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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

Author-ID : 619778

Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

Published : 21 Documents Claim Missing Document

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Articles

Title

Design of Private Geographycal Information System (GIS) Server for Battlefield Management System Alde Alanda; Erwadi Bakar
JOIV : International Journal on Informatics Visualization Vol 1, No 1 (2017)
Publisher : Politeknik Negeri Padang

Geographic Information System (GIS) data is needed for model earth surface in 3D simulation for SAR operation to make simulation process as real as possible. Adding integrated GIS data server to simulation system make simulation application user does not need to input and prepare the GIS data manually, by reducing simulation application user task, user can more concentrate on simulation process.In this research the design and implementation of GIS data application that can provide the data needed by a simulation application using existing data on the online map provider. Application designed to display data necessary to carry out the conversion of GIS data to the format used in the simulation . Based on the test resuls of the conversion of GIS data to map format generated simulation has the same texture to the original map. Simulations can be run by using the map conversion and the simulation can run using real map but the level of height accuracy is not optimal.

Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks Abdulkareem A. Hezam; Salama A. Mostafa; Zirawani Baharum; Alde Alanda; Mohd Zaki Salikon
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.733

Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future. Even though many solutions have been developed to identify and prevent this assault, which is mainly targeted at IoT devices, the danger continues to exist and is now larger than ever. It is common practice to launch denial of service attacks in order to prevent legitimate requests from being completed. This is accomplished by swamping the targeted machines or resources with false requests in an attempt to overpower systems and prevent many or all legitimate requests from being completed. There have been many efforts to use machine learning to tackle puzzle-like middle-box problems and other Artificial Intelligence (AI) problems in the last few years. The modern botnets are so sophisticated that they may evolve daily, as in the case of the Mirai botnet, for example. This research presents a deep learning method based on a real-world dataset gathered by infecting nine Internet of Things devices with two of the most destructive DDoS botnets, Mirai and Bashlite, and then analyzing the results. This paper proposes the BiLSTM-CNN model that combines Bidirectional Long-Short Term Memory Recurrent Neural Network and Convolutional Neural Network (CNN). This model employs CNN for data processing and feature optimization, and the BiLSTM is used for classification. This model is evaluated by comparing its results with three standard deep learning models of CNN, Recurrent Neural Network (RNN), and long-Short Term Memory Recurrent Neural Network (LSTMâ€“RNN). There is a huge need for more realistic datasets to fully test such models' capabilities, and where N-BaIoT comes, it also includes multi-device IoT data. The N-BaIoT dataset contains DDoS attacks with the two of the most used types of botnets: Bashlite and Mirai. The 10-fold cross-validation technique tests the four models. The obtained results show that the BiLSTM-CNN outperforms all other individual classifiers in every aspect in which it achieves an accuracy of 89.79% and an error rate of 0.1546 with a very high precision of 93.92% with an f1-score and recall of 85.73% and 89.11%, respectively. The RNN achieves the highest accuracy among the three individual models, with an accuracy of 89.77%, followed by LSTM, which achieves the second-highest accuracy of 89.71%. CNN, on the other hand, achieves the lowest accuracy among all classifiers of 89.50%.

Web Application Penetration Testing Using SQL Injection Attack Alde Alanda; Deni Satria; M.Isthofa Ardhana; Andi Ahmad Dahlan; Hanriyawan Adnan Mooduto
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.470

A web application is a very important requirement in the information and digitalization era. With the increasing use of the internet and the growing number of web applications, every web application requires an adequate security level to store information safely and avoid cyber attacks. Web applications go through rapid development phases with short turnaround times, challenging to eliminate vulnerabilities. The vulnerability on the web application can be analyzed using the penetration testing method. This research uses penetration testing with the black-box method to test web application security based on the list of most attacks on the Open Web Application Security Project (OWASP), namely SQL Injection. SQL injection allows attackers to obtain unrestricted access to the databases and potentially collecting sensitive information from databases. This research randomly tested several websites such as government, schools, and other commercial websites with several techniques of SQL injection attack. Testing was carried out on ten websites randomly by looking for gaps to test security using the SQL injection attack. The results of testing conducted 80% of the websites tested have a weakness against SQL injection attacks. Based on this research, SQL injection is still the most prevalent threat for web applications. Further research can explain detailed information about SQL injection with specific techniques and how to prevent this attack.

Network Security Assessment Using Internal Network Penetration Testing Methodology Deni Satria; Alde Alanda; Aldo Erianda; Deddy Prayama
JOIV : International Journal on Informatics Visualization Vol 2, No 4-2 (2018): Cyber Security and Information Assurance
Publisher : Politeknik Negeri Padang

The development of information technology is a new challenge for computer network security systems and the information contained in it, the level of awareness of the importance of network security systems is still very low. according to a survey conducted by Symantec, the desire to renew an existing security system within a year within a company has the result that only 13% of respondents consider changes to the security system to be important from a total of 3,300 companies worldwide as respondents. This lack of awareness results in the emergence of security holes that can be used by crackers to enter and disrupt the stability of the system. Every year cyber attacks increase significantly, so that every year there is a need to improve the security of the existing system. Based on that, a method is needed to periodically assess system and network security by using penetrarion testing methods to obtain any vulnerabilities that exist on the network and on a system so as to increase security and minimize theft or loss of important data. Testing is carried out by using internal network penetration testing method which tests using 5 types of attacks. From the results of the tests, each system has a security risk of 20-80%. From the results of these tests it can be concluded that each system has a security vulnerability that can be attacked.

Liquefaction Potential Map based on Coordinates in Padang City with Google Maps Integration - Liliwarti; - Satwarnirat; Alde Alanda; Rizka Hadelina
JOIV : International Journal on Informatics Visualization Vol 4, No 1 (2020)
Publisher : Society of Visual Informatics

Abstractâ€” Padang City is prone to liquefaction phenomena due to earthquakes. These phenomena can cause various damages to structures, infrastructures, and even can also cause deaths. Therefore, as one of the urban populated cities, the information about liquefaction potential is needed. One of them is by providing a liquefaction potential map, which is useful for mitigation and seismic disaster risks strategies. This article aims to provide a digital map of liquefaction potential in Padang City that integrates with Google Maps. The map is based on 40 coordinates in 7 subdistricts in the city with 3 colored markers that represent the levels of potential liquefaction i.e. no liquefaction level, moderate liquefaction level, and severe liquefaction level. The levels are classified based on the analysis of the secondary Cone Penetration Test data by using the calculation of the Factor of Safety and Liquefaction Potential Index with an earthquake assumption of 8 SR. The result shows that the map has ben able to display information about liquefaction potential, where 32.05% coordinates are classified as no liquefaction level with the highest percentage are in Kuranji, 22.5% are classified as moderate liquefaction level with the highest percentage are in Padang Utara, and 45.0% are classified as severe liquefaction level with the highest percentage are in Koto Tangah.

The Rise of Deep Learning in Cyber Security: Bibliometric Analysis of Deep Learning and Malware Kamarudin, Nur Khairani; Firdaus, Ahmad; Osman, Mohd Zamri; Alanda, Alde; Erianda, Aldo; Kasim, Shahreen; Ab Razak, Mohd Faizal
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1535

Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y.

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

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.

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

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.

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

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.

Co-Authors - Liliwarti A. Mostafa, Salama Ab Razak, Mohd Faizal Abdulkareem A. Hezam Ade Irma Suryani Ahmad Firdaus, Ahmad Aldo Erianda Aldo Erianda Aldo Erianda, Aldo Alfajri Maulana Ibrahim Andi Ahmad Dahlan Armoogum, Sheeba Armoogum, Vinaye Bappoo, Soodeshna Chandra, Anurag Deddy Prayama Defni, - Deni Satria Dewi, Deshinta Arrova Dwiny Meidelfi Erwadi Bakar Gunasekaran, Saraswathy Shamini Hadelina, Rizka Hanriyawan Adnan Mooduto Hanriyawan Adnan Mooduto Hidra Amnur Humaira, Humaira Huriati, Putri Indri Rahmayuni Indri Rahmayuni Iswandi Saputra Kamarudin, Nur Khairani Khaleefah, Shihab Hamad Khattak, Umar Farooq Kurniawan, Tri Basuki M.Isthofa Ardhana Meri Azmi Mohd Salikon, Mohd Zaki Mohd Zaki Salikon Mooduto, H.A Mooduto, Hanriyawan Adnan Osman, Mohd Zamri Rahmat Hidayat Rasyidah Rasyidah, - Rizka Hadelina Rohayanti Hassan, Rohayanti Ronal Hadi Salama A. Mostafa Salamat, Mohamad Aizi Sarah Latifah Marif Satwarnirat Satwarnirat Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Yaacob, Siti Salwani Yance Sonatha Yuhefizar Yuhefizar Zainodin, Muhammad Edzuan Zirawani Baharum

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