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LPPM Universitas PGRI Yogyakarta Jl. PGRI I Sonosewu No. 117 Daerah Istimewa Yogyakarta 55182 Indonesia
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Applied Science and Technology Research Journal
Published by Universitas PGRI Yogyakarta
ISSN : ""     EISSN : 29636698     DOI : https://doi.org/10.31316/astro.v2i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Applied Science and Technology Research Journal specifically focuses on problems in the development of Research in science and technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 86 Documents
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Analysis Experience New Users of Flo App Based on Group Age with the User Experience Questionnaire (UEQ) Maulana Ridwan, Muhamad Fikry; Purwenti, Devita Ayu; Amsori, Trenggar S D C; Dwijayanti, Irmma
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8013

Abstract

The advancement of digital technology has significantly driven innovation in health applications, offering users practical tools to monitor their physical and emotional well-being. Among these, Flo: Period & Pregnancy Tracker stands out as a popular application designed to help women track their reproductive cycles, ovulation, and associated hormonal symptoms. This study aims to evaluate the user experience of new users of the Flo application across two age groups: 12–25 years and 26–45 years, to understand their perceptions of comfort and ease of use, employing a quantitative approach with the User Experience Questionnaire (UEQ). Analysis results indicate that both age groups generally provided positive assessments of the application. The Stimulation and Efficiency aspects received the highest scores, while Novelty was the lowest-scoring aspect. Further analysis revealed that the 12– 25 year age group tended to prioritize hedonic qualities (such as Stimulation and Attractiveness), whereas the 26– 45 year age group valued pragmatic qualities (such as Efficiency and Perspicuity) more in their initial app usage experience. These findings underscore the importance of UI/UX design that adapts to the differing needs and expectations of users across age segments for overall experience improvement. It is important to note that the imbalance in the number of respondents between age groups is a limitation of this study, which may affect the validity of peer-to-peer comparisons and the generalizability of results due to constraints in time and primary respondent data availability.
A Comparative Study Of HC-SR04 and HY-SRF05 Ultrasonic Sensors For Automated Height Measurement Based On IoT Kusuma, Mohan Henry; Banu Santoso
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8247

Abstract

The inefficiency and potential for operator error in manual height measurements limit data reliability in health and fitness monitoring. To address this, we developed an automated IoT-based system to compare the performance of HC-SR04 and HY-SRF05 ultrasonic sensors. The system architecture is built on a NodeMCU ESP8266 microcontroller, which sends measurement data to a cloud-based Firebase platform for real-time storage and historical analysis, all visualized on a dynamic ReactJS dashboard. The evaluation involved 30 human subjects with heights ranging from 100 to 200 cm. The analysis revealed a mean absolute error of 0.20 cm (0.131%) for HY-SRF05 and 0.233 cm (0.16%) for HC-SR04. Crucially, statistical testing found no significant difference in accuracy between the two sensors (T-test, p > 0.05). The study concludes that both low-cost sensors are highly capable and statistically equivalent for this application. The complete IoT system demonstrates a robust solution for deploying affordable, scalable, and accurate automated height measurement tools, offering a significant improvement over traditional methods.
Implementation of Association Rule With Algorithm Apriori On Loan Data Library and Archives Service Book Regency Sukoharjo Sari, Septiana Cahaya; Arif Himawan; Murdiyanto, Aris Wahyu
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8411

Abstract

The library has an important role in improving literacy, education, and facilitating access to information for the community. The Department of Libraries and Archives of Sukoharjo Regency has a high number of collections and visitors every year. An analysis of book borrowing transaction data is necessary to obtain information that can enhance the quality of services in the Sukoharjo Regency Library. This research aims to process book borrowing data at the Sukoharjo District Library and Archives Office by applying the Knowledge Discovery in Databases method. In addition, this also seeks to implement the Apriori algorithm to discover association rules that illustrate the relationships between books that are often borrowed together by library members, as well as to provide recommendations for book management to the library staff. The Knowledge Discovery in Databases method is used because it is a systematic approach that focuses on collecting hidden knowledge from large and complex data. This method consists of five main stages, namely selection, preprocessing, transformation, data mining, and evaluation. This research succeeded in identifying patterns of book borrowing at the Sukoharjo Regency Library and Archives Service based on 1,052 lending transaction data, with a minimum support of 0.005 and a confidence of 0.2 obtained from 64 association rules.
Comparative Analysis Of Artificial Intelligence Models For User Behavior Prediction In Big Data-Driven Information Systems Faqihuddin Al Anshori; Muhammad Fairuzabadi; Mohd Nawi, Mohd Nasrun
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8428

Abstract

In the era of digital transformation, Artificial Intelligence (AI) plays a pivotal role in enabling intelligent, data-driven information systems. This study presents a comprehensive comparative analysis of AI models: Decision Tree (DT) and Artificial Neural Network (ANN), for user behavior prediction within simulated big data environments, specifically in the e-commerce domain. Using 1,000 synthetic sessions that mimic real-world user activities, the study evaluates model performance using classification metrics such as accuracy, precision, recall, and F1-score. ANN outperforms DT across all metrics, achieving 87.2% accuracy and demonstrating superior learning efficiency and generalization. To complement the evaluation, a Long Short-Term Memory (LSTM) model is employed for time-series prediction, yielding a low MAPE of 1.12%, confirming its effectiveness in capturing sequential patterns. The findings offer valuable insights into AI model selection for adaptive and predictive information systems, with implications for developers and researchers seeking to enhance system responsiveness and personalization.
Facial Expression Detection In Video-Recorded Images Using a Mobilenet-Based Transfer Learning Approach Sulthon Adam Maulana
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8575

Abstract

Emotions play an important role in human communication, and facial expressions are one of the main indicators for recognizing emotional states. Most studies in Facial Expression Recognition (FER) still focus on static images or real-time webcam tracking, while evaluation approaches based on recorded video remain less explored. This study aims to design a simple but functional pipeline to evaluate the performance of MobileNetV2 with transfer learning on verbal interaction video data. The Karolinska Directed Emotional Faces (KDEF) dataset was used for training with seven basic emotion classes, while the test data came from video recordings processed frame-by-frame. The pipeline includes frame extraction, face detection using Haar Cascade, image preprocessing, and classification with the fine-tuned MobileNetV2 model. Evaluation metrics such as accuracy, precision, recall, and F1-score were applied. The results show that the model reached 87% validation accuracy and was able to identify dominant emotions in video, although predictions tended to be biased toward the neutral class in subtle expressions such as anger and disgust. On the other hand, clearer expressions such as happy were detected more reliably. In conclusion, the proposed pipeline successfully bridges static-image models with video data, offering a practical and efficient evaluation approach that can support Human-Computer Interaction (HCI) applications on resource-limited devices.
FaceGuardVMAPA: Developing an Advanced IoT-Based Facial Recognition System Using Convolutional Neural Networks for Security and Monitoring at Victorino Mapa High School Angel Danielle F. Cruz; Richelle O. Mendoza; Kurt Lorenz B. Verzosa
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The study, titled “FACEGUARDVMAPA: Developing an Advanced IoT-Based Facial Recognition System Using Convolutional Neural Networks for Security and Monitoring at Victorino Mapa High School,” aims to improve security measures and automate student attendance tracking at Victorino Mapa High School. The system leverages Convolutional Neural Networks (CNNs) for facial recognition to facilitate automatic identification and attendance management. To assess its performance, a Likert scale survey based on the ISO 25010 quality model was conducted, focusing on functional suitability, performance efficiency, usability, and security. Feedback from students, parents, and teachers reflected positive reactions, with average satisfaction ratings of 4.41, 4.43, and 4.35, respectively. These results indicate high satisfaction with the system’s features and functionality. Additionally, the inclusion of an SMS notification system, which sends real-time attendance updates to parents, strengthens communication between the school and families. The findings highlight that integrating facial recognition technology and optimized classroom scheduling improves entrance security, enhances attendance monitoring, and supports more efficient resource management. For future improvements, the study suggests the development of more user-friendly interfaces, increased accuracy of the facial recognition algorithm, and the implementation of multi-factor authentication to further enhance security.

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