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All Journal JOIV : International Journal on Informatics Visualization
Kamarudin, Nur Diyana
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Computer Science & IT

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Journal : joiv international journal on informatics visualization

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Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm Binti Mohd Sabudin, Umi Syahirah; Makhtar, Siti Noormiza; Nor, Elya Mohd; Muhamed, Siti Anizah; Mohd Sani, Fareisya Zulaikha; Kamarudin, Nur Diyana
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.8.3.2040

Abstract

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.
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.
Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model Mohd Sani, Fareisya Zulaikha; Mohamad Zin, Ahmad Arif Izudin; Mohd Nor, Elya; Kamarudin, Nur Diyana; Makhtar, Siti Noormiza
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.8.2.2177

Abstract

The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.
Measuring Score of Ethnic Tolerance Index among Peacekeepers using a MyETI System Dashboard Wan Husin, Wan Norhasniah; Kamarudin, Nur Diyana; Hilmi, Muhammad Amjad; Zainurin, Siti Juwairiah; Jamilah, Maryam
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2261

Abstract

In post-conflict nations, the state-building process might take up to ten years to provide positive outcomes. This scenario has become increasingly challenging due to the escalation of intolerance among different ethnic groups, leading to incidents of communal violence in the aftermath of the war. Therefore, it is imperative to have ethnic tolerance and cultural understanding in peacekeeping operations that occur in a more intricate setting. The presence of ethnic tolerance among peacekeepers is crucial for ensuring the efficacy of peacekeeping missions. The increase of intolerant perspectives often causes the beginning of ethnic conflicts in multi-ethnic societies. Therefore, the main objective of peacekeepers deployed in these countries is to reinstate peace and security. This study proposes employing an online analysis to assess the ethnic tolerance index among peacekeepers accurately. The suggested method entails collecting and analyzing real-time survey data via a MyETI system dashboard, which may precisely evaluate the ethnic tolerance index score among Malaysian individuals. The MyETI e-survey has 103 questionnaires organized into four main categories: ethnic cross-relationships, governance, ethnic tolerance, and religious beliefs. To achieve the study's goal, 103 Malaysian peacekeepers who have previously been deployed to different United Nations Peacekeeping Operations (UNPKO) will be requested to answer the questions using the MyETI dashboard. The results could enhance the ethical guidelines for cultural competence, prioritizing understanding ethnic tolerance in peacekeeping operations or deployments.
Ontology Modeling for Subak Knowledge Management System Hariyanti, Ni Kadek Dessy; Linawati, Linawati; Oka Widyantara, I Made; Sukadarmika, Gede; Arya Astawa, I Nyoman Gede; Kamarudin, Nur Diyana
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3386

Abstract

Subak, as a Balinese traditional agricultural organization, has knowledge of cultural heritage, including both explicit and tacit elements. This research aimed to develop ontology knowledge model for the digital preservation of Subak culture in the form of Knowledge Management System (KMS). The development of model was based on three main stages, including requirement analysis, ontology development, and ontology assessments. Requirement analysis included data collection through field observations, in-depth interviews, and document analysis, while ontology development consisted of hierarchical classes, object and data properties, as well as individual entities. Furthermore, ontology assessments were the stage of evaluating and testing the resulting ontology. Protégé software was used to apply ontology model, generating Ontograph visualizations and producing Ontology Web Language (OWL). Validation was carried out using both Ontology Quality Analysis (OntoQA) and expert comments. The evaluation results showed a Relationship Richness (RR) value of 0.8, an Inheritance Richness (IR) value of 0.78, and an Attribute Richness (AR) value of 3.89, showing that ontology captured a comprehensive and representative body of knowledge. Expert comments stated that ontology model created was worthy of being used to represent Subak knowledge as a form of cultural preservation. The developed Subak ontology could serve as a foundational knowledge base for further research in related fields such as agricultural management, social organization, and cultural preservation.
Enhancing The Server-Side Internet Proxy Detection Technique in Network Infrastructure Based on Apriori Algorithm of Machine Learning Technique Maskat, Kamaruzaman; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Kamarudin, Nur Diyana; Ismail, Mohd Nazri
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3410

Abstract

The widespread use of proxy servers has introduced challenges in managing and securing internet connections, particularly in detecting non-transparent proxies that obscure the originating IP address. Proxy servers, while beneficial for bandwidth management and anonymity, can be exploited for malicious purposes, such as bypassing geo-restrictions or concealing cyberattacks. This study aims to address the gap in identifying proxy usage by providing an organized review of existing detection techniques and proposing a hybrid server-side detection framework. The objectives of the research include identifying and comparing proxy detection methods, developing a hybrid approach using machine learning, and evaluating its effectiveness in enhancing network security. The methodology involves collecting primary data through controlled environments simulating direct and proxy-based connections. A machine learning model, based on the Apriori algorithm, is employed to analyze network traffic patterns and identify characteristics indicative of proxy usage. Attributes such as IP addresses, port numbers, and round-trip times are used to train the model. The proposed framework is tested for its robustness, accuracy, and speed against existing detection methods. The results demonstrate the feasibility of the hybrid approach in improving the detection of non-transparent proxies, particularly those not easily identifiable using conventional techniques. The findings have significant implications for securing server-side infrastructure, aiding in cyber threat mitigation, and enforcing organizational policies. Future research can expand on this framework by testing it against broader proxy types and integrating real-world data to enhance its reliability and scope. This study contributes to advancing cybersecurity practices by addressing a critical challenge in proxy detection.
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.
Co-Authors Abdul Fataf, Nur Aisyah Binti Mohd Sabudin, Umi Syahirah Binti Muhammad Zahruddin, Nursyuhadah Alghazali Dessy Hariyanti, NK Gede Sukadarmika Hilmi, Muhammad Amjad I Made Oka Widyantara I Nyoman Gede Arya Astawa Khairuddin, Mohammad Adib Linawati Linawati Makhtar, Siti Noormiza Maryam Jamilah Maskat, Kamaruzaman Mat Jusoh, Ruzanna Mohamad Zin, Ahmad Arif Izudin Mohamad, Ahmad Umaer Mohd Isa, Mohd Rizal Mohd Nazri Ismail, Mohd Nazri Mohd Nor, Elya Mohd Sani, Fareisya Zulaikha Muhamed, Siti Anizah Nor, Elya Mohd Ong, Ang Teoh Othman, Nur Syahiera Rahmat Hidayat Ramli, Suzaimah Wan Husin, Wan Norhasniah Zainurin, Siti Juwairiah