gamelan music notation remains manual, disorganized, and difficult to access. This poses challenges to balanced education, community sustainability, and digital preservation. This study introduces an automated data extraction and gamelan notation transcription process for transforming Javanese gamelan notation in PDF format into a structured CSV. The innovation process involves parsing PDF-based Kepatihan notation, symbol-to-number conversion, musical section recognition (e.g., buka, lagu, suwuk), and organization in gatra units—each of four melodic notes. The process produces detailed metadata, such as song title, tuning (laras), mode (pathet), and gendhing classification. To evaluate extraction accuracy, the validation period also included a comparison of the converted gatra with the original PDF. The results show that the system achieved 100% accuracy on a sample size of 10 gatra and reduced processing time by 97.5% compared with manual methods. The completed dataset consists of 31 gendhing songs, providing an analyzable and scalable collection for future musicological research and education training. This study contributes to the fields of Music Information Retrieval (MIR) and Digital Humanities by enabling the efficient, standardized digitization of historical music notation. This structured dataset empowers the development of automatic notation generators, making inclusive learning tools accessible to novices and facilitating the documentation of cultural heritage through technology.
Attention Mechanism with Kalman Smoothing Improved Long Short-Term Memory Mechanism for Obesity Weight Forecasting Pranolo, Andri; Utami, Nurul Putrie; Anasyua, Fairuz Khairunnisa
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.9.6.4633

Abstract

This study aims to evaluate and compare the performance of several variants of Long Short-Term Memory (LSTM) based models in predicting obesity weight data. The main contribution of this research was to perform an extensive assessment of the effectiveness of LSTM-based models, including the combination of Attention-LSTM with Kalman Smoothing (KS), using two different data normalization methods (Z-score and Min-Max). This research used a publicly available dataset on obesity levels based on eating habits and physical condition, available at the UCI Machine Learning Repository. The models evaluated include the standard LSTM, Attention-LSTM, KS-LSTM, and the proposed KS-Attention-LSTM. The evaluation is conducted using the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results showed that the proposed KS-Attention-LSTM model with Min-Max normalization achieved the lowest MAPE (0.28372) and the highest R² (0.79527) among the models. This suggests that the proposed model offers advantages in terms of prediction accuracy and has a good ability to handle data variations. Therefore, the KS-Attention-LSTM model with Min-Max normalization is strongly recommended for practical implementation, particularly for time-series data prediction in the health sector. This research is beneficial and contributes an effective alternative model that improves prediction accuracy, supports decision-making in the health sector, and enriches forecasting methods. 
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%.
Classification of Defect Photovoltaic Panel Images Using Matrox Imaging Library for Machine Vision Application Othman, Nur Syahiera; Ramli, Suzaimah; Kamarudin, Nur Diyana; Mohamad, Ahmad Umaer; Ong, Ang Teoh
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2182

Abstract

The maintenance of large-scale photovoltaic (PV) power plants has long been a challenging task. Currently, monitoring is carried out using electrical performance measurements or image processing, which have limited ability to detect faults, are time-consuming and costly, and cannot pinpoint the defect's precise location quickly. To address these challenges, this research focused on using deep learning techniques to classify defect and non-defect PV panels. The application provided deep learning algorithms capable of image classification in various classifiers. The image dataset was carefully curated and split into training and development datasets during the training model to ensure the highest accuracy for the prediction of the presence or absence of defects on the PV panel. Statistical measures, which are the average accuracy for the training model and average prediction, were employed to evaluate the classification performance of the defect PV panel model. The results demonstrated a remarkable total accuracy of model 99.9% for each class, and prediction results showed that almost 70% of defect PV panels were detected from the testing dataset. Furthermore, a comparative analysis was conducted to benchmark the findings against other algorithms. The practical implications of this research are significant, showcasing the effectiveness of deep learning algorithms and their compatibility with machine vision applications for the classification of defect PV panel images. By leveraging these techniques, solar farm operators can significantly improve maintenance management, thereby enhancing the efficiency and reliability of solar power generation and potentially saving significant costs.
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.
Modeling, Implementing and Evaluation a Decision Support System Used for Choosing the Best HVAC System in The Buildings, Case Study in Iraq Ahmed, Ahmed Shihab; Ali Salah, Hussein
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.8.1.1947

Abstract

The life cycle cost of a building is affected by the heating, ventilation, and air conditioning (HVAC) system chosen by the Life Cycle Costs (LCC). Quality, constructability, appearance of the structure's interior and exterior, HVAC size and weight, and LCC are some of the criteria influencing the choice. Methods: To monitor a project's progress based on energy savings, standard measures such as cost variance (CV) and schedule variation have used an idea when tracking the performance of intelligent buildings. Also, as described in the article, this research compared the decision-making limits of Building Information Modelling (BIM) and (MCDM). Analysis: The conventional approach cannot reveal information regarding divergence from the expected level of performance. Based on the outcomes of the construction cost variables, the key finding was the observation of 12 efficient elements. Finding and Novelty: According to the R, a building's most valuable features are its (Energy Saving Features, Warranties, Budget, Protect Your Unit, SEER Ratings, and Home Square Footage). The findings of Actual value (AV) and planned value (PV) were significantly different, as noted by the Multi-Criteria Decision Maker (MCDM). The new method also makes it possible to track project costs and timetables more accurately. The paper will characterize the HVAC Decision Support System's architecture (HVACDSS). Also, a case study of action modeling is provided, and the preliminary findings are addressed. Six criteria characteristics are used by the HVACDSS technique by an analysis of building construction conducted using the WEKA mining tool (decision tree).
Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier Liman, Muhamad Arief; Josef, Antonio; Kusuma, Gede Putra
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.8.1.1707

Abstract

Handwritten character recognition is a problem that has been worked on for many mainstream languages. Handwritten letter recognition has been proven to achieve promising results. Several studies using deep learning models have been conducted to achieve better accuracies. In this paper, the authors conducted two experiments on the EMNIST Letters dataset: Wavemix-Lite and CoAtNet. The Wavemix-Lite model uses Two-Dimensional Discrete Wavelet Transform Level 1 to reduce the parameters and speed up the runtime. The CoAtNet is a combined model of CNN and Visual Transformer where the image is broken down into fixed-size patches. The feature extraction part of the model is used to embed the input image into a feature vector. From those two models, the authors hooked the value of the features of the Global Average Pool layer using EMNIST Letters data. The features hooked from the training results of the two models, such as SVM, Random Forest, and XGBoost models, were used to train the machine learning classifier. The experiments conducted by the authors show that the best machine-learning model is the Random Forest, with 96.03% accuracy using the Wavemix-Lite model and 97.90% accuracy using the CoAtNet model. These results showcased the benefit of using a machine learning model for classifying image features that are extracted using a deep learning model.

Page 116 of 118 | Total Record : 1172

 114   115   116   117   118 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue