This research focuses on exploring and understanding the role of Habit in sustaining the adoption and continuous use of AIKU technology as its primary objective. The UTAUT2 model was chosen, emphasizing the Habit variable (HT) along with 6 other key variables: Performance Expectancy (PEX), Effort Expectancy (EEX), Price Value (PV), Social Influence (SIN) on Behavioral Intention (BIN), and User Behavior (UB), with the addition of external variables Perceived Risk (PR) and moderating Gender and Experience. The research methodology involves data analysis using SmartPLS 4.0 software and the UTAUT2 model as the framework and theoretical foundation. Survey data were collected from 414 AIKU users in Indonesia. Findings based on data analysis indicate that factors such as PE, EE, and PV significantly positively influence BI to continue using AIKU. In addition to filling gaps in knowledge regarding the role of Habit in air quality technology literature, another finding is that H significantly positively influences BIN and UB, while BIN significantly positively influences UB. This research’s novelty and primary contribution lie in the implications concerning the crucial emphasis on the Habit factor in sustaining AIKU usage. The development implications underscore the importance of psychological factors in technology acceptance and retention, providing valuable insights for future strategy and policy development. Limitations of this research lie in the focus on the AIKU system affecting result generalization. Future research, expanding coverage, considering air quality system variations, employing diverse data collection methods such as in-depth interviews are recommended.

Air quality monitoring is increasingly relying on data-driven analytic decision-making tools to provide accurate and timely information, forming the background of this study. The objective is to understand the factors influencing the adoption and usage behavior of these tools using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The method involves incorporating UTAUT2 constructs Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), and Habit (H), alongside external variables such as Considered Risk (CR) and Considered Trust (CT). Data from 287 respondents were analyzed to assess their impact on Behavior Intention (BI) and Usage Behavior (UB). The results demonstrate that both trust and risk considerations significantly affect user behavior, underscoring the need to address these factors to enhance the adoption of air quality monitoring systems. In conclusion, this research provides valuable insights for developers and policymakers on improving the implementation and acceptance of data-driven technologies in environmental monitoring, thereby contributing to more effective air quality management.

Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption during the COVID-19 pandemic, as wearing face masks became an important measure to prevent the spread of the virus. Face mask detection helps to enforce mask-wearing guidelines, which can significantly reduce the spread of respiratory illnesses, including COVID-19. Wearing masks in densely populated areas provides individuals with protection and hinders the spread of airborne particles that transmit viruses. The application of deep learning models in object recognition has shown significant progress, leading to promising outcomes in the identification and localization of objects within images. The primary aim of this study is to annotate and classify face mask entities depicted in authentic images. To mitigate the spread of COVID-19 within public settings, individuals can employ the use of face masks created from materials specifically designed for medical purposes. This study utilizes YOLOv8, a state-of-the-art object detection algorithm, to accurately detect and identify face masks. To analyze this study, we conducted an experiment in which we combined the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD) into a single dataset. The detection performance of an earlier research study using the FMD and MMD was improved by the suggested model to a “Good” level of 99.1%, up from 98.6%. Our study demonstrates that the model scheme we have provided is a reliable method for detecting faces that are obscured by medical masks. Additionally, after the completion of the study, a comparative analysis was conducted to examine the findings in conjunction with those of related research. The proposed detector demonstrated superior performance compared to previous research in terms of both accuracy and precision.

This paper presents an optimized Automatic License Plate Recognition (ALPR) system designed for resource-constrained devices, leveraging YOLOv8 for real-time object detection and Optical Character Recognition (OCR) to extract license plate information under challenging conditions such as low-light, motion blur, and occlusions. Unlike traditional ALPR systems that rely on high computational resources, our approach balances detection accuracy, processing speed, and efficiency. The system is evaluated on three benchmark datasets: the Chinese City Parking Dataset (CCPD) with 250,000 images under diverse conditions, the UFPR-ALPR Dataset (Universidade Federal do Paraná, Brazil) containing 4,500 real-world traffic images, and the RodoSol-ALPR Dataset with 20,000 highway surveillance images for high-speed license plate recognition. Among various YOLOv8 variants tested, YOLOv8-s achieved the best performance, with a mean Average Precision (mAP) of 99.3% while sustaining over 30 Frames Per Second (FPS), making it suitable for real-time ALPR applications. Furthermore, image sharpening and contour segmentation techniques improved OCR recognition accuracy by 5.1% under low-light conditions, improving robustness. Comparative analysis against state-of-the-art OCR-based ALPR methods (EasyOCR, FastOCR, and CR-NET) demonstrated that our approach surpasses existing models in both recognition rate and computational efficiency. These findings establish YOLOv8 as a highly effective and deployable solution for intelligent transportation, surveillance, and law enforcement applications requiring real-time license plate recognition with minimal computational overhead.

Penelitian ini bertujuan untuk mengklasifikasikan review pada aplikasi Tripadvisor yang terdapat pada Google Playstore berbasis Word Cloud dan Visual Network Explorer dengan algoritma Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), dan Naïve Bayes. Tahapan penelitian dimulai dengan pengumpulan data sebanyak 1.000 data yang diperoleh dengan menggunakan teknik Web Scraping, kemudian tahap Preprocessing data dan dilanjutkan dengan model klasifikasi menggunakan SVM, K-NN, dan Naïve Bayes. Berikutnya evaluasi model dengan hasil SVM memperoleh akurasi tebaik 89,8%. Tahap terakhir analisis visual menggunakan Visual Word Cloud dan Visual Network Explorer untuk mendapatkan informasi keputusan pengguna dalam memberikan Review positif dan negatif. Keluhan yang sering muncul seperti aplikasi jelek, aplikasi lumayan, hapus aplikasi, aplikasi error, aplikasinya tolong diperbaiki, aplikasi membuat hp jadi lemot karna sering update. Untuk mengatasi masalah tersebut diharapkan pihak aplikasi Tripadvisor agar lebih meningkatkan kinerja aplikasi dengan melakukan update dari segi penggunaan aplikasi agar mengatasi masalah yang dialami para pengguna.