Evolution of Information, Communication and Computing System
Currently, Evolution of Information, Communication and Computing Systems (EICCS) is a yearly peer-reviewed indexed book (1 issue/year) published by the UTHM Publisher. This book is open to all scholars and experts in the area of information, communication, and computer systems.
Copyright (c) 2025 Evolution of Information, Communication and Computing System
This AI and IoT Integration: Empowering a Connected and Sustainable Future book explores the profound intersection of artificial intelligence, Internet of Things (IoT), and data-driven applications in addressing real-world challenges. Each chapter delves into specialized systems and models developed to solve pressing issues, from environmental monitoring to enhancing public services. This collection seeks to advance understanding and application of AI and IoT in crucial domains, offering a comprehensive resource for practitioners, researchers, and students eager to explore these dynamic technologies. We extend our deepest gratitude to the contributors, whose efforts and insights have enriched this volume. We hope readers find inspiration and valuable knowledge in these pages, empowering them to push the boundaries of innovation.
An Analysis of The Bio-Battery Efficiency from Fruits and Vegetables’ Waste as An Alternative to Energy Sources
Global warming and increasing air pollution have encouraged humans to continue to develop various environmentally friendly energies. One solution that can be taken is the development of bio-batteries, an energy storage system using microorganisms as an energy source. Bio-batteries promise environmentally friendly energy because they utilize organic waste. In this research, a bio-battery will be designed using electrolyte paste from several fruit and vegetable wastes, including oranges, mangoes, bananas, papaya, tomatoes and star fruit. These fruits and vegetables have previously been mashed and added with electrolyte liquid and then put into used batteries which have been cleaned from the manganese oxide which is the content of the battery. Then a comparison was also carried out with conventional batteries to determine the level of efficiency of these fruit and vegetable waste bio-batteries. This research shows that fruit and vegetable waste is less efficient if used as a substitute for manganese oxide or electrolyte in unused batteries. Because when compared to conventional batteries, the highest efficiency value for bio-batteries only reaches 32.47% for tomato waste bio-batteries and the lowest efficiency value is 4.77% for banana waste bio-batteries.
Prototype Design Monitoring System of A Salt Tunnel Processing Using Heater Based on Smartphone Application
Indonesia is an archipelago that has a vast ocean with the second-longest coastline in the world. This considerable marine potential affects the variety of livelihoods of people in Indonesia, especially in coastal communities. One of the many livelihoods of coastal communities is being a salt farmer. These farmers still utilize the sun's heat for the evaporation process, so salt production depends on weather factors. If the weather is rainy or cloudy, then the production will run very long or even stop which causes the production yield to decrease. With this condition, it is necessary to have a technology that can help the process of evaporating seawater during cloudy/rainy weather conditions, and even at night farmers can still carry out the process of making salt with a remote monitoring system. Researchers will design and build a prototype for monitoring tunnel salt processing by utilizing a heater for alternative seawater evaporation processes. This requires regular monitoring of the temperature in the tunnel, so a DS18B20 temperature sensor is used. In addition to the temperature sensor, it is also necessary to monitor the weather around the tunnel with an FC-37 rain sensor. Monitoring of the salt water concentration is done using a water pH sensor. The readings from all sensors will be displayed on the I2C 1602 LCD (Liquid Crystal Display) screen. This prototype will be applied directly to a mini tunnel mockup that uses UV plastic material for testing. All monitoring is expected to be done in real-time and flexibly from anywhere. The heater that will be applied should also be able to be controlled remotely. Therefore, researchers hope that with the use of an application-based monitoring system to monitor and control the evaporation process in salt the resulting production is maximized and effective.
Comparison Analysis of Satellite Images for Wildfire Detection using Convolutional Neural Network (CNN)
This research aims to compare the effectiveness of two datasets, the Kaggle Wildfire Prediction Dataset, and the SAT-4 dataset, in detecting wildfires using Convolutional Neural Networks (CNNs). The study analyzes satellite images relevant to wildfire incidents from both datasets. The Kaggle dataset, which uses satellite images to detect historical trends, has a test accuracy of 93.76%. In contrast, the SAT-4 dataset, used by Google Research, has a higher accuracy of 95.97%. Both datasets demonstrate high levels of accuracy, but the SAT-4 dataset is faster and more accurate in its implementation. Combining the historical data from Kaggle with the real-time efficiency of SAT-4 can further enhance the system's ability to detect and prevent wildfires more effectively. Integrating CNN-based image processing with remote sensing data improves the accuracy and effectiveness of wildfire identification and monitoring. The system leverages Telegram to notify users once a wildfire is detected, ensuring timely alerts and informed responses. The findings from this research have the potential to significantly improve existing approaches to wildfire management, bringing them to a new level of efficiency and accuracy.
Kaggle, Satellite image, SAT-4, wildfire, deep learning
Wildfire Detection Based on Satellite Information Processing Using Convolutional Neural Network
The exponential growth in satellite data availability and the need for more advanced processing methods have led to significant advancements in satellite information processing in recent years. This article provides a comprehensive review of the current trends and future directions in satellite information processing. Various processing techniques are employed for satellite imagery and remote sensing data. This includes preprocessing steps such as radiometric and geometric correction, as well as advanced processing methods like image classification, change detection, and feature extraction. In conclusion, satellite information processing is a multifaceted domain encompassing various techniques and applications critical for modern society. Understanding and advancing these processing methods are essential for harnessing the full potential of satellite data in addressing global challenges and fostering innovation across industries.
Wildfire Detection Using Convolutional Neural Network
This project focuses on leveraging deep learning techniques for the detection of wildfires, emphasizing their severe threat to property, human life, and ecosystems. Timely and accurate wildfire detection is crucial for effective response and mitigation. The study utilizes convolutional neural networks (CNNs) to analyze diverse data sources like satellite photos, aerial images, and real-time video feeds, eliminating the need for human feature engineering. The project achieves a final accuracy of 96.36% and a loss of 0.1013 in fire segmentation, with training accuracy at 95.12% and a loss of 0.1418. During validation, the model reaches a final accuracy of 94.54% with a loss of 0.2612 in fire classification. The outcomes demonstrate the potential of deep learning in improving wildfire response plans, and early warning systems, and reducing devastation. The project underscores the importance of continuous research and development in advancing real-time monitoring and proactive wildfire management strategies for securing lives, property, and natural surroundings.
Advancing Sustainable Development Through the Integration of AI, IoT, and Robotics
The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Robotics represents a promising approach to advancing sustainable development across critical sectors. This paper explores how these technologies, when applied collectively, can support the Sustainable Development Goals (SDGs) through enhanced efficiency, resource optimization, and equitable access to technological solutions. By reviewing current advancements and practical applications in smart cities, precision agriculture, renewable energy, healthcare, and education, this study illustrates the transformative potential of AI, IoT, and Robotics in achieving sustainability targets. Key case studies reveal that AI-driven analytics, IoT-enabled connectivity, and robotics-enabled automation provide substantial improvements in areas such as energy management, food production, and urban infrastructure. Additionally, the paper addresses essential ethical, regulatory, and social considerations, including data privacy, algorithmic transparency, and the need for standardized protocols. It emphasizes the importance of capacity building and interdisciplinary collaboration to ensure responsible, inclusive, and equitable deployment of these technologies. Future directions indicate that advancements in explainable AI, low-power IoT sensors, and autonomous robotics will further enhance the sustainability impacts of integrated systems, contributing to resilient and adaptable solutions for global challenges. This study underscores the critical role of AI, IoT, and Robotics in building a sustainable future, advocating for strategic partnerships and policy support to maximize their potential for positive social and environmental impact.
Enhancement of Real-Time Face Recognition via Video Surveillance System
This project report is about the investigation to enhance the accuracy of the face recognition video surveillance system. Recently, video surveillance with Closed Circuit TV (CCTV) cameras to analyze crowd people activities and real-time face recognition have been widely used. There are many face recognition systems that use Haar Cascade feature. However, due to the limitations of Haar Cascade, there are many methods and algorithms that have been implemented to enhance the accuracy of face recognition. One of them is the Gray Level Co-occurrence Matrix (GLCM). The hardware and software used in this project are Raspberry Pi, Pi camera, OpenCV library, and Scikit-Image library. The developed system consists of four phases for the operating method, which are data gathering, GLCM image texturizing, train recognizer, and recognition. Thonny Python was used in all of these phases with the OpenCV library and Scikit-Image library applied. All the results of this project were based on face detection, face recognition with face complications, face recognition with distances, and face recognition with different face angles. The results confirmed that the system was reliable with human face detection and face recognition with an accuracy up to 68%.
Surveillance camera, Face recognition, Raspberry Pi, Haar cascade classifier, Gray level co-occurrence matrix
Satellite Image Wildfire Detection Using Convolutional Neural Networks (CNN) With Kaggle Wildfire Prediction Dataset
Wildfires threaten human lives, property, and the environment, requiring advanced early detection and mitigation methods. This research work employs the use of Convolutional Neural Networks (CNNs) and remote sensors to create an early warning system for wildfires. It stresses the need for immediate action to mitigate destructive effects. In recent years, several tactics have been developed in artificial intelligence (AI), especially in CNNs, enhancing image classification performance. These advancements are utilized in this research to develop a CNN-based model for analyzing images relevant to wildfire incidents to achieve better results than current methods. The study employs the Kaggle Wildfire Prediction Dataset, which includes satellite images distinguishing regions that have experienced wildfires. This dataset is useful for detecting historical trends to train models. The model demonstrates satisfactory learning, as by the final test accuracy of 93.76%. However, the deep model takes more time to be trained than a shallow model due to its size and dimensionality, requiring a training period of 152-170 seconds. Accumulating data from Kaggle can improve the system for detecting wildfires, helping to prevent them more efficiently. To achieve this goal, it is possible to use CNN-based image processing and integrate it with remote sensing data, thus achieving improved and accurate wildfire identification and monitoring. The system will leverage Telegram to notify users once a wildfire is detected in the area, ensuring timely information is received.
Convolutional Neural Network (CNN), Wildfire, Satellite, Telegram
Tour Concierge: Tour Route Recommendation System Based on User Short-Term and Long-Term Preference Models
This chapter introduces Tour Concierge, an interactive system that learns each user's preferences and suggests optimal tour plans. A user preference model that is continuously optimized over time is proposed to recommend personalized tour routes. This model distinguishes between the user's short-term and long-term preferences and combines them with a tour route generation algorithm to increase user satisfaction. Twenty-six university students were asked to evaluate the use of this system and were surveyed. The survey results showed that 70.5% of the respondents felt that the tour routes generated by the system were compatible with their preferences. In addition, in a comparison experiment with ChatGPT, 64.1% of the respondents answered that the tour routes generated by the system were better. The experimental results suggest that this system has the potential to significantly improve the user's tour experience.
Copyright (c) 2023 Evolution of Information, Communication and Computing System
In an era defined by constant technological innovation and transformation, the field of Information, Communication, and Computing Systems is a needlepoint of ideas, theories, and practical applications. With each passing year, we witness a profound evolution that shapes our world and opens up new horizons for the future. The "Evolution of Information, Communication and Computing Systems (EICCS)" seeks to capture this ever-changing landscape and serve as an instrument to the collective wisdom and expertise of scholars and experts in this domain. As we delve into the pages of this book, we invite you to explore the frontiers of knowledge and innovation. Whether you are a seasoned expert or a curious newcomer, the insights within these pages are designed to inspire, inform, and challenge your understanding of the ever-evolving world of information, communication, and computing systems. We extend our heartfelt gratitude to the authors, reviewers, editors, and all those who have contributed to the creation of this volume. Your dedication to advancing the frontiers of knowledge is the driving force behind this initiative. It is our hope that "Evolution of Information, Communication and Computing Systems (EICCS)" serves as both a reflection of the present and a window into the future of these dynamic fields. Join us in this exploration of knowledge, and let the journey begin. Abd Kadir Mahamad, UTHM, 2023
CHITETEZO IoT-Based Air Pollution Monitoring System in Malawi using Raspberry Pi
Air pollution is a mounting concern in Malawi, especially in urban areas where particulate matter pollution consistently surpasses the World Health Organization's (WHO) recommended guidelines of 10 µg/m³ for PM2.5. This severe air pollution, exacerbated by the use of solid fuels for cooking in rural regions, has led to respiratory ailments and poses a significant threat to public health. To tackle this problem, a solution named "CHITETEZO" has been introduced. CHITETEZO is an IoT-based air pollution monitoring system that employs a Raspberry Pi 3 microprocessor and an MQ-135 gas sensor to detect harmful gases such as Carbon Monoxide, Particulate Matter, Nitrogen Monoxide, Ozone, and Smoke. It also integrates a backup MQ-2 gas sensor and a DHT22 sensor to measure temperature and humidity. The system provides alerts using a buzzer and a light-emitting diode (LCD) when hazardous gases are detected. The results of CHITETEZO's implementation have been promising. It offers accurate and timely air quality information, emphasizing affordability and accessibility. Data is transmitted wirelessly to web dashboards, mobile applications, and ThinkSpeaks platforms for storage, visualization, and analysis. This enables government agencies, researchers, and the public to access real-time air quality data conveniently. The CHITETEZO system has been successfully deployed in various indoor settings, including hospitals, offices, households, classrooms, and laboratories. It holds the potential to address the air pollution issue in Malawi by providing actionable data for stakeholders and raising public awareness about pollution sources and cleaner practices.
Implementation and Simulation of UDP Client-Server Environment using Contiki Cooja Simulator
This paper presents a simulation and evaluation of a UDP (User Datagram Protocol) client-server model using the Cooja simulator and Contiki OS System. The goal is to establish communication between the router, client, and server to monitor the data transmission and reception through a web server. The UDP protocol allows for fast and efficient data transfer without the need for a pre-established virtual path. The simulation results demonstrate the feasibility of utilizing UDP for real-time services and live communication. The findings highlight the flexibility of UDP in selecting multiple paths for data transmission, enhancing robustness and reliability. Further research can explore optimization techniques for UDP-based communication in diverse Internet of Things (IoT) networks.
The Design Once, Provide Anywhere Concept for the Internet of Things Service Implementation
In today's IoT landscape, most platforms primarily focus on connecting devices through application-centric strategies. This approach entails using separate protocols for device intercommunication and accommodating devices with diverse capabilities for various situations. However, the current methodology of designing and implementing IoT services proves unreliable due to device heterogeneity and environmental variations. This approach incurs significant time, financial, and developmental costs for service deployment. To address these limitations, a novel platform for deploying IoT services that embrace the 'design once, provide anywhere' concept is introduced. By treating logical devices and environmental elements as separate entities while unifying them under a single adaptable platform, the proposed work aims to streamline the IoT deployment process.
Room Surface Material Detection using Deep Learning to Determine the Reverberation Time of Classroom
In this study, we propose a novel approach for determining the reverberation time of a classroom using image processing and deep learning. A pre-trained ResNet-50 deep convolutional neural network was fine-tuned on a dataset of images captured in various classrooms. The images were augmented and processed to extract relevant features, such as room dimensions and material properties, that were used as input to the network. The network was trained to predict the reverberation time based on these features. The results showed that the proposed approach achieved high accuracy and outperformed traditional methods for determining the reverberation time. This approach can be used in various real-world applications, such as acoustic design and optimization of classrooms for enhanced learning environments. From the model that has been developed, the image used is able to be classified according to their respective class, resulting in 98.0% accuracy, and the calculation of reverberation time of the classroom using the Sabine equation is able to conduct, with a result of 0.4823s.
Hybrid Mechanisms on IoT Smart Lamps' Model for Time Efficiency
This study performs lamps activation process based on the Internet of Things (IoT) using hybrid mechanisms: site and far-off system. The site mechanism is using Bluetooth. Nevertheless, the far-off mechanisms are using internet and Network time protocol (NTP) activations. These mechanisms are presented in a model named I-Smart Lamps, which is designed and constructed conveniently to activate the lamps over the internet. The process of the three mechanisms: Bluetooth, NTP, and IoT are discussed in effective time activation. The Bluetooth mechanism gives an average time process of 13 seconds, the duration of the Bluetooth mechanism is better for residents in the building. The far-off system using IoT gives a better time process of activation. These mechanisms are convenient for residential buildings to serve the residents' activities with a rapid duration of lamps activation time.
Cafe Order Notification Tools Using Wireless with Point to Multipoint Communication Model
Day by day, new technologies and devices are developed that can help improve performance because technology makes things more accessible and economical. Various tools have been developed to facilitate human work. The author uses the ESP8266 Wemos D1 Mini as the brain of all components and the Blynk application as a device controller. This project includes a charger module, push button, LED, 1.3-inch OLED LCD, buzzer, transistor, and 1000mAH Lithium Ion battery. This research also applies an Internet of Things technology. Therefore, this paper, titled Cafe Order Notification Tool Using a Wireless Communication Model With Point To Multipoint, is developed with the working principle of (i) customers make an order, (ii) customers are given a queue notification tool for this cafe order, (iii) once the order is ready, the cashier control the device using the Blynk application and send a signal to the device. Then, if the tool exceeds the range and the WiFi network is disconnected, the tool will issue an alarm sound and an indicator on the LCD "Network Disconnected" within 20 meters. With a distance of 25 meters, tool number 1 can still be reached but tool numbers 2, 3, 4, and 5 are no longer reachable, whereas when they are kept about 30 meters away, all tools cannot reach the WiFi network that has been provided.
Design and Build A Logistics Delivery Drone to the Patients’ Quarantine Room Using ROS Technology Without GPS: An Initial Study
Drones are small unmanned aircraft that usually use GPS (Global Positioning System) technology to identify areas they pass through. However, its use is limited to open areas (outdoors) where the GPS sensor can connect to satellites without obstacles that can weaken the signal. An updated idea emerged about an indoor logistics delivery drone to the patient quarantine room automatically using ROS (Robot Operating System) technology without the GPS. This drone uses the main components of a Raspberry Pi, a PX4 flight controller, and an Intel Realsense T265 camera sensor. The Intel Realsense T265 camera sensor gets the current position and the drone’s movement; then, the data is processed on the Raspberry Pi. The drone goes to the place commanded by the program on the Raspberry Pi. It automatically places the logistics according to the desired location when it reaches the specified position. Even without GPS, it is hoped that drones can make it easier for medical personnel to deliver logistics on target and minimize virus transmission because they are not close to patients. Based on three times testing, the progress obtained is quite good. The drone can fly stably. Although some accidents were in the trial, they can be handled well.
Trends and Challenges in Mobile Edge Computing for the Next Generation Massive Internet of Things
Mobile Edge Computing (MEC) is a novel computing paradigm that brings data storage and computing resources closer to the end-users (i.e., at the network edge). The proximity between end users and the edge servers enables efficient access to data storage and faster processing, reduced network latency, and improved Quality of Service (QoS). The integration of MEC with the Next Generation Massive Internet of Things (MIoT) has the potential to solve several challenges such as scalability, reliability, and security of IoT systems but also enables unprecedented novel use cases. This chapter explores the current trends and challenges in MIoT and MEC, the benefits of their integration, and the state-of-the-art techniques used to achieve MEC-enabled MIoT systems. The chapter also discusses the potential applications and use cases of MEC for MIoT, including smart cities, industrial automation, and healthcare.
A Survey on Multi-Resident Activity Recognition in Smart Environments
Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.
Role of Consumer Technology and Connected Electronic Devices on SCM: A Discussion on its Usages, Impact and Challenges
Consumer technology and connected electronic devices such as the Internet of Things (IoT) are ubiquitous in our everyday lives. These digital devices play an integral role in modernizing human society as well as creating a positive impact on human advancements. The increased trade demands of the rapidly developing modern society and the evolution of supply chain management (SCM) have provided the necessary and sufficient infrastructure for the proliferation of these intelligent consumer technology devices. This chapter presents a novel study on the role and impact of consumer technology and connected electronic devices in supply chain management. A novel perspective and analysis of the impact and usage of the Internet of Things and consumer technology digital devices in supply chain management, along with the agile nature of the supply chain management over the Internet of Things, is presented in this chapter. Moreover, details regarding the advantages of IoT integration with supply chain management, challenges in the deployment of IoT in supply chain management, and future use of IoT in SCM are also discussed in this chapter.
Copyright (c) 2022 Evolution of Information, Communication and Computing System
Alhamdulillah, praise to Allah Almighty, the third series of book chapters under Evolution of Information, Communication and Computing System publication. This series is meaningful as it demonstrates continuous efforts over the past three years to achieve the Scopus indexed book chapter.
Classification Technique for Brain Tissues Diagnoses: Segmenting Healthy, Cancer Affected and Edema Brain Tissues
Brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is crucial to improving patients’ quality of life. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate, etc. Primarily, in this work, MRI images are used to diagnose a tumor in the brain. However, MRI scans’ huge amount of data thwarts manual classification of tumor vs non-tumor at a particular time. However, with some limitations, accurate quantitative measurements are provided for a limited number of images. Hence, trusted and automatic classification schemes are essential to prevent the human death rate. The automatic brain tumor classification is a very challenging task in large spatial and structural variability of the surrounding brain tumor region. This work proposes automatic brain tumor detection to segment the Region Proposal Network (RPN) by the Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps predict the correct type of tumor with better accuracy, about 99%, and classifies using Convolutional Neural Networks (CNN). The deeper architecture design is performed by using small kernels. Experimental results show that the CNN archives rate of 98% accuracy with low complexity compared with all other states of arts.
IoT based Parking Management System using Video Processing for Open Space Parking Area
The flawed parking management system may waste time and cost searching parking spaces. It may also cause traffic congestion and carbon emission. Finding a parking space in most metropolitan areas, especially during rush hours, is difficult for drivers. A difficulty arises from not knowing where the available spaces may be when traffic congestion may occur. The proposed system avoids poor parking management through object recognition technology that detects parking spaces of modern busy parking lots. Traffic congestion reduces by notifying the driver exact vacant parking spaces location. So the users can park their car in available space directly without looking around for space. The main objective of this project is to design IoT based parking system using TensorFlow based on object detection technology. Follow by the integration of the developed IoT parking system for normal road user’s. The project used Anaconda prompt to run the TensorFlow main object detection code. Telegram app act as IoT platform for users that can notify about the available or non-available parking. For the demo purpose, the project uses a video feed as the input for object detection. As a result, the parking status data keeps counting on the terminal for object detections in Anaconda prompt. At the same time, Telegram sends the notification to the user. In conclusion, the system proposes a practical methodology for managing the parking system using a camera placed for a lamp-post view. The system’s accuracy depends on the training model used for object training and the number of samples provided for training.
Vehicular Safety System : Driving Behaviour Identification Based on V2V Data Exchange System
Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.
Classification Algorithm for the Sentiment Analysis of the Covid 19 Pandemic in Indonesia Context
The Covid-19 pandemic that is happening in Indonesia has had a significant impact both in terms of economy, education and health. Pros and cons related to the policies made by the government in tackling the impact of the spread of the virus produces a variety of sentiments in the community that is interesting to analyze. By taking sentiment data on Twitter, which extracted using Tweepy, then the tweets data are analyzed with Textblob library contained in Python. Naïve Bayes algorithm then, have been used to classify the tweets either in positive, negative or neutral sentiments. From the experiments conducted, the test results found that there were 60% positive sentiments, 15% negative sentiments and 25% neutral sentiments on economic topics. On the other hand, 70% positive sentiment, 3% negative sentiment and 27% neutral sentiment on the topic of health. Meanwhile, 10% positive sentiment, 50% negative sentiment and 40% neutral sentiment on the topic of education.
Design Prototype of Traffic Light Control System using HSV Colour Segmentation
In Indonesia, most of the traffic lights are installed at intersections to perform traffic control. Signalling (Red, Green, Yellow) can orderly the traffic flow. However, many of these traffic lights work with fixed time settings. With the technological advancement nowadays, especially in image processing and computer vision, we can create a dynamic time based on traffic density for a traffic light. The OpenCV library uses to perform various tasks for image processing and computer vision for this project. Therefore, the proposed prototype system is designed to control a traffic light based on traffic density using image processing and HSV colour segmentation. The HSV colour algorithm for multiple colour detection with the provided number of upper and lower for each car colour (blue, green, pink and yellow) combined with binary mask and morphology operation was applied to estimate traffic density and classified traffic condition based on a number of vehicles on the road. It was using a USB camera as a peripheral to capture road conditions which Raspberry Pi 3 embedded the HSV algorithm for processing the video in real-time. The experiment result that ran in the room with a lighting intensity range of 230 – 246 lux showed that the HSV algorithm could estimate traffic density and classified traffic conditions based on the number of vehicles on the road. This system also has the ability to give time for a traffic light (green light) based on the traffic condition on the road.