The internet's continuous evolution has profoundly impacted society through the advancement of website technology and applications, reshaping contemporary ways of life. These digital platforms offer unrestricted information access, overcoming spatial and temporal limitations. In the realm of software development, Vulnerability Assessment is essential for producing high-quality products, as seemingly minor errors can create dangerous vulnerabilities that malicious actors may exploit to pilfer information from websites or applications. This study examines the security level of the Integrated website and application within the Faculty of Medicine, Universitas Andalas (Fakultas XYZ) environment, utilizing the Acunetix Web Vulnerability Scanner tool. The initial scan revealed a threat level of 3 (high) for the Fakultas XYZ website and level 2 (medium) for the Integrated application. Following a recapitulation process, several web alerts were identified for optimization, including Cross-Site Scripting (XSS), Blind SQL Injection, Application error message, HTML form without CSRF protection, Development configuration file, Directory listing, Error message on page, and User credentials sent in clear text. The optimization process involved source code review and enhancement to improve website features. A subsequent scan post-optimization demonstrated a reduction in threat levels for both the website and the UNAND FK Symphony application, with both achieving threat level 1 (low).

The research investigated the efficiency of a novel virtual reality (VR) method for anatomy education utilizing a web application designed for virtual dissection. This platform encompasses various features, including virtual dissection, quizzes, a chat forum, and direct messaging, transforming it into a virtual dissection classroom. To evaluate the efficiency of the virtual reality system we compared the web application with other existing web applications using parameters such as Accuracy, Precision, Jaccard index, Dice co-efficient, Processing time, and user rating. From the result obtained from the research in comparing the rendered models from this research with models from other work, the rendered models were able to achieve a Jaccard index of 0.93 and a dice coefficient of 0.94. It also achieved a remarkable average processing time of 15 seconds and a high user rating of 4.5.

This research presents the development of a U-Arm model robot with three degrees of freedom, utilizing Inverse Kinematic calculations. The novelty of this project lies in its precise control of the robot arm's movements through advanced kinematic algorithms. Inverse Kinematics is a mathematical process used to determine the joint angles of the robot arm from known (x, y, z) coordinates of the end-effector and the lengths of each link. The robotic arm consists of four links with lengths of 8.2 cm, 15 cm, 16 cm, and 18.4 cm, respectively, and is equipped with a gripping module for object manipulation. The methodology involves calculating the joint angles required for the desired end-effector position, ensuring accurate and efficient movement. Testing results indicate an average coordinate error of 7.13%, demonstrating the system's precision and reliability. This error rate provides valuable insights into the performance and potential areas for improvement in the kinematic model. Additionally, this research includes the development of a program to control the servo motor speed using For and delay functions. This program enhances the robot's operational efficiency by allowing precise speed adjustments, which are crucial for various applications. Overall, this study contributes to the field of robotics by offering a detailed analysis of kinematic control and program development for a multi-link robotic arm, highlighting its potential for practical applications.

The study evaluates the Quality of Service (QoS) performance of the Destination-Sequenced Distance Vector (DSDV) and Dynamic Source Routing (DSR) protocols in vehicle-to-vehicle (V2V) communication within a Vehicular Ad-Hoc Network (VANET) environment. Simulations were conducted to analyze key QoS metrics, including throughput, delay, and routing overhead, under various traffic densities and network dynamics. The results reveal that DSR excels in scenarios with rapid topology changes due to its lower routing overhead. At the same time, DSDV provides better route stability in less dynamic conditions, ensuring consistent performance. These findings underscore the importance of matching routing protocols to the specific requirements of V2V applications, such as real-time data exchange or traffic safety, to ensure informed decision-making. The study also highlights the potential for a hybrid protocol that integrates the stability of DSDV with the efficiency of DSR to address diverse VANET challenges and enhance overall QoS performance

Automatic recognition of sign language from hand gesture images is crucial for enhancing human-robot interaction, especially in critical scenarios such as rescue operations. In this study, we employed a DJI TELLO drone equipped with advanced machine vision capabilities to recognize and classify sign language gestures accurately. We developed an experimental setup where the drone, integrated with state-of-the-art radio control systems and machine vision techniques, navigated through simulated disaster environments to interact with human subjects using sign language. Data collection involved capturing various hand gestures under various environmental conditions to train and validate our recognition algorithms, including implementing YOLO V5 alongside Python libraries with OpenCV. This setup enabled precise hand and body detection, allowing the drone to navigate and interact effectively. We assessed the system's performance by its ability to accurately recognize gestures in both controlled and complex, cluttered backgrounds. Additionally, we developed robust debris and damage-resistant shielding mechanisms to safeguard the drone's integrity. Our drone fleet also established a resilient communication network via Wi-Fi, ensuring uninterrupted data transmission even with connectivity disruptions. These findings underscore the potential of AI-driven drones to engage in natural conversational interactions with humans, thereby providing vital information to assist decision-making processes during emergencies. In conclusion, our approach promises to revolutionize the efficacy of rescue operations by facilitating rapid and accurate communication of critical information to rescue teams.

The Long-Term Evolution (LTE) technology is currently evolving in the cellular communication system. Currently, LTE technology is only used for faster internet data activities. Unfortunately, phone calls still rely on second-generation (2G) or third-generation (3G) networks. To improve the quality of voice calls, one of the ways is through the utilization of Voice Over LTE (VoLTE) technology. The reason for using VoLTE in fourth-generation (4G) networks includes the voice quality based on Internet Protocol (IP). This study analyzes the performance of VoLTE technology networks. Based on the data collected, the Reference Signal Received Power (RSRP) with a percentage of 37.73% falls into the Good category, Signal to Interference Noise Ratio (SINR) with a percentage of 55.32% falls into the Fair category, and Throughput with a percentage of 66.16% falls into the Poor category. In terms of delay, it has a score of 4, categorized as very good, jitter has a score of 3, categorized as good, and packet loss has a score of 4, categorized as very good. The optimization results using physical tuning show that the Reference Signal Received Power (RSRP) falls into the Good category with a percentage of 52.8%, Signal to Interference Noise Ratio (SINR) falls into the Good category with a percentage of 70%, and Throughput falls into the Very Good category with a percentage of 64.50%.

Thrower Robot need to maintain system stability in carrying out their tasks, which require accuracy and stability while throwing the ring in different zones and have different distances and height, so the thrower robot needs to maintain the stability of the system in order to reach target properly. Maintain the stability of the thrower is important because of the physical task like throwing the ring. However, disturbance from external systems can affect the accuracy which can reduce the performance of the robot while performing their task. Therefore, system needs stable accuracy in performing the tasks despite interference. The control system is used to maintain acceleration and elevation in the process of throwing the ring so that it can reach the specified target. The implemented system uses Proportional, Integral and Derivative (PID) control based on the Cuckoo Search Algorithm (CSA). Function of PID control is to maintain a constant position at a certain target and CSA is used to simplify PID control tunning when it has some parameter modifications. Therefore, combination of PID-CSA is applied for this system to produce a control system that aims to maintain stability and reduce disturbances contained in the ring throwing robot based on manipulation. From the result obtained, the PID-CSA method has a better level of stability because it can reduce the percentage value of the error which produced by PID-TE by showing the percentage value of distance error up to 0.68% and value of angle error up to 2.39%.

In computer vision applications, pattern recognition is one of the important fields in artificial intelligence. With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. The purpose of conducting the research is to create the first-ever Dzongkha handwritten digit dataset and develop a model to classify the digit. In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. The model was trained on three different image dimensions: 28 by 28, 32 by 32, and 64 by 64. Compared to image dimensions 28 by 28 and 32 by 32, 64 by 64 gave the highest train, validation, and test accuracy of 98.66%, 98.9%, and 99.13% respectively. In the future, the sample of digits needs to be increased and use the transfer learning concept to train the model.

Information security is paramount for individuals, companies, and governments. Threats to data confidentiality are increasingly complex, demanding strong protection. Therefore, cryptography and steganography play pivotal roles. This study proposes the utilization of LSB (Least Significant Bit) steganography on image files employing the 3DES (Triple Data Encryption Standard) algorithm. The aim is to facilitate secure transmission and reception of data, including confidential messages, within digital information media. The research methodology involves implementing 3DES + LSB using Image Citra and innovating 3DES + MD5 Hash in .txt files. The results and discussions described include, among others, Pseudocode, Cryptographic Testing, and Steganography Testing. Based on the results of program analysis and testing, it can be concluded that the more messages that are inserted in the image, the more pixel differences there are in the stego image. The more colors in the image to which the message will be inserted, the more pixel differences in the stego image will be. The images that stego objects can present are only images with .png and .jpeg extensions. Testing from the fidelity aspect, the average PSNR obtained is 66,365, meaning that the stego image quality is very good. Testing from the recovery aspect, from 4 tested stego images, showed that messages can be extracted again. Testing of the robustness spec using two attack techniques, namely rotation, and robustness, shows that the message cannot be extracted from the image. Testers of the computation time, from testing 1-1000 characters, show the average time required for computation is about 0.798 seconds.

Ensuring safety while riding a motorbike is an imperative task. Currently, safety products such as helmets have the capability to provide protection to users without the additional feature of issuing warnings. Consequently, a preemptive alert system is developed to offer timely notifications to drivers. The experimental setup involves the utilization of a Max30100 sensor that is linked to a microcontroller and integrated into a helmet. The objective of this final project is to offer a timely alert to the rider and utilize the Max30100 sensor for pulse detection in order to ascertain the normalcy of the rider's pulse. In instances where the rider encounters tiredness and fatigue, it is common for the pulse intensity to exhibit a reduction. The Blynk application presents the detection pulse findings on the smartphone screen, while the buzzer on the helmet will activate in response to vibrations and sounds once the pulse has diminished. Based on testing, the average pulse rate on quiet road conditions is 78.58 BPM. On busy road conditions, the average pulse rate is 73.25 BPM. While in traffic conditions, the average pulse rate is 73.5 BPM. The helmet theft detector uses a Sharp GP2Y0A21 sensor that can only detect object distances up to 10 cm.