Factors Influencing Public Intention to Use the Kepahiang Local Tax Mobile Application: An Adapted UTAUT Perspective

This study aims to identify factors that influence the public’s behavioral intention to use the Kepahiang Local Tax Mobile Application. Developed by the Regional Government of Kepahiang Regency through the Revenue Division of the Regional Financial Agency, the application facilitates local tax payments, particularly for PBB-P2 (Rural and Urban Land and Building Tax). The research adopts an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, incorporating additional variables such as Computer Self-Efficacy and Cost of Service, along with original UTAUT constructs: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. It also examines moderating variables including Gender, Age, and Experience. Data were gathered through a questionnaire distributed to 152 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that Performance Expectancy and Social Influence significantly and positively affect Behavioral Intention to use the application, whereas Cost of Service shows a negative influence. Furthermore, Gender is found to moderate the relationship between Social Influence and Behavioral Intention. These findings offer insights into the key factors influencing the adoption of government mobile applications, serving as a useful reference for policymakers aiming to increase user acceptance and enhance the effectiveness of digital public services

Sentiment Analysis of Twitter Towards the Free Lunch Program Using the C4.5 Algorithm

This study analyzes public sentiment towards the Makan Siang Gratis (Free Lunch) Program on social media X using the C4.5 algorithm. This program, which was initiated as a campaign promise in the 2024 Election, aims to provide free nutritious food for school students in Indonesia. Given the high public interaction on social media, this study was conducted to determine the public response to the program, which can be positive, neutral, or negative sentiment. The methods used include data collection from social media X, text pre-processing, sentiment labeling, application of Term Frequency-Inverse Document Frequency (TF-IDF), and model evaluation with accuracy metrics. The dataset consists of 3,344 tweets which are then classified using the C4.5 algorithm. Based on the evaluation results, it produces an average precision value of 79%, recall of 76%, F1-score of 77%, and is able to provide an accuracy of 78%. Thus, this model shows effective performance in classifying public sentiment. This study can contribute to the use of social media sentiment analysis as a tool for public policy evaluation

The Importance of Literacy on Artificial Intelligence for the Higher Education Students: A Systematic Literature Review

The rapid development of AI technology makes AI literacy crucial in providing individuals with an understanding of the essential functions of AI and its ethical application in higher education. This study used a scoping literature review method by searching the Scopus, Web of Science, Science Direct, and Sage Journals databases. Based on the search results, the eligibility criteria data were analyzed. Authors found as many as 153 pieces of literature, and eleven were declared to meet the eligibility criteria for the literature reviewed in this study. This study shows that AI literacy is essential in higher education. Educators and higher education institutions are responsible for providing programs that support the development of AI literacy skills in students. The application of AI literacy for students in higher education is essential in dealing with the development of AI technology. However, the lack of studies that address the evaluation of the importance of AI literacy and its implications limits the in-depth understanding of this topic

Comparison of Text Classification Techniques in Fake News Detection in the Digital Information Age

A comparison of text classification techniques for detecting fake news in the digital information age has been discussed in this study, with a focus on the application of Deep Learning methods, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The increasing spread of fake news through digital platforms emphasizes the importance of developing effective methods for identifying inaccurate information. In this study, a news dataset was collected from various sources, and both models were applied for text classification analysis. The performance of the model was then measured based on accuracy, precision, recall, and F1-score. The results showed that although both have their own advantages, better results in terms of processing speed and classification accuracy were found in CNN compared to RNN. These findings provide important insights for the development of more efficient and effective fake news detection systems in the digital age

A Hybrid Model of Graph Attention Networks and Random Forests for Link Prediction in Co-Authorship Networks

Co-authorship prediction is important in academic network analysis due to it helps to understand patterns of scientific collaboration and supports collaboration recommendation systems. Topology-based approaches, such as connectivity metrics and node distance, have been widely used to model new relationships in networks. However, these approaches often overlook relevant author attributes, such as reputation and productivity. This study develops a co-authorship prediction model by combining a Graph Attention Network (GAT) and a Random Forest. GAT is used to extract topological features from the co-authorship graph, while Random Forest leverages additional attributes such as h-index and the number of publications to improve prediction accuracy. Experiments were conducted on a co-authorship dataset comprising over 10,000 authors and 50,000 publications. The results show that GAT achieved 85% accuracy, while Random Forest reached 80%. The combination of the two yielded 90% accuracy and a higher F1-score, indicating a better balance between precision and recall. The combined model also proved more accurate in predicting collaborations involving highly productive authors. These findings suggest that a hybrid approach can more comprehensively capture the dynamics of academic collaboration and may serve as a foundation for developing more effective collaboration prediction systems in the future

Grid-Based Ship Density Analysis and Anomaly Detection for Ship Movements Monitoring at Tanjung Priok Port

Indonesia, as a maritime country, depends on ports to support inter-island transport and a smooth regional economy. So, the awareness of knowing the marine status with various platforms is needed. This research distinguishes itself from several previous studies on ship movement detection by concentrating specifically on anomalies in ship movement within areas of high traffic density. This research proposes to find out the ship density area using the grid technique and identify the anomalies that have occurred, as information on ship movements at Tanjung Priok Port. Anomaly detection is done by looking for it through visualization, where AIS data is converted into a form of visualization using the Python language. The results obtained two pieces of information, namely that the areas with the highest density are around the harbor, docks, and ship lanes. Then, two types of anomalies were detected, namely large ships with dangerous cargo speeding in dense areas and ships that behave differently compared to other ships with the same status

Analysis of Factors Influencing the Intention to Use QRIS As a Payment Tool in Central Kalimantan Province

In recent years, the Quick Response Code Indonesian Standard (QRIS) has emerged as a digital payment system in Indonesia. Central Kalimantan has witnessed a substantial increase of 91% in the number of merchants adopting this system in 2021 compared to previous years. However, the significant growth of merchants with QRIS options has not yet had the expected increase in usage frequency. This research aims to identify the factors influencing the utilization of QRIS as a payment method and to formulate recommendations to enhance its adoption. Employing a quantitative method, this study modifies the Technology Acceptance Model (TAM) by including external variables such as Subjective Norm, Perceived Security based on preliminary research to understand their impact on Behavioral Intention to use. Data calculation and analysis were conducted using the SmartPLS 3 tool. The findings reveal that Attitude Toward Using exerts a significant influence on Behavioral Intention to Use. While Subjective Norm, Perceived ease of use and Perceived Usefulness significantly affect usage intention through Attitude Toward Using, this study highlights the potential for increased QRIS adoption by leveraging community figures or influencers in socialization efforts. Furthermore, enhancing perceived usefulness through merchant promotions and user education is crucial for fostering positive attitudes towards QRIS usage

Indonesian Sign Language (BISINDO) Classification Using Xception Transfer Learning Architecture

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 94% during testing

Customer Transaction Clustering with K-Prototype Algorithm Using Euclidean-Hamming Distance and Elbow Method

This study aims to cluster customer transactions in a Japanese food stall using the K-Prototype Algorithm with a combination of Euclidean-Hamming Distance and the Elbow method. Facing intense industry competition, this study seeks to understand customer purchasing behavior to increase loyalty and sales. From 9.721 initial entries, 9.705 cleaned and transformed records were analyzed. K-Prototype was chosen because of its ability to handle numeric features (Total Sales, Product Quantity) and categorical features (Payment Method, Order Type, Day Category and Time Category). The combination of Euclidean-Hamming distances was used for distance measurement. The optimal number of clusters was determined using the Elbow method, with the results recommending three clusters as the most optimal number. A Silhouette score of 0.6191 indicates a Good Structure clustering result, effectively identifying three distinct customer grouping: "Loyal Regulars" (49.5%), "Casual Shoppers" (42.3%), and "Premium Shoppers" (8.2%). Statistical validity was also tested using ANOVA and Chi-Square, the results showed significant differences between the clusters in numerical and categorical variables with a p-value <0.0001. The clusters are statistically valid in both numerical and categorical aspects. These insights provide an understanding of customer characteristics and reveal a strategically valuable cluster for targeted marketing

A Content-Based Filtering Approach for Matching Village Potentials with Community Service Programs

Village development through the development of village potential is carried out by universities in the form of community service. With the community service program, it is expected to help villages in village development so that they can become independent villages. However, in its implementation, the designed assistance programs are not specific and not aligned with the needs and potential of the village. As a result, the assistance provided is less effective and having minimal impact on village development. One of the causes is the unavailability of data on village potential and problems systematically and structured. Based on these problems, a recommendation system is needed that is able to provide assistance program proposals that are in accordance with the potential and problems of the village specifically and relevantly. This research uses Content-Based Filtering which provides recommendations based on the similarity of input data content with available historical data. The purpose of this research is to make the planning and implementation process of village assistance programs more efficient, effective, and on target. The results of the research are that the Content-Based Filtering method has proven effective in providing recommendations that are appropriate for mapping village potential and village assistance programs