International Journal on Advanced Science, Engineering and Information Technology
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Change in Attitude toward Artificial Intelligence through Experiential Learning in Artificial Intelligence Education
Get PDFGiven the rapid advancements in artificial intelligence (AI), the education sector has been actively striving to instill AI-related competencies in students. In a notable development in 2022, South Korea took a pioneering step by overhauling its curriculum with a primary focus on enhancing students' AI skills. However, despite these efforts, a persistent challenge remains: many students continue to harbor unfavorable perceptions and attitudes toward AI. The existing educational methods have proven insufficient in addressing this issue. Consequently, this study embarked on a quest to identify effective strategies for cultivating a more positive outlook on AI among middle school students. To tackle this challenge head-on, an experiential learning-based AI education program was meticulously designed and implemented for middle school students in Korea. The study rigorously evaluated the program's impact on students' attitudes toward AI. The results unveiled a significant improvement in students' perceptions of AI following the intervention, providing solid empirical evidence of the efficacy of the experiential learning-based AI education program in reshaping middle school students' attitudes toward AI. This research underscores the paramount importance of practical, hands-on experiences in education as a potent means to bridge the gap between knowledge and perception. It offers invaluable insights that can guide the development of AI education curricula worldwide, emphasizing the indispensable role of experiential learning approaches in nurturing positive attitudes and beliefs about AI among students
Development of Land Price Model with Geographically Weighted Regression on the Existence of Spatial Planning Zones: A Case Study in the Eastern Bandung City
Get PDFOne of the methods used to estimate land prices is the Geographically Weighted Regression (GWR). The GWR method is built based on the dependent and independent variables (land prices) (the spatial proximity between the land object and other facilities). However, this study will develop the independent variable by adding a spatial planning zone to provide the complexity of land price estimation. This study proposes an implementation mechanism by setting each zone type as an independent variable. Based on the spatial planning zones in Eastern Bandung City, there are five spatial planning zones. Thus, 15 variables were used in this GWR model, with ten variables from public facilities and five from spatial planning zones. The variables are categorized into worship, industry, government offices, health, sports/recreation, education, prisons, defense offices, terminals, trade and service zones, industrial zones, and low-residential, medium, and high-residential zones. The results of this study indicate that the implementation of the spatial planning zone variable has a better accuracy rate than the GWR model without involving the spatial planning zone variable. The approach with the proposed mechanism gives better accuracy of 8.6%. Spatial planning zone variable can be a new perspective in making a GWR-based land price estimation model in addition to the physical object variable in the form of public or social facilities, especially to improve the quality of the model formed
An Improved Fingerprint Method for Indoor Mobile Object Positioning
Get PDFThe mobility of people in big cities tends to increase with the improvement in the availability of sharing access in public facilities. However, the dense environment with moving objects will allow the possibility of the object's detachment from a monitoring system. This condition will cause concern, especially if the object is a priority that needs to be protected. We propose a system for detecting moving objects in indoor environments using a fingerprint method of Received Signal Strength (RSSI) data retrieval. The work was conducted in the observation area, a part of a mall full of tenant stores and humans moving during shopping hours. The randomness of RSSI data emitted from access points in the indoor area is affected by multipath and signal reflection because of walls or furniture existing in the building. The K-NN regression algorithm was utilized to generate RSSI data based on the on-site measurement to prune this randomness. The generated data will be clustered, using the K-means algorithm and Elbow method to ensure the optimal number of K. The experiment results showed that using the best K for RSSI clustering, the positioning accuracy produced by the system reached 77% of the total expected accuracy. Meanwhile, according to the signal characteristics of indoor buildings, the entrance corner had the worst data distribution, with 37.35% of the data generated having an RSSI value close to the receiver sensitivity threshold
A Study on Computational Thinking for Major of Computer Science
Get PDFRecent advances in artificial intelligence and Software have resulted in a paradigm shift in education, necessitating greater changes in educational methods and environments. Many countries worldwide have highlighted computational thinking as the core competence related to software education, leading to an increase in the number of studies focusing on computational thinking in Korea as well. Future demands must be analyzed, and curricula must be improved through in-depth education in computational thinking. Therefore, computational thinking curricula currently offered by universities for students majoring and not majoring in related fields must be reformed for precise educational goals. While research on computational thinking for non-major students has been consistently conducted, curricula for major students are inadequate, and there is a lack of learning opportunities because of the expectation that education on computational thinking for computer science major students will be naturally achieved. Thus, for the purpose of improving the computational thinking education for computer science major students, this study conducted a survey consisting of six questions on "perception of computational thinking" and 11 questions on "need of computational thinking" among 313 students majoring in computer science at a university in Korea. In the study results, 177 students (56.5%) answered "I do not know" for the question "I know what computational thinking is well enough," indicating that computational thinking education must be expanded and considered not only for non-computer science majors but also for computer science major students
Botnet Detection Model in Encrypted Traffics Software-Defined Network (SDN) Using Deep Neural Network (DNN)
Get PDFThe presence of network technology eliminates regional boundaries that become obstacles in communicating and exchanging data and information to the public. The wider the zone of a network, the network infrastructure will increase in size. The bigger the network infrastructure, the higher the level of management complexity. The Software Defined Network (SDN) concept is a new network concept that provides a solution for managing large infrastructure networks and has a wide service zone. SDN architecture is different from traditional networks. The SDN architecture is divided into three: the data plane, control plane, and application plane. Whereas in the traditional network architecture, the three are combined into one. Besides, in maintaining network security. SDN offers a security system, namely the OpenFlow Protocol. The OpenFlow Protocol security system works to regulate the packet traffic that passes. Forwards registered packet data traffic and performs down the action for unknown packet traffic. The weakness is that the OpenFlow Protocol must always be updated with SDN network packet traffic, and the system cannot detect the threat of attacks on encryption traffic. Nowadays, the frequency of attacks on network traffic is relatively high. The attack techniques used also evolved. The techniques used are also evolving. Botnets have been able to use several encryption protocols such as TLS / HTTPS, Tor, and P2P as loopholes to attack a network. SDN's presence as a management solution for large infrastructure networks is not directly proportional to its security system that undoubtedly have a bad impact on SDN network users. Therefore, this study aims to develop an SDN Network Intrusion Detection System (IDS) model to detect botnets in encryption traffic. The model was developed using the Deep Neural Network (DNN) approach. The SDN network botnet detection model developed can detect encryption traffic botnets with an accuracy rate of 94.78%, 93.28% precision, and a recall of 99.11%
Evaluation and Optimization Based on Exergy in Kamojang Geothermal Power Plant Unit 3
Get PDFThe quality of production well from the Kamojang geothermal power plant unit 3 diminishes annually, whereas there has been a substantial rise in the demand for electrical energy in the region. This research focuses on optimizing the vacuum pressure in the main condenser by employing exergy analysis, a methodology grounded in the principles of the second law of thermodynamics. Exergy analysis offers insights into each system component's exergy efficiency and irreversibility. Furthermore, an energy assessment is conducted to offer insights into each component's energy consumption or utilization. Energy and exergy rates are computed for every state and component within the power plant, encompassing the steam receiving header, separator, demister, turbine, main condenser, inter condenser, after condenser, and cooling tower. The exergy analysis findings reveal that the exergy rate derived from the production well amounts to 95327 kW, generating 52882 kW of electricity and producing a system exergy efficiency of 55.47%. The turbine experiences the highest irreversibility, totaling 12874 kW. Adjustments are made to the main condenser vacuum pressure to optimize the system, aiming to identify the optimal setting that maximizes both exergy efficiency and power output. The optimization outcomes indicate that reducing the vacuum pressure in the main condenser leads to enhanced exergy efficiency and increased power output. The optimal vacuum pressure obtained is 0.1 bar, resulting in the highest exergy efficiency and output power of 57.42% and 54738 kW, respectively, with the lowest irreversibility of 32751.07 kW
Telecommunication Fiber Box Detection Using YOLO in Urban Environment
Get PDFThe Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data
Potential Economic Value of Water Resource Sustainability for Sustainable Environment: A Case Study in South Sumatra, Indonesia
Get PDFErosion in coal mining land causes water quantity and quality depletion and inadequate drinking water resources for surrounding communities, making water resources unsustainable. Meanwhile, reclamation reduces erosion but cannot restore water depletion optimally; thus, these resources remain unsustainable. These resources remain unsustainable. The objectives of this study were to develop a water resource sustainability concept for a sustainable environment by analyzing the potential economic value and, secondly, to calculate the water resource value due to erosion, reclamation, and domestic and economic importance, of recycling efforts. The method used in this study was the Expanded NPV. Furthermore, the total potential economic value of water resources loss resulting in unsustainability was IDR 1,137,621,671,375 or IDR 1.14 trillion. In contrast, the potential economic value of depleted water utilization for drinking was IDR 2,298,339,797,000 or IDR 2.3 trillion. Therefore, this utilization provides a potential economic value worth IDR 1.16 trillion for the resources’ sustainability in the TAL area of PTBA. The study found and recommended depleted water utilization for drinking as a suitable method to replace water resources lost due to erosion and community drinking water resource loss and to discover a sustainable environment’s sustainability concept. In addition, the study formulates environmental economics as a new mining science related to natural resource economics and mining for sustainable water resources and the mining environment
IoT-Based Air Conditioning Control System for Energy Saving
Get PDFA high portion of electric consumption is air conditioning in householders or offices, reaching 42%. Therefore, it is necessary to automatically regulate air conditioning operations for energy saving using a control system, usually involving its hardware. As another option, the control system also involves electronic devices and software. This research developed an automatic control system using an ESP32 microcontroller integrated into the Blynk-based internet of things (IoT) for energy-saving air conditioning. The ESP32 was programmed using Arduino IDE and combined with a motion sensor to maximize energy saving. The motion sensor was a trigger to turn on the system. The recorded data were current, voltage, power, temperature, and motion detection. Based on the recorded power, the consumed energy was computed using trapezoidal and Simpson's composite rules of numerical integrations and ordinary methods. The testing was conducted in conventional, manual, and automatic operations. The yielded automatic control system operated adequately. The testing results revealed that the automatic operation saved 1.15 kWh (15.00%),0.99 kWh (13.66%), and 1.14 kWh (14.87%) average daily energy compared to the conventional operation, respectively, by using the ordinary, Simpson's composite rule, and trapezoidal composite rule computations. While the automatic compared to the manualmethodssaved1.68 kWh (20.44%), 1.78 kWh (22.14%), and 1.66 kWh (20.24%), respectively, for the same computations. Thus, the automatic system considerably saved energy compared to conventional and manual operations. Moreover, these energy savings were also higher than some previous research on air conditioning energy savings
The Effect of Consequences in Utilizing Real Estate Investment Trust (REIT) on Property Development
Get PDFThere are many financing sources in the property development investment process. Conventional financing often produces an un-optimal and unprofitable cost of capital. Real Estate Investment Trust (REIT) is one of the alternative financings that has been applied in global property projects. This financing strategy can be used as an option in property development in Indonesia. Real estate companies in Indonesia understand the development of REITs and also the advantages of using REITs. But still doubtful about the implementation. This study examines the consequences of using REIT and its influence on financing for developers. Two research methods were carried out. The first is a meta-analysis to determine the consequences of using REITs based on previous research, and the second is a questionnaire survey to confirm the results of the meta-analysis to the respondents of developer companies in Surabaya. Data were then analyzed using multiple linear regression. The findings indicate that the consequences of REITs are tax advantage, financial report transparency, 90% dividend distribution obligations, and the need to enter the capital market or acquire property. Then from the statistical results, it is found that the necessity to enter the capital market or acquire property is the most significant consequence of the decision to use REIT. These consequences affect the decision to use REIT by 40.4%, which means that the effect is considered weak, and all of the independent variables positively influence the dependent variables