Engineering Headway Vol. 27

Abstract: Traffic congestion has been a major problem in big cities, including Yogyakarta, with negative impacts including time, economic, and psychological losses. Based on data from the Yogyakarta Special Region Transportation Agency and Yogyakarta City Transportation Agency, analysis of congestion level data, and field observations, it was found that one of the main causes of congestion on the most congested roads in Yogyakarta City is vehicles parked on the side of the road. The proposed solution involves roadside parking detection and warning using surveillance cameras integrated with Artificial Intelligence (AI). The proposed system involves vehicle detection with pre-trained deep learning models, parking detection algorithms with Intersection over Union (IoU) tracking, and alerts that are forwarded to motorists as well as authorities such as the Transportation Department and local traffic police. The Yogyakarta CCTV dataset is used to test parking detection using various models, such as YOLOv5-medium, YOLOv5-large, YOLOv7-tiny, and Haar Cascade. The model evaluation shows that YOLOv5-large provides the highest accuracy of 86.1% with a processing speed of 5.5 Frames Per Second (FPS) to perform parking detection. With this proposed system, this research can contribute to solving congestion problems and improving traffic conditions in Yogyakarta City.

Abstract: Halal tourism is a rapidly growing industry that caters to the needs of Muslim travelers worldwide. This field’s research is also becoming more intriguing since it offers products such as halal restaurants, sharia hotels, banking, medicines, and finance. With the increasing popularity of halal tourism, it is essential to understand the sentiments of tourists regarding halal tourism products and services. Sentiment analysis is a useful tool to extract the polarity of reviews and opinions about products or services. In this paper, we present a survey of existing literature on sentiment analysis in halal tourism and the use of machine learning techniques or extracting sentiment polarity from reviews. We discuss various machine learning algorithms, including Random Forest, Naive Bayes, Support Vector Machine, Decision Tree, Convolutional Neural Network, BERT, and attention-based models, used for sentiment analysis in halal tourism. Our survey also covers various aspects of halal tourism, including food, cosmetics, hotels, and finance sectors. This paper highlights the importance of sentiment analysis in halal tourism and provides insight for future research.

Abstract: The COVID-19 pandemic has posed unprecedented challenges worldwide, requiring efficient and effective knowledge management strategies to aid in mitigation efforts. This paper introduced the Knowledge Management Analytic Dashboard (K-MAD) to support decision-makers, healthcare professionals, and policymakers in combating the spread of COVID-19. K-MAD leverages data analytics, and knowledge representation techniques to aggregate, analyze, and visualize diverse information sources related to the pandemic. The proposed dashboard provides real-time updates on the number of initiatives, project catalog, initiatives model, problem-based approach, and the methods, enabling stakeholders to make data-driven decisions promptly. K-MAD incorporates sophisticated data integration mechanisms to gather data from various sources, including government reports, public health agencies, research papers, and social media, ensuring comprehensive coverage and accuracy. The dashboard has a user-friendly interface caters to users with varying levels of technical expertise, promoting accessibility and usability. Customizable visualizations and interactive elements empower users to explore data from different angles and obtain valuable insights quickly. The paper concludes with a validation study, demonstrating K-MAD's efficacy in supporting decision making processes during the pandemic. Knowledge Management Analytic Dashboard presented in this paper offers a powerful and comprehensive tool to support COVID-19 mitigation efforts.

Abstract: Graph coloring is one of the common problems encountered in scheduling optimization. In the context of school scheduling, graph coloring is used to efficiently allocate time for various school activities. The objective of this research is to apply the Tabu Search algorithm in solving the graph coloring problem for school scheduling. This study utilizes the Tabu Search algorithm as an optimization method to find schedule solutions that satisfy all given constraints. The Tabu Search algorithm combines local search with a broader exploration mechanism. The Tabu Search algorithm is implemented by considering various important factors such as the number of subjects, time constraints, and specific preferences. In the graph coloring modeling, each subject is represented as a graph node, while the relationships between subjects that cannot take place at the same time are represented by graph edges. The results of this research show that the Tabu Search algorithm is capable of producing schedule solutions that meet all the given constraints. The found solutions have advantages in efficient time allocation, avoiding overlaps between activities, and considering the preferences compared to manual scheduling. This research contributes to the field of school scheduling by utilizing the Tabu Search algorithm from graph coloring.