45 research outputs found
Data Analysis of Social Media\u27s Impact on COVID19 Pandemic Users\u27 Mental Health
Full text linkSocial media has a significant impact on people\u27s daily lives and spread widely. Unrestrained usage of social media could have worsening consequences on mental health. The majority of COVID-19 users who were exposed to social media learned numerous facts, which made their anxiety and depression-related mental health disorders worse. This study aims to determine how social media usage affects users\u27 mental health during the COVID19 pandemic. Through surveys and expert interviews, this study collects both quantitative and qualitative data. The total number of respondents involved was 106 with the average age group of 18-41-year-old. Using reliability testing (Cronbach alpha test) and inferential statistic (Pearson Correlation and Chi-Square), results show that during the COVID19 pandemic, there is a significant link between social media use and mental health. Anxiety and depression brought on by social media are more common among young adults, predominantly female, between the ages of 18 and 24 than in men. Additionally, correlation plot analysis with a variety of queries reveals the mental health issues and activities on social media
Perancangan Sistem Informasi Akuntansi Penjualan Menggunakan Standar Akuntansi EMKM
No full textThis study intends to design a sales accounting information system for UMKM Toko Utara Game. The purpose of this study is to design a sales information system with EMKM standards at Toko Utara Game that can be implemented as one of the human error risk management that might occur if the system is run conventionally and not computerized. The research method used in this study is a descriptive research method, in which the author analyzes and describes events that occur in the present with the intention of overcoming problems that occur at Toko Utara Game. The types of data used in this research are primary data and secondary data. Data collection techniques used in this study were interviews, observation and literature studies. The results of interviews and observations are used as primary data and the results of literature studies are used as secondary data. The system development methodology used in this study is the prototyping method with an object-oriented system design method using UML (Unified Modeling Language). The system design made in this study is a Use Case Diagram, an Activity Diagram, and uses an Entity Relationship Diagram (ERD). At the stage of creating the program code, researchers used the Java and MYSQL programming languages which were poured into the NetBeans IDE8.1 software using XAMPP as a web server. The result of this implementation is the design of a web-based sales application so that Toko Utara Game sales management can be computerized properly.Penelitian ini bermaksud untuk merancang sistem informasi akuntansi penjualan pada UMKM Toko Utara Game. Tujuan dari penelitian ini adalah merancang sistem informasi penjualan dengan standar EMKM di Toko Utara Game yang dapat diimplementasikan sebagai salah satu manajemen risiko human error yang mungkin terjadi jika sistem dijalankan secara konvensional dan tidak terkomputerisasi. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian deskriptif, dimana penulis menganalisis dan mendeskripsikan kejadian yang terjadi pada masa kini dengan maksud untuk mengatasi permasalahan yang terjadi pada Toko Utara Game. Jenis data yang digunakan dalam penelitian ini adalah data primer dan data sekunder. Teknik pengumpulan data yang digunakan dalam penelitian ini adalah wawancara, observasi dan studi pustaka. Hasil wawancara dan observasi digunakan sebagai data primer dan hasil studi literatur digunakan sebagai data sekunder. Metodologi pengembangan sistem yang digunakan dalam penelitian ini adalah metode prototyping dengan metode perancangan sistem berorientasi objek menggunakan UML (Unified Modelling Language). Perancangan sistem yang dibuat pada penelitian ini adalah Use Case Diagram, Activity Diagram, dan menggunakan Entity Relationship Diagram (ERD). Pada tahap pembuatan kode program, peneliti menggunakan bahasa pemrograman Java dan MYSQL yang dituangkan ke dalam software NetBeans IDE8.1 dengan menggunakan XAMPP sebagai web server. Hasil dari implementasi ini adalah rancangan aplikasi penjualan berbasis web sehingga manajemen penjualan Toko Utara Game dapat terkomputerisasi dengan baik
New Approach to Image Segmentation: U-Net Convolutional Network for Multiresolution CT Image Lung Segmentation
Full text linkImage processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio. Doi: 10.28991/ESJ-2023-07-02-014 Full Text: PD
WSLC: Weighted semi-local centrality to identify influential nodes in complex networks
No full textIdentifying and ranking influential nodes in complex networks is a critical aspect to study the survival and robustness of networks. Many ongoing researches have proposed centrality metrics to address this problem, so that the performance of each is attributed to specific scenarios. For example, metrics based on local structure have low ranking accuracy due to the use of limited information, and metrics based on global structure suffer from high complexity. Meanwhile, metrics based on semi-local structure are amazingly well, but an efficient centrality for identifying influential nodes is still not available due to differences in the structure and scale of networks. In addition, most semi-local centrality metrics only consider one aspect of each node's information, and their development still faces serious challenges. This paper develops a Weighted Semi-Local Centrality (WSLC) to identify influential nodes in complex networks based on extended neighborhood concept. Here, several different weights are investigated to find the best performance on WSLC. We use the extended neighborhood concept to select the nearest neighbors, which considers the global information of the network in a limited and efficient way to calculate the ranks. Here, a distributed approach is presented that can cut a subgraph of the entire network for each node with low complexity. This subgraph contains neighbors with different hops, which are used to maintain high efficiency when facing large-scale networks. In addition to the importance of the node itself, WSLC also combines the importance of the node's nearest neighbors with different hops for ranking. Therefore, defining semi-local structure with a distributed approach as well as using an efficient edge weighting policy differentiates WSLC from other existing centrality metrics. The evaluation of WSLC has been done through several real-world networks using Kendall's correlation. The effectiveness of WSLC under the SIR infection spreading model has been verified by extensive simulations compared to state-of-the-art centrality metrics
BHE+ALBERT-Mixplus: A Distributed Symmetric Approximate Homomorphic Encryption Model for Secure Short-Text Sentiment Classification in Teaching Evaluations
No full textThis study addresses the sentiment classification of short texts in teaching evaluations. To mitigate concerns regarding data security in cloud-based sentiment analysis and to overcome the limited feature extraction capacity of traditional deep-learning methods, we propose a distributed symmetric approximate homomorphic hybrid sentiment classification model, denoted BHE+ALBERT-Mixplus. To enable homomorphic encryption of non-polynomial functions within the ALBERT-Mixplus architecture—a mixing-and-enhancement variant of ALBERT—we introduce the BHE (BERT-based Homomorphic Encryption) algorithm. The BHE establishes a distributed symmetric approximation workflow, constructing a cloud–user symmetric encryption framework. Within this framework, simplified computations and mathematical approximations are applied to handle non-polynomial operations (e.g., GELU, Softmax, and LayerNorm) under the CKKS homomorphic-encryption scheme. Consequently, the ALBERT-Mixplus model can securely perform classification on encrypted data without compromising utility. To improve feature extraction and enhance prediction accuracy in sentiment classification, ALBERT-Mixplus incorporates two core components: 1. A meta-information extraction layer, employing a lightweight pre-trained ALBERT model to capture extensive general semantic knowledge and thereby bolster robustness to noise. 2. A hybrid feature-extraction layer, which fuses a bidirectional gated recurrent unit (BiGRU) with a multi-scale convolutional neural network (MCNN) to capture both global contextual dependencies and fine-grained local semantic features across multiple scales. Together, these layers enrich the model’s deep feature representations. Experimental results on the TAD-2023 and SST-2 datasets demonstrate that BHE+ALBERT-Mixplus achieves competitive improvements in key evaluation metrics compared to mainstream models, despite a slight increase in computational overhead. The proposed framework enables secure analysis of diverse student feedback while preserving data privacy. This allows marginalized student groups to benefit equally from AI-driven insights, thereby embodying the principles of educational equity and inclusive education. Moreover, through its innovative distributed encryption workflow, the model enhances computational efficiency while promoting environmental sustainability by reducing energy consumption and optimizing resource allocation
Strategic Customer Segmentation: Harnessing Machine Learning For Retaining Satisfied Customers
Full text linkThis research paper explores the burgeoning field of machine learning and its application in strategic customer segmentation within the aviation industry. Leveraging the Airline Passenger dataset, this study assesses the potential of various machine learning classifiers to enhance customer retention by effectively segmenting satisfied customers. Our methodology involves a comparative analysis of five machine learning classifiers: Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN). Each classifier is rigorously tested and evaluated based on key performance metrics including accuracy, precision, recall, and F1-score.
The results indicate a diverse range of classifier effectiveness. Notably, the Random Forest classifier outperforms others with outstanding metrics: accuracy, precision, recall, and F1-scoreof 0.96. Decision Tree follows closely, also achieving high performance with a score of 0.95 across all metrics. Naive Bayes and ANN demonstrate respectable performance, with accuracyscores of 0.86 and 0.90 respectively. In contrast, KNN presents lower but consistent performance, with all metrics at 0.75. These quantitative findings highlight the nuanced performancedifferences among classifiers, emphasizing the critical role of algorithm selection in achieving precise customer segmentation.
This study provides significant insights into the application of machine learning for strategic customer retention in the aviation sector, presenting practical implications for airlines aiming to optimize their segmentation strategies and retain satisfied customers. By showcasing the varying performances of different classifiers, this research contributes to the broader discourse on integrating machine learning into customer-centric strategies, ultimately aiding airlines to engage and retain their customer base more effectively
Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series
No full textThis study seeks to test novel capabilities, specifically those of the hybrid fuzzy k-medoids (FKM) and cat and mouse-based optimizer (CMBO) partitioning approach, in overcoming the Markov weighted fuzzy time series (MWFTS) limitation in creating U talk intervals without fundamental standards. Researchers created a hybrid cat and mouse-based optimizer–fuzzy k-medoids (CMBOFKM) approach to be used with MWTS, since these limits may impair the accuracy of the MWFTS approach. Symmetrically, the hybrid method of CMBOFKM is an amalgamation of the FKM and CMBO methods, with the CMBO method playing a part in optimizing the cluster center of the FKM partition method to obtain the best U membership matrix value as the medoid value that will be used in the MWFTS’s fuzzification stage. Air quality data from Klang, Malaysia are used in the MWFTS–CMBOFKM technique. The evaluation of the model error values, known as mean absolute percentage error (MAPE) and root mean square error, yields the MWFTS–CMBOFKM evaluation findings that are displayed (RMSE). A 6.85% MAPE percentage and a 6071 RMSE score are shown by MWFTS–CMBOFKM using air quality data from Klang, Malaysia. The FKM partition approach can be hybridized with additional optimization techniques in the future to increase the MWFTS method’s precision
