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

Fri, 30 Aug 2024 00:00:00 +0000

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

Hendrawan Armanto, Harits Ar Rosyid, Muladi Muladi, Gunawan Gunawan — Mon, 11 Nov 2024 00:00:00 +0000

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

Mohammad Isa Irawan, Nadhifa Afrinia Dwi Putris , Noryanti binti Muhammad — Sun, 22 Dec 2024 00:00:00 +0000

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

Al Haris, Muhammad Dzeaulfath, Rochdi Wasono — Wed, 25 Dec 2024 00:00:00 +0000

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%).

Analysis of Organizational Development Strategy Through Achieving Maturity Levels of Business IT Alignment

Oktalia Juwita, Fajrin Nurman Arifin, Rachma Ailsya — Wed, 25 Dec 2024 00:00:00 +0000

Utilization of IT as a tool for achieving company goals must be balanced with efficiency in its management, so that it can increase superiority in business competition. The application of appropriate technology is not only limited to use, but also requires alignment, because this will have an impact on the formation of business strategy. Strategic alignment is the alignment between business strategy and IT as demonstrated through the correct and timely implementation of technology, in harmony with business strategy, goals and needs. Strategic alignment is the extent to which IT application processes, infrastructure and organization are used to build strategies and business processes and develop them. Measuring the maturity of the business-IT alignment is very necessary, so that system implementation can support existing business activities. By measuring business and IT strategies, companies can find out the level of company condition and what steps should be taken to build better performance. Strategic Alignment Maturity Model (SAMM) is considered comprehensive and well-established in assessing the business-IT alignment. The output produced from this SAMM is in the form of a value for the maturity level of business-IT alignment. The alignment value will be used as a reference in developing recommendations to formulate a company development strategy and increase the level of alignment between business and IT strategies

Handwritten Javanese Character Reconstruction with Extensive Damage Areas Using Deep Learning-Based Inpainting Techniques

Fitri Damayanti, Eko Mulyanto Yuniarno , Yoyon Kusnendar Suprapto — Tue, 31 Dec 2024 00:00:00 +0000

Ancient Javanese manuscripts stored in several museums have been damaged, so some parts of the script are missing. The requirement for repairing broken Javanese script is maintaining the correct stroke shape and consistent font style. The Javanese script has similarities between the letters, so if there is damage to the area that is characteristic of the script, it will not be easy to reconstruct. In addition, the reconstruction of the Javanese script will also experience difficulties if the damage area is large. Character improvement can be done using character inpainting techniques. Due to the limitations of textual data, more research on character inpainting needs to be done. In this study, the implementation of the character painting technique is proposed to reconstruct the handwritten Javanese script with the condition of a wide area of damage or damage to the area that is characteristic of the script. The proposed method uses Deep Learning architecture, namely Convolutional Autoencoder, Partial Convolutional Neural Network, UNet, and ResUNet. These methods were evaluated qualitatively and quantitatively on a dataset consisting of 12000 handwritten Javanese characters. Restoration of missing character sections is evaluated using SSIM and PSNR metrics. Overall, the ResUNet method achieves the best performance compared to other methods. Because the ResUNet method has the concept of residual learning introduced in ResNet.

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

Tue, 31 Dec 2024 00:00:00 +0000

The Urban Heat Island (UHI) phenomenon causes significant temperature increases in urban areas, adversely affecting the environment and public health. This research develops a prediction model of land surface temperature in Malang City using Support Vector Regression (SVR) with remote sensing data from Landsat-8, Sentinel-2, and SRTM. A cloud masking process is applied to improve image quality, while features such as NDVI, NDBI, NDWI, NDMI, elevation, and LST are calculated and normalized. The test results show that the Radial Basis Function (RBF) kernel with hyperparameters C = 10, Epsilon = 0.1, and gamma = 1 provides the best performance, with R² of 0.887, MSE of 1.625, and MAPE of 2.71%. This study shows that SVR with RBF kernel and appropriate tuning parameters can improve prediction accuracy. These results provide a strong basis for the development of more effective prediction models in managing UHI in big cities