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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 54 Documents
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Search results for , issue "Vol 7, No 4 (2023)" : 54 Documents clear
Arabic Character Recognition Using CNN LeNet-5 Satya Nugraha, Gibran; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri; Yudo Husodo, Ario; Hamami, Faqih
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.7.4.2422

Abstract

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
Breed Lineage Prediction of Small Ruminants Using Deep Learning Kamil, Mohammad Farizshah Ismail; Akmal Jamaludin, Nor Azliana; Mohd Isa, Mohd Rizal
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.7.4.1168

Abstract

Sheep are a significant food source for humans, besides cattle and poultry. Despite its significance to Malaysian Muslims, who make up approximately 60% of the local population, the sheep supply is limited by the high mortality rate caused by fatal diseases such as foot and mouth disease (FMD) and tetanus. Infected sheep can spread food-borne bacteria, such as Escherichia coli, at various preparation phases, contaminating the meat. The objectives of this study are to identify internal and external factors that influence sheep breed lineage continuity, investigate current practices for collecting and managing data knowledge on sheep breed and hereditary diseases, and propose a sheep breed and disease data knowledge model based on the feedforward artificial neural network (FANN) deep learning method. This study utilized qualitative and quantitative data to obtain in-depth answers to the research questions, which involves collecting all the information required for the system development using the FANN deep learning method. This study found that breeding is the leading data group for tracking each sheep's ADG and BCS. Feed type, sanitization, and medication influence sheep’s daily increase and health. Collaboration, worker knowledge, and climate are recognized as external factors that potentially influence sheep's daily increase. The interview analysis also suggested attributes that could contribute to detecting breed lineage, including breed, category, ADG, and BCS. Therefore, it is recommended that future research adopt this method for other farmed animals.
Land Suitability for Mustard Plants Using Multi-Objective Optimization by Ratio Analysis Method Hatta, Heliza Rahmania; Ariani, Riska; Khairina, Dyna Marisa; Maharani, Septya; Kamila, Vina Zahrotun; Wijayanti, Arini
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.7.4.1290

Abstract

Sawi dapat dikembangkan atau dikembangkan dari sudut pandang finansial dan bisnis untuk memenuhi permintaan pembeli dan menangkap peluang pasar yang signifikan. Sawi merupakan tanaman hortikultura yang mempunyai daya adaptasi tinggi dan waktu panen yang relatif singkat. Sawi ini menawarkan banyak keuntungan bagi petani. Misalnya saja banyak petani yang menanam sawi di Samarinda, Kalimantan Timur, Indonesia. Meskipun sangat mudah beradaptasi, beberapa spesies sawi tidak tumbuh subur di tanah tertentu. Tanah yang baik sangat penting untuk hasil optimal saat menanam sawi. Sawi yang ditanam dapat diseleksi dengan menggunakan pendukung keputusan berdasarkan kriteria lahan untuk mendapatkan hasil terbaik. Tujuan dari penelitian ini adalah untuk merekomendasikan tanaman sawi yang cocok berdasarkan kebutuhan luas dengan menggunakan pendekatan multi-objective optimize by ratio analysis (MOORA). MOORA merupakan suatu metode pengambilan keputusan yang membantu dalam memilih alternatif terbaik dari beberapa pilihan atau alternatif berdasarkan beberapa kriteria atau tujuan. Pengamatan ini menggunakan lima kriteria yaitu jenis tanah, pH tanah, curah hujan, suhu, ketinggian lokasi, dan enam alternatif sawi. Berdasarkan uji lahan, sawi yang direkomendasikan metode MOORA adalah Sawi Sendok atau Pak Choy dengan nilai Yi sebesar 7,6698. Jadi yang dipilih sebagai sawi yang ditanam di lahan tersebut adalah Sawi Sendok atau Pak Choy. Untuk penelitian selanjutnya perlu dilakukan penambahan atau penyesuaian kriteria dan sensor baru secara real-time yang dapat diterapkan untuk meningkatkan efisiensi sawi menuju smart farming yang fokus pada hasil yang lebih baik dengan tetap menjaga keseimbangan alam.
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
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.
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.  
Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes Binti Muhammad Zahruddin, Nursyuhadah Alghazali; Kamarudin, Nur Diyana; Mat Jusoh, Ruzanna; Abdul Fataf, Nur Aisyah; Hidayat, Rahmat
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.7.4.2454

Abstract

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks Zakaria, Nurzarinah; Mohmad Hassim, Yana Mazwin
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.7.4.1752

Abstract

Convolutional Neural Networks (CNNs) have become essential to solving image classification tasks. One of the most frequent models of CNNs for image classification is the Visual Geometry Group (VGG). The VGG architecture is made up of multiple layers of convolution and pooling processes followed by fully connected layers. Among the various VGG models, the VGG16 architecture has gained great attention due to its remarkable performance and simplicity. However, the VGG16 architecture is still prone to have many parameters contributing to its complexity. Moreover, the complexity of VGG16 may cause a longer execution time. The complexity of VGG16 architecture is also more highly prone to overfitting and may affect the classification accuracy. This study proposes an enhancement of VGG16 architecture to overcome such drawbacks. The enhancement involved the reduction of the convolution blocks, implementing batch normalization (B.N.) layers, and integrating global average pooling (GAP) layers with the addition of dense and dropout layers in the architecture. The experiment was carried out with six benchmark datasets for image classification tasks. The results from the experiment show that the network parameters are 79% less complex than the standard VGG16. The proposed model also yields better classification accuracy and shorter execution time. Reducing the parameters in the proposed improved VGG architecture allows for more efficient computation and memory usage. Overall, the proposed improved VGG architecture offers a promising solution to the challenges of long execution times and excessive memory usage in VGG16 architecture. 
Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method Pratama, I Putu Agus Eka; Raharja, I Made Sunia
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.7.4.1762

Abstract

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.
Optimizing Pigeon-Inspired Algorithm to Enhance Intrusion Detection System Performance Internet of Things Environments Ratnawati, Fajar; Siswanto, Apri; Jaroji, -; Effendy, Akmar; Tedyyana, Agus
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.7.4.1724

Abstract

Intrusion Detection Systems (IDS) are crucial in maintaining network security and safeguarding sensitive information against external and internal threats. This study proposes a novel approach by utilizing a Pigeon-Inspired Algorithm optimized with the Hyperbolic Tangent Function (Tanh) function to enhance the performance of IDS in threat detection specifically tailored for Internet of Things (IoT) environments. We aim to create a more robust solution for optimizing intrusion detection systems by integrating the efficient and effective Tanh function into the Pigeon-Inspired Algorithm. The proposed method is evaluated on three widely-used datasets in the field of IDS: NSL-KDD, CICIDS2017, and CSE-CIC-IDS2018. Experimental results demonstrate that integrating the Tanh function into the Pigeon-Inspired Algorithm significantly improves the performance of the intrusion detection system. Our method achieves higher accuracy, True Positive Rate (TPR), and F1-score while reducing the False Positive Rate (FPR) compared to traditional Pigeon-Inspired Algorithms and several other optimization algorithms. The Pigeon-Inspired Algorithm optimized with the Tanh function offers an efficient and effective solution for enhancing intrusion detection system performance, specifically in Internet of Things environments. This method holds great potential for application in diverse network environments, bolstering information security and safeguarding systems from evolving cybersecurity threats. By extending the applicability and effectiveness of the Pigeon-Inspired Algorithm optimized with the Tanh function, researchers can contribute to developing more comprehensive and robust security solutions, addressing the ever-evolving landscape of IoT-based cybersecurity threats.

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