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Application of Neural Network Method to Determine Public Satisfaction Level on Pertalite Fuel
No full textThis research aims to analyze public interest in Pertalite fuel using the Data Mining method, specifically using the Neural Network method. The stages in this research include Data Analysis, Data Preprocessing, Designing Classification Models in Data Mining, Classification Results in Data Mining, Designing Evaluation Models in Data Mining, and Evaluation Results on Data Mining. The classification results show that of the total of 105 community data analyzed, 97 community data showed interest in Pertalite fuel, while only 8 community data showed no interest. The accuracy results obtained were 100%, indicating that the Neural Network method is very suitable and effective in classifying people's interest in Pertalite fuel. The Data Analysis process was carried out to understand and analyze the characteristics of data regarding public interest in Pertalite fuel. Data preprocessing is carried out to clean, transform and integrate data so that it is ready for the classification process. Next, the Designing Classification Models in Data Mining process is carried out to design a classification model using the Neural Network method. Classification Results in Data Mining produces information that the majority of people have an interest in Pertalite fuel. Designing Evaluation Models in Data Mining is carried out to design classification evaluation models, which then produce Evaluation Results on Data Mining which show an accuracy level of 100%. Thus, this research shows that the Neural Network method is very effective in classifying people's interest in Pertalite fuel
An Enterprise Architecture in the Construction Management Software using the Business Model Canvas
No full textIndonesia's construction industry has boomed over the past decade, acting as a powerful engine for the nation's economic growth. However, this success story comes with a growing list of challenges. Construction projects are becoming increasingly intricate, demanding not only efficiency and quality but also enhanced safety and a minimized environmental footprint. To tackle these complexities, the industry is undergoing a crucial transformation towards "smart construction." This approach leverages the power of information and communication technology (ICT) throughout the entire project lifecycle, from planning and design to execution and maintenance. By integrating ICT tools like Building Information Modeling (BIM) and cloud-based project management platforms, smart construction streamlines processes, minimizes errors, and optimizes resource allocation, ultimately leading to improved efficiency, enhanced worker safety, and a reduction in environmental impact. A key pillar of smart construction lies in the integration of enterprise architecture (EA) within construction management software. EA provides a structured framework for aligning IT systems with construction businesses' specific goals and objectives. This ensures that software development and management are adaptable to evolving industry needs and foster continuous innovation. This research delves into the application of EA within the construction management software industry, specifically focusing on the TOGAF Architecture Development Method (ADM). By exploring EA through this established framework, the research aims to shed light on how the construction industry in Indonesia can leverage technology to its fullest potential. This will ensure continued growth within the sector and contribute to a lasting positive impact on the nation's economy
Performance Comparison of KNN and CNN in Classifying Balinese Gangsa Instrument Tones
No full textBalinese traditional music, particularly the Gamelan Gangsa, represents a unique aspect of Indonesia’s cultural heritage. Despite its cultural significance, the study and teaching of this instrument face challenges, particularly in tone standardization and the availability of effective learning tools. This research addresses these challenges by exploring the application of Artificial Intelligence (AI) technologies specifically K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN) in the identification and classification of Gamelan Gangsa tones. The study involved the creation of a dataset comprising audio recordings of the instrument, followed by the development and evaluation of KNN and CNN models. The results indicate that KNN, with an accuracy of 90%, outperformed CNN, which achieved an accuracy of 85%. The findings suggest that KNN is particularly effective in distinguishing subtle tonal differences, making it a valuable tool for supporting traditional music education. This research not only contributes to the technical understanding of Gamelan Gangsa’s acoustic characteristics but also underscores the potential of AI in cultural preservation. The development of AI-based tone identification systems can facilitate the teaching and learning of traditional music, ensuring its transmission to future generations. The study serves as a foundation for further exploration into the integration of AI technologies with cultural heritage, demonstrating how modern innovations can enhance the appreciation and understanding of traditional arts
Enterprise Architecture of the Basic Banking Feature for a New Challenger of Digital Banking in Indonesia
No full textDigital transformation has significantly impacted Indonesia's banking industry, leading to the rise of digital banks that leverage technology for their operations, posing challenges to traditional banking models. This research investigates the implementation of enterprise architecture within the core features of digital banking in Indonesia, utilizing the TOGAF framework and Archimate modeling. The study's primary objective is to identify the core processes, challenges, and opportunities associated with managing the complex architecture of digital banks. Employing a qualitative methodology, data were gathered through in-depth interviews, direct observations, and a review of pertinent literature. The research identified three central processes in digital banking operations: deposits, time deposits, and loans. These processes were then modeled using the TOGAF framework and Archimate to align business strategies with operational activities more effectively. The SWOT analysis conducted highlights digital banks' strengths in operational efficiency, strategic partnerships, and innovation capabilities, while also recognizing weaknesses such as technological dependency and challenges in serving the less tech-savvy population. The study also identifies opportunities for product innovation, market expansion, and ecosystem integration. However, threats like regulatory changes, increased competition, and cybersecurity risks must be carefully managed. The research recommends adopting emerging technologies, enhancing third-party risk management, and improving customer data security and privacy to bolster digital banks' global competitiveness, operational sustainability, and service innovation
Optimization of Backpropagation Method with PSO to Improve Prediction of Land Area and Rice Productivity
No full textThis research aims to optimize the Backpropagation method using Particle Swarm Optimization (PSO) optimization to improve the accuracy of prediction of harvest area and rice productivity. The results show that the best architecture for prediction of harvest area is 3-15-1, with a Mean Squared Error (MSE) value of 0.0049980 for standard Backpropagation, and 0.00092376 after being optimized with PSO. Meanwhile, for rice productivity prediction, the best architecture is also 3-15-1, with an MSE value of 0.0049998 for standard Backpropagation, and 0.000435762 after using PSO. PSO optimization significantly reduces the MSE value, which indicates that this method is more accurate than standard Backpropagation. Predictions from 2024 to 2026 show more consistent results with some provinces experiencing an increase or decrease in harvested area and rice productivity that is different from the standard Backpropagation method. Based on the prediction accuracy that reaches 100% and the lower MSE value, it can be concluded that Backpropagation with PSO optimization is a superior method. The results of this study are useful for government, farmers, researchers, and policy makers in more effective agricultural planning and better risk managemen
Data Visualization for Building a Cyber Attack Monitoring Dashboard Based on Honeypot
No full textComputer networks are essential for modern life, enabling efficient global information exchange. However, as technology advances, network security challenges grow. To enhance security, honeypots are used alongside firewalls, mimicking legitimate systems to attract hackers and analyze their attack methods. In this research, Cowrie and Dionaea honeypots are implemented. Cowrie targets brute force attacks on SSH, while Dionaea detects port scanning and denial of service (DoS) attacks. These honeypots effectively capture and log malicious activities, providing insights into attack patterns. The collected data is analyzed using the ELK Stack, which offers real-time visualization of attack trends, frequency, and methods. This analysis helps security teams quickly identify and mitigate threats. The integration of honeypots with the ELK Stack significantly enhances network defense by improving detection, analysis, and response to cyber threats. The analysis of the results shows that both honeypots effectively capture and record malicious activities entering the network, providing critical insights into the attack patterns employed by attackers. Within just minutes of deployment, the honeypots logged over 1,000 attacks, predominantly originating from botnets attempting to exploit system vulnerabilities. The captured log data is processed through the ELK Stack, allowing for real-time visualization of attack patterns, including geographic origins, attack frequency, and methods used. This enables security teams to proactively identify trends, assess risks, and implement targeted mitigation strategies more efficiently
E-Homestay Application Based on Decision Support System for Optimizing Tourism
No full textPagar Alam City, a growing tourist destination, has seen a steady increase in visitors each year, driving greater demand for accommodations, especially homestays. Homestays are often favored by tourists due to their affordability compared to hotels. However, many tourists face challenges in selecting a suitable homestay that meets their preferences and needs. To address this issue, this study proposes the development of a web-based Decision Support System (DSS) integrated into the e-homestay platform. The system utilizes the Simple Additive Weighting (SAW) method, chosen for its capability to assess multiple alternatives based on specific weighted criteria, including price, facilities, location, distance, and guest ratings. This approach is designed to assist tourists in identifying the optimal homestay that aligns with their preferences and budget, thereby enhancing their overall travel experience in Pagar Alam City. Moreover, the platform has the potential to promote local economic growth by supporting digital marketing of homestays, while also contributing to sustainable tourism development and management
UX Analysis on SpeedID Application Using Usability Testing Method and System Usability Scale
No full textThe SpeedID application is a smart city application developed by a subsidiary of PT Bamboomedia Cipta Persada, namely PT Inovasi Solusi Nusantara since 5 years ago. The SpeedID application wants to present a solution to the city's problems to become a new digital identity for the smart city community. Because it was only developed 5 years ago, the SpeedID application is classified as a new application and has never been analyzed for usability before. Usability analysis is carried out to improve user experience, so that the SpeedID application can be accepted and used more easily by users. This research uses Usability Testing method with Performance Measurement and Retrospective Think Aloud (RTA) techniques and System Usability Scale (SUS). The results obtained are the SpeedID application has a quality that cannot be said to be effective, efficient and meet user satisfaction. In addition, the average score of the System Usability Scale (SUS) Questionnaire is 70.33. The score is rated "C" with an adjective rating of "Good" with the acceptance range included in the "Marginal" category, and finally the net promotion score (NPS) is included in the "Passive" category, which explains that the use of the SpeedID application gets an assessment that is still marginalized by its users. This shows that the SpeedID application still urgently needs correction to improve quality to its users and design improvements also need to be made so that the SpeedID application is even better at meeting user expectations in the future
Student Organization Website with E-Voting Feature by Using Student Card Verification Concept Design
No full textStudent organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal result
Whoosh User Sentiment Analysis on Social Media Using Word2Vec and the Best Naïve Bayes Probability Model
No full textBy using the Twitter microblogging feature, users can post short tweets with limited characters that express their thoughts and opinions regarding a matter. The newest transportation in Indonesia, a high-speed train namely Whoosh is one of the things that Twitter users responded to. This latest transportation has led to the emergence of opinions from the Indonesian people which are shared publicly in various media, one of which is social media. Therefore, to make it easier for business people or companies to understand public opinion regarding service improvements in the future, sentiment analysis on social media is needed to determine user opinions regarding high-speed train transportation. In this research, sentiment analysis of high-speed train users will be carried out on social media Twitter using Word2Vec and Naïve Bayes as classification methods. In this research, a comparison of Naïve Bayes models will also be carried out to find out the best Naïve Bayes method opportunity model. Simultaneously, the Word2vec feature extraction method was chosen because Word2Vec can be used to improve model performance and increase the accuracy of sentiment classification. This research found that the Word2Vec Skip-Gram model outperformed the Word2Vec CBOW model. The best model obtained was the use of the Gaussian Naïve Bayes and Word2Vec Skip-Gram models with an accuracy score of 77.18%, precision 70.35%, recall 76.09%, and f1-score 73.10%