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A VIKOR-Based Decision Support System for Prioritizing Public Facility Improvements in Malang City with Geotagging Integration

2024-06-12T04:11:34+00:00

Public facilities play a crucial role in driving economic growth and development. Nevertheless, the dearth of public information concerning facility enhancements fosters a sense of public distrust towards the government. Additionally, numerous facilities, which should be prioritized for improvement, have not received adequate attention. In contrast to several prior studies, the present study encompasses a broader scope and incorporates geotagging techniques to precisely identify the location of complaints and determine the optimal route to reach them. Moreover, an analysis process utilizing the VIKOR method has been devised to assess the priority of public facility improvements. This method yielded an accuracy rate of 89,7%, signifying a commendable level of precision and a 16% increase in accuracy based on confusion matrix method compared to previous studies. Through user usability testing, it was determined that the majority of users agreed that this system can facilitate public reporting, enable progress monitoring of public facility improvements, and aid in prioritizing such improvements.

Utilization of the Particle Swam Optimization Algorithm in Game Dota 2

2024-09-27T22:34:57+00:00

Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files.

Customer Churn Prediction Using the RFM Approach and Extreme Gradient Boosting for Company Strategy Recommendation

2024-10-12T15:10:22+00:00

Customers are vital assets in the growth and sustainability of business organizations. However, customers may discontinue their engagement with a company and switch to competitors’ products or services for various reasons. This event referred to as customer churn. Losing customers significantly impacts a company's revenue, often resulting in financial decline. Churn events, which are subject to dynamic monthly changes, are further influenced by intense competition and rapid technological advancements. Analyzing customer characteristics is crucial to understanding customer behavior, with metrics such as recency, frequency, monetary (RFM) serving as key indicators of subscription and transaction patterns. The Extreme Gradient Boosting method is applied to address the challenge of classifying churn and non-churn customers. The prescriptive analytics process is carried out to identify the features most influential in prediction outcomes, enabling the formulation of strategic recommendations to mitigate churn problems. The integration of RFM analysis with the XGBoost method provides optimal results, particularly in the third segmentation, achieving an accuracy of = 0.98833, precession = 0.98768, recall = 0.98899, and f1-score = 0.98833. The prescriptive analytics process highlights three critical features, namely city factor, GMV generation, and total customer transaction generation. This findings demonstrate that the segmentation characteristics, data representation, and behavioral approach with RFM analysis have an effect on improving the performance of the model in churn prediction.

Principal Component Analysis on Convolutional Neural Network Using Transfer Learning Method for Image Classification of Cifar-10 Dataset

2024-10-12T15:50:58+00:00

The current era was defined by an overwhelming abundance of information, including multimedia data such as audio, images, and videos. However, with such an enormous amount of image data available, accurately and efficiently selecting the necessary images poses a significant challenge. To address this, image classification has emerged as a viable solution for organizing and managing large volumes of image data, thereby mitigating the issue of cluttered image datasets. One of the most popular algorithms for image classification is the Convolutional Neural Network (CNN), which reduces the complexity of network structure and parameters by leveraging local receptive fields, weight sharing, and pooling operations. CNN is a type of artificial neural network specifically designed to process grid-like data, such as images, using convolutional layers to automatically detect local features. Nonetheless, CNN faces several challenges, such as gradient diffusion, large dataset requirements, and slow training processes. To overcome these issues, Transfer Learning has been widely adopted in CNN-based image classification, and Principal Component Analysis (PCA) has been employed to accelerate the training process. PCA is a technique used to reduce data dimensionality by identifying the principal components that account for most of the variance in the data. This study tested the efficacy of PCA-based CNN architecture using the Transfer Learning method on the Cifar-10 dataset. The results demonstrated that the PCA-based CNN architecture achieved the highest accuracy, with a testing accuracy rate of 0.8982 (89%).

Data Visualization of the Maturity Level From the Perspective of Business-IT Alignment at PT. XYZ

2024-10-12T15:03:17+00:00

This article presents descriptive information about the results of the performance analysis and the maturity of the business-IT alignment process at PT.XYZ through the implementation of data-based visualization techniques within the SAMM perspective. This research is divided into four stages: preliminary research, data collection, data analysis, and organizational development analysis. Based on the results of the analysis using SAMM from the perspective of top-level management of an organization, PT. XYZ has achieved maturity in the attribute of good cooperation. This indicates that the company and organization understand the need for business-IT alignment for their development. However, the attainment level of maturity in the skills and communication attributes is less than optimal. This impacts the communication process and the distribution of information in the company’s development through IT implementation. This research only provides data visualization of the maturity achievements of PT. XYZ’s business alignment and information technology are based on the SAMM method. The process of analyzing the company's strategic alignment data and providing recommendations for implementing Information Technology to optimize the company's business was not included in this research.

Deep Learning-Based Inpainting for Reconstructing Severely Damaged Handwritten Javanese Characters

2024-09-13T14:39:40+00:00

Long-term storage at museums can damage ancient Javanese manuscripts; for instance, temperature changes and other factors may cause parts of the script to disappear. The Javanese script exhibits similarities among its letters, making the reconstruction process challenging, particularly when dealing with severe damage to the script's characteristic areas. To address this issue, we conducted a character painting technique that utilizes deep learning architecture, specifically the convolutional autoencoder, partial convolutional neural network, UNet, and ResUNet. The dataset contains 12,000 handwritten Javanese characters. We evaluated the restoration of missing characters using SSIM and PSNR metrics. The ResUNet achieves the best performance compared to other methods, with an SSIM value of 0.9319 and a PSNR value of 18.9507 dB. According to this study, the ResUNet models can reconstruct Javanese manuscripts with strong performance, offering an alternative solution to ensure the preservation and accessibility of these valuable historical documents for future generations.

Improving Urban Heat Island Predictions Using Support Vector Regression and Multi-Sensor Remote Sensing: A Case Study in Malang

2024-10-20T08:19:15+00:00

The Urban Heat Island (UHI) phenomenon is characterized by higher temperatures in urban areas compared to surrounding rural areas. This condition poses various environmental risks and adversely impacts public health, particularly in Malang, Indonesia. This study aims to predict land surface temperature (LST) in Malang to better understand and mitigate the effects of UHI's. Support Vector Regression (SVR) is employed using remote sensing data from Landsat-8, Sentinel-2, and SRTM. Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), elevation, and LST are calculated and normalized to ensure accurate data representation. Model testing results indicate that the Radial Basis Function (RBF) kernel performs best with hyperparameter settings of C = 10, Epsilon = 0.1, and gamma = 1. This model achieves an R² of 0.887, an MSE of 1.625, and a MAPE of 2.71%. These findings confirm that SVR with an appropriately tuned RBF kernel can improve prediction accuracy. Consequently, the study provides a robust foundation for developing more effective predictive models to address UHI management in urban areas.