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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 1,172 Documents
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A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm Noaman, Salam Abdulkhaleq; Ahmed, Ali Mohammed Saleh; Salman, Aseel Dawod
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.7.4.1865

Abstract

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level
Development of Automatic Object Detection and IoT for Garbage Pickup Assignment Problem Bayu Setyawan, Erlangga; Novitasari, Nia; Zahira, Aulia Dihas
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.8.2.2740

Abstract

Waste management remains a challenge in certain cities, particularly in allocating fleets responsible for collecting garbage from temporary disposal sites. Inadequate planning can lead to the accumulation of substantial waste piles. This study aims to enhance truck assignment by considering truck capacity and the collection route. The assignment process incorporates the fundamental concept of the transportation problem, precisely the northwest corner method. The volume of waste transported aligns with the resident or industrial population within the designated service area. The waste generation capacity determines the future fleet and quantity, forming a crucial element of the ensuing distribution channel. A monitoring system integrating object detection and the Internet of Things (IoT) has been devised to ensure effective garbage collection. Cameras strategically positioned at temporary disposal sites transmit real-time images. The system evaluates garbage collection capacity through object detection facilitated by neural network training. The research outcomes demonstrate the system's capability to identify waste pile levels and validate the garbage pickup process by the designated fleet. Future research should focus on assignment and scheduling in waste transportation, enabling fleet allocation within specific timeframes. Additionally, an object detection algorithm refinement is necessary for more precise identification of waste pile locations.
Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions Tan, Shao Xian; Ong, Jia You; Goh, Kah Ong Michael; Tee, Connie
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.8.1.2313

Abstract

In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. This study proposes techniques to enhance vehicle object detection and classification using augmentation methods based on the YOLO (You Only Look Once) network. The primary objective of the trained model is to generate a local vehicle detection system for Malaysia which have the capacity to detect vehicles manufactured in Malaysia, adapt to the specific environmental factors in Malaysia, and accommodate varying lighting conditions prevalent in Malaysia. The dataset used for this paper to develop and evaluate the proposed system was provided by a highway company, which captured a comprehensive top-down view of the highway using a surveillance camera. Rigorous manual annotation was employed to ensure accurate annotations within the dataset. Various image augmentation techniques were also applied to enhance the dataset's diversity and improve the system's robustness. Experiments were conducted using different versions of the YOLO network, such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8, each with varying hyperparameter settings. These experiments aimed to identify the optimal configuration for the given dataset. The experimental results demonstrated the superiority of YOLOv8 over other YOLO versions, achieving an impressive mean average precision of 97.9% for vehicle detection. Moreover, data augmentation effectively solves the issues of overfitting and data imbalance while providing diverse perspectives in the dataset. Future research can focus on optimizing computational efficiency for real-time applications and large-scale deployments.
A Mixed Integer Linear Programming for Exam-Invigilator Assignment Problem: A Case Study at Universiti Pertahanan Nasional Malaysia Irfan Hanafi, Muhammad Aiman; Syed Ali, Sharifah Aishah; Mat Jusoh, Ruzanna; Ali, Fazilatulaili; Abd Rahman, Norzaura
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.8.1.2196

Abstract

The assignment of invigilators for examinations is a complex and challenging task, particularly when faced with numerous factors that must be carefully considered. Critical elements are essential in this process, including staff availability, room capacity, and time constraints, requiring thorough evaluation and coordination. This paper focuses on improving the allocation of invigilators for examinations at Universiti Pertahanan Nasional Malaysia (UPNM). The issue arises when academic staff members responsible for teaching the subject are also assigned as exam invigilators, which conflicts with their primary role of assisting students in addressing their queries during examinations. It is essential to reconsider the distribution of invigilator roles, ensuring that academic staff members can focus solely on providing educational support. In contrast, qualified non-academic staff handle invigilation duties effectively. A mixed-integer linear programming (MILP) model is formulated using the existing examination timetable to solve this problem. The model is solved using a simple algorithm implemented in the XPress MP programming language, resulting in an improved solution that requires less computational effort than the conventional method. This approach offers an alternative and better solution for scheduling examination invigilators at UPNM, ensuring the efficient and effective management of exam procedures while maximizing the utilization of available resources. It can serve as a starting point for future investigations into UPNM's scheduling procedures.
Analyzing the Impact of Project-Based Learning on Student Entrepreneurship Readiness: A Structural Equation Modeling and Statistical Analysis in Higher Education Yulastri, Asmar; Ganefri, Ganefri; Ferdian, Feri; Elfizon, Elfizon
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.7.4.2457

Abstract

The study aimed to examine the influence of entrepreneurial passion, entrepreneurial literacy, role model inspiration, and self-efficacy on entrepreneurship readiness among higher education students and the role of the project-based learning model implementation as a moderator variable. The population in the study were students in higher education in Indonesia who had taken entrepreneurship courses. Data from 313 valid respondents were analyzed against the research model using the Partial Least Squares Structural Equation Modelling. The findings revealed that entrepreneurial passion, entrepreneurial literacy, and role model inspiration were found to positively influence self-efficacy as well as entrepreneurship readiness among students in higher education. Unpredictably, the moderator project-based learning models’ implementation was shown to have an insignificant effect on the influence of entrepreneurial passion, entrepreneurial literacy, and role model inspiration toward entrepreneurship readiness among students in higher education. The findings of this study provide several important theoretical and practical implications for entrepreneurship readiness among students in higher education.  higher education in Indonesia who had taken entrepreneurship courses. Data collected from 313 valid respondents were analyzed against the research model using the Partial Least Squares Structural Equation Modelling. The findings revealed that entrepreneurial passion, entrepreneurial literacy, and role model inspiration were found to positively influence self-efficacy as well as entrepreneurship readiness among students in higher education. Unpredictably, the moderator project-based learning models’ implementation was shown to have an insignificant effect on the influence of entrepreneurial passion, entrepreneurial literacy, and role model inspiration toward entrepreneurship readiness among students in higher education. The findings of this study provide several important theoretical and practical implications for entrepreneurship readiness among students in higher education.
A Classification Algorithm Inspired by the Chromatographic Separation Mechanism Dedicated to the Classification of Variable-length and Multi-class Vectors Mariusz Święcicki
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.8.1.2324

Abstract

Nowadays, one of the critical problems related to data mining is the processing of large data sets. This article presents an algorithm that may apply to the issues associated with classifying large-volume data sets. The motivation behind defining this type of algorithm was that the methods used to process this data type are subject to several significant limitations. The first considerable limitation of using classical classification methods is ensuring a constant data size. The second type of constraint is related to the data dimension. The last limitation in using classic classification algorithms is associated with the situation in which a given input vector may contain data belonging to many classes simultaneously, in which case we are talking about so-called multi-class vectors. The presented algorithm is inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. As we know, in the case of chromatographic separation, we are dealing with a similar class of problems that occur when processing large data sets, firstly: the molecules of a chemical substance have a different number of molecules - i.e., they have different lengths, which corresponds to the situation that occurs when processing large data sets. In this work, a classification algorithm inspired by the mechanism of resolution chromatography is presented. The article presents the results of calculations for sample data sets. It discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class and multi-class data.
Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia Agastya, I Made Artha; Aminuddin, Afrig
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.7.4.1819

Abstract

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.  
Handling Imbalanced Data for Acute Coronary Syndrome Classification Based on Ensemble and K-Means SMOTE Method Muzakki, Muhammad Faris; Prayogo, Rizal Dwi; Rizky A, M Afif
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.7.3-2.1429

Abstract

Acute Coronary Syndrome (ACS) is a disease that has a high mortality rate with a mortality percentage of 40% after 5 years from diagnosis. Despite the high mortality rate, the conventional process of overestimating ACS can be life-threatening. For this reason, several alternatives for prediagnosis have been investigated to reduce the detection of ACS intensively, one of which is by using a machine learning approach. The machine learning-based prediagnosis approach utilizes patient medical record data as input for making detection models. This approach can produce an optimal model when there is quite a lot of data and the labels have a fairly balanced comparison. However, in machine learning-based ACS detection studies, researchers often do not have balanced data between positive and negative labels that have the potential to cause overfitting. That problem occurs because obtaining additional data with specific labels is difficult. To solve the imbalanced problem in ACS detection, we generated synthetic ACS data using the K-Means SMOTE method. The synthesis data is used as training data to build an ensemble-based machine-learning model. In this study, we obtain an increase in the F1 score of more than 10% when compared to machine learning models that do not use the K-Means SMOTE as an oversampling process. In addition to the greater F1 score, the results obtained are relatively more resistant to overfitting because the data variations in the training set are more diverse.
Classifying Gender Based on Face Images Using Vision Transformer Tahyudin, Ganjar Gingin; Sulistiyo, Mahmud Dwi; Arzaki, Muhammad; Rachmawati, Ema
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.8.1.1923

Abstract

Due to various factors that cause visual alterations in the collected facial images, gender classification based on image processing continues to be a performance challenge for classifier models. The Vision Transformer model is used in this study to suggest a technique for identifying a person’s gender from their face images. This study investigates how well a facial image-based model can distinguish between male and female genders. It also investigates the rarely discussed performance on the variation and complexity of data caused by differences in racial and age groups. We trained on the AFAD dataset and then carried out same-dataset and cross-dataset evaluations, the latter of which considers the UTKFace dataset.  From the experiments and analysis in the same-dataset evaluation, the highest validation accuracy of  happens for the image of size  pixels with eight patches. In comparison, the highest testing accuracy of  occurs for the image of size  pixels with  patches. Moreover, the experiments and analysis in the cross-dataset evaluation show that the model works optimally for the image size  pixels with  patches, with the value of the model’s accuracy, precision, recall, and F1-score being , , , and , respectively. Furthermore, the misclassification analysis shows that the model works optimally in classifying the gender of people between 21-70 years old. The findings of this study can serve as a baseline for conducting further analysis on the effectiveness of gender classifier models considering various physical factors.
Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones Huda, Achmad Torikul; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Sigit, Riyanto
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.8.2.1961

Abstract

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.

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