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All Journal JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Journal of Information Technology and Computer Engineering JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Advanced Science Computing and Engineering
Alde Alanda, Alde
Politeknik Negeri Padang, Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

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A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System Khaleefah, Shihab Hamad; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Khattak, Umar Farooq; Yaacob, Siti Salwani; Alanda, Alde
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.8.2.2701

Abstract

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
Implementation of The Moving Average Method for Forecasting Inventory in CV. Tre Jaya Perkasa Huriati, Putri; Erianda, Aldo; Alanda, Alde; Meidelfi, Dwiny; Rasyidah, -; Defni, -; Suryani, Ade Irma
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 2 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.89 KB) | DOI: 10.62527/ijasce.4.2.77

Abstract

The supply chain is an organization's place to distribute production goods and services to customers. This chain is a network of various organizations that are interrelated and have the aim of carrying out the procurement or supply of goods. Inventory is storing goods in the form of raw materials, semi-finished goods or finished goods that will be used in the production or distribution process. CV. Tre Jaya Perkasa is a company engaged in the distribution of goods such as snacks, drinks and daily necessities. CV. Tre Jaya Perkasa is located in Solok, West Sumatra, Indonesia. From January 2020 to June 2021, CV. Tre Jaya Perkasa has made more than 10 thousand transactions. Based on the sales data, each period (month) of sales transactions can increase and decrease, and the company must plan product sales in the coming period. To maximize profits and minimize losses, a strategy is needed to maintain the availability of goods that are often purchased by customers. From historical transaction data, the company can predict how much stock should be provided for transactions in the coming period. The method used is the moving average method, to measure the error rate of forecasting, MAD, MSE and MAPE are used. Based on the research that has been done, then carried out on the product Trick Potato Biscuit BBQ 24 BOX X 10X18 forecasting comparison between using 3 periods and 5 periods, using 5 product data that are most often purchased by buyers, it was found that the average value of MAD, MSE and MAPE closer to 0 is to use 3-period forecasting.
Systematic Literature Review: Digitalization of Rural Tourism Towards Sustainable Tourism Rasyidah; Erianda, Aldo; Alanda, Alde; Hidayat, Rahmat
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.134

Abstract

In the context of rural tourism, the internet is crucial. It is vital to create and implement new technology in order to assist digital tourism in tourist communities and undeveloped, frontier, and remote locations.  The utilization of big data can enhance the precision of predicting tourist flows, providing valuable insights to assist and enhance destination management, planning, and advertising. It can also ease mobility and encourage visitors to be distributed according to time. In addition to supporting visitors with specific access needs and keeping management informed about visitor behavior, artificial intelligence (AI) and automation can also be very helpful in the tourist industry by enabling those with limited mobility to travel the world. In this sense, as the sharing and gig economies grow along with technology, we have more options in our everyday lives—as long as they are properly set up and maintained. Therefore, this paper aims to study the research on internet criteria based on AlUla framework to achieve sustainable tourism in rural areas and to identify the key journals, articles and authors. The findings in this research are that there has been an increase in the number of journals post COVID19, where the country that produces the most journals is China and the author that is most cited is Pesonen JA. To achieve the goal of sustainable digital rural tourism, infrastructure is needed in the form of internet penetration, internet speed and usability, and internet security level.
Capacity Building for Farming System Digitalization Using Farming Management System Hidayat, Rahmat; Amnur, Hidra; Alanda, Alde; Yuhefizar; Satria, Deni
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.186

Abstract

Agriculture is one of the most important areas of human activity around the world. As the population increases, it is necessary to increase agricultural production. In the age of information technology, information plays a key role in people's lives. Agriculture is rapidly becoming a highly development-intensive industry where farmers need to collect and evaluate a large amount of information in their business processes to become more efficient in production and communicate information accordingly. Modernizing agriculture requires technological know-how for the efficient use of agricultural inputs. It deals with factors such as ecological footprint, product safety, labor welfare, nutritional responsibility, plant/animal health and welfare, economic responsibility and local market presence. They cover almost all stages in the production chain concerning day-to-day agricultural tasks, transactional activities for all stakeholders involved, and support for information transparency in the food chain. The use of information technology and network infrastructure currently enables the application of technology in agricultural business processes, but there is no standardized solution to enable interoperability and integration among services and stakeholders. Farming Management System (FMS) is expected to be a solution and standard in the use of technology in agriculture. Farming management system is a management system specifically designed to assist farmers or farm managers in managing their farming operations more efficiently and effectively. This system usually consists of integrated software and hardware to monitor and collect data from various aspects of agriculture, such as irrigation, fertilization, pesticide spraying, etc.
A Comprehensive Review of Cyber Hygiene Practices in the Workplace for Enhanced Digital Security Armoogum, Sheeba; Armoogum, Vinaye; Chandra, Anurag; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Bappoo, Soodeshna; Mohd Salikon, Mohd Zaki; Alanda, Alde
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.9.1.3787

Abstract

In today's digital age, cybercrime is increasing at an alarming rate, and it has become more critical than ever for organizations to prioritize adopting best practices in cyber hygiene to safeguard their personnel and resources from cyberattacks. As personal hygiene keeps one clean and healthy, cyber hygiene combines behaviors to enhance data privacy. This paper aims to explore the common cyber-attacks currently faced by organizations and how the different practices associated with good cyber hygiene can be used to mitigate those attacks. This paper also emphasizes the need for organizations to adopt good cyber hygiene techniques and, therefore, provides the top 10 effective cyber hygiene measures for organizations seeking to enhance their cybersecurity posture. To better evaluate the cyber hygiene techniques, a systematic literature approach was used, assessing the different models of cyber hygiene, thus distinguishing between good and bad cyber hygiene techniques and what are the cyber-attacks associated with bad cyber hygiene that can eventually affect any organization. Based on the case study and surveys done by the researchers, it has been deduced that good cyber hygiene techniques bring positive behavior among employees, thus contributing to a more secure organization. More importantly, it is the responsibility of both the organization and the employees to practice good cyber hygiene techniques. Suppose organizations fail to enforce good cyber hygiene techniques, such as a lack of security awareness programs. In that case, employees may have the misconception that it is not their responsibility to contribute to their security and that of the organization, which consequently opens doors to various cyber-attacks. There have not been many research papers on cyber hygiene, particularly when it comes to its application in the workplace, which is a fundamental aspect of our everyday life. This paper focuses on the cyber hygiene techniques that any small to larger organization should consider. It also highlights the existing challenges associated with the implementation of good cyber hygiene techniques and offers potential solutions to address them.
Implementasi Cloud Based Video Conference System Menggunakan Amazon Web Service Alanda, Alde; Satria, Deni
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 02 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.02.75-80.2021

Abstract

Since December 2019, the world and Indonesia have fought a major disaster, namely the Covid-19 virus pandemic. With the rapid spread or transmission of the virus, the Indonesian government decided to impose social distancing or social restrictions that impacted the education sector. Students and lecturers cannot conduct lectures face-to-face in class or laboratory, but lectures must be conducted online. For that, we need an open-source system developed by the campus in carrying out online courses. This application was developed using cloud technology and JITSI as an open-source video-conferencing application. In this study, testing of the features that exist in video conferencing and resource usage on the server is carried out. The results of feature testing on the application run as expected with several important features used for learning such as chat, share screen, recording features that can run optimally. The result tested the system resources based on the number of participants, 31 participants with an average use of 2.1GB RAM and 78 participants with an average RAM usage of 2.8GB.
Continuous Integration and Continuous Deployment (CI/CD) for Web Applications on Cloud Infrastructures Alanda, Alde; Mooduto, Hanriyawan Adnan; Hadelina, Rizka
JITCE (Journal of Information Technology and Computer Engineering) Vol. 6 No. 02 (2022)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.6.02.50-55.2022

Abstract

At this time, the application development process has experienced much development in terms of tools and the programming language used. The application development process is required to be carried out in a fast process using various existing tools. The application development and delivery process can be done quickly using Continuous Integration (CI) and Continuous Delivery (CD). This study uses the CI/CD technique to develop real-time applications using various programming languages implemented on a cloud infrastructure using the AWS codepipeline, which focuses on automatic deployment. Application source code is stored on different media using GitHub and Amazon S3. The source code will be tested for automatic deployment using the AWS code pipeline. The results of this study show that all programming languages can be appropriately deployed with an average time of 60 seconds
Real-time Defense Against Cyber Threats: Analyzing Wazuh's Effectiveness in Server Monitoring Alanda, Alde; Mooduto, H.A; Hadi, Ronal
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 2 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.2.56-62.2023

Abstract

As cloud computing grows exponentially, organizations face escalating cybersecurity challenges due to increased cyber threats and attacks on cloud-based networks. Monitoring cloud servers is one action that can be taken to improve the security. This can be done with the help of various server monitoring tools, such as Wazuh. The study investigates Wazuh's effectiveness in real-time monitoring of three AWS EC2 instance-based cloud servers. Wazuh's capabilities such as log data collection, malware detection, active response automation, and Docker container monitoring, are examined. The research reveals detailed insights into user activities, web server access, and database operations. Wazuh proves adept at tracking file integrity, detecting malware, and responding actively, as evidenced by the 342 alerts generated during a 24-hour monitoring period. The result shows that Wazuh is a particularly effective tool for protecting cloud environments from cyberattacks because it provides quick and ongoing security monitoring, which is essential for securing intricate cloud infrastructures.
A Hybrid Approach for Malicious URL Detection Using Ensemble Models and Adaptive Synthetic Sampling Sujon, Khaled Mahmud; Hassan, Rohayanti; Zainodin, Muhammad Edzuan; Salamat, Mohamad Aizi; Kasim, Shahreen; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4627

Abstract

Malicious URLs pose a significant cybersecurity threat, often leading to phishing attacks, malware infections, and data breaches. Early detection of these URLs is crucial for preventing security vulnerabilities and mitigating potential losses. In this paper, we propose a novel approach for malicious URL detection by combining ensemble learning methods with ADASYN-based oversampling to address the class imbalance typically found in malicious URL datasets. We evaluated three popular machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, and incorporated ADASYN (Adaptive Synthetic Sampling) to handle the class-imbalanced nature of our selected dataset. Our detailed experiments demonstrate that the application of ADASYN can significantly increase the performance of the predictive model across all metrics. For instance, XGBoost saw a 2.2% improvement in accuracy, Random Forest achieved a 1.0% improvement in recall, and Decision Tree displayed a 3.0% improvement in F1-score. The Decision Tree model, in particular, showed the most substantial improvements, particularly in recall and F1-score, indicating better detection of malicious URLs. Finally, our findings in this research highlighted the potential of ensemble learning, enhanced by ADASYN, for improving malicious URL detection and demonstrated its applicability in real-world cybersecurity applications.
Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection Hamzah, Khairatun Hisan; Osman, Mohd Zamri; Anthony, Tumusiime; Ismail, Mohd Arfian; Abdullah, Zubaile; Alanda, Alde
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.8.3-2.3451

Abstract

Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code. Traditional XSS detection systems often struggle to identify increasingly complex XSS payloads. To address this issue, this research evaluated the efficacy of Machine Learning algorithms in detecting XSS threats within online web applications. The study conducts a comprehensive comparative analysis of XSS attack detection using four prominent Machine Learning algorithms, which consist of Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This research utilizes a comparative methodology to assess the selected Machine Learning algorithms by analyzing their performance metrics, including confusion matrix, 10-fold cross-validation, and assessment of training time to thoroughly evaluate the models. By exploring dataset characteristics and evaluating the performance metrics of each selected algorithm, the study determined the most robust Machine Learning solution for XSS detection. Results indicate that Random Forest is the top performer, achieving 99.93% accuracy and balanced metrics across all criteria evaluated. These findings will significantly enhance web application security by providing reliable defenses against evolving XSS threats.
Co-Authors - Liliwarti A. Mostafa, Salama Ab Razak, Mohd Faizal Abdullah, Zubaile Ade Irma Suryani Ahmad Firdaus, Ahmad Aldo Erianda Aldo Erianda Aldo Erianda, Aldo Alfajri Maulana Ibrahim Anthony, Tumusiime Ardhana, M.Isthofa Armoogum, Sheeba Armoogum, Vinaye Baharum, Zirawani Bappoo, Soodeshna Binti Abdullah, Nurafrina Arrysya Chandra, Anurag Dahlan, Andi Ahmad Deddy Prayama Defni, - Deni Satria Dewi, Deshinta Arrova Dwiny Meidelfi Erwadi Bakar Gunasekaran, Saraswathy Shamini Hadelina, Rizka Hamzah, Khairatun Hisan Hanriyawan Adnan Mooduto Hezam, Abdulkareem A. Hidra Amnur Humaira, Humaira Huriati, Putri Indri Rahmayuni Indri Rahmayuni Ismail, Mohd Arfian Iswandi Saputra Kamarudin, Nur Diyana Binti Kamarudin, Nur Khairani Khaleefah, Shihab Hamad Khattak, Umar Farooq Kurniawan, Tri Basuki Makhtar, Siti Noormiza Mat Jusoh, Ruzanna Meri Azmi Mohd Salikon, Mohd Zaki Mooduto, H.A Mooduto, Hanriyawan Adnan Mostafa, Salama A. Osman, Mohd Zamri Rahmat Hidayat Rasyidah Rasyidah, - Rizka Hadelina Rohayanti Hassan, Rohayanti Ronal Hadi Salamat, Mohamad Aizi Salikon, Mohd Zaki Sarah Latifah Marif Satwarnirat Satwarnirat Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Yaacob, Siti Salwani Yance Sonatha Yuhefizar Yuhefizar Zainodin, Muhammad Edzuan