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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 92 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.
IoT-Based Smart Farming System Design for Greenhouse Monitoring in Urban Areas Marti Widya Sari; Erika Amalia; Prahenusa Wahyu Ciptadi; R. Hafid Hardyanto; Banu Santoso
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 5 No. 1 (2026): 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.v4i2.9323

Abstract

The rapid development of urban areas has led to a significant reduction in agricultural land, creating the need for innovative solutions to meet food demands, especially for fresh vegetables. One promising alternative is urban farming supported by the Internet of Things (IoT). This research aims to design and develop an IoT-based smart farming system for monitoring vegetable crops in urban areas with limited land availability. The system uses a NodeMCU ESP32 as the main controller, an SHT20 sensor to measure temperature and humidity, and a pH sensor to monitor the acidity of the nutrient solution. Sensor data are displayed in real time through an LCD and an Android-based application, and are also used to control an automatic fan to maintain optimal environmental conditions. The research method applied is an experimental approach comprising a literature review, system design, hardware and software implementation, and system testing. Based on the research results, the IoT-based smart farming system was successfully developed and can monitor plant environmental conditions and nutrient solutions in real time via an Android application, with data stored in a database and displayed appropriately. The test results indicate that the system helps users manage vegetable cultivation more efficiently. This system is expected to provide an effective, efficient, and sustainable smart farming solution for urban areas with limited land availability.
Applying an IoT Technology in Hydroponic Smart Farming Systems on a Limited Area Dwi Endah Wahyuni; Marti Widya Sari; Prahenusa Wahyu Ciptadi; R. Hafid Hardyanto; Banu Santoso
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 5 No. 1 (2026): 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.v4i2.9324

Abstract

Population growth continues to drive food demand, while agricultural land becomes increasingly scarce due to urbanization and land conversion. This condition demands agricultural innovations that can increase productivity sustainably in limited land. One emerging solution is the hydroponic system, but managing key parameters such as water temperature, water level, and nutrient content (TDS) is often still done manually, which risks reducing crop quality. This study aims to design and implement an Internet of Things (IoT)-based hydroponic system capable of real-time and remote monitoring. The research method used is an experimental method, including literature review, needs analysis, system design, hardware and software installation, and system testing. The system was developed using an ESP32 microcontroller with a DS18B20 temperature sensor, a JSN SR04T ultrasonic sensor, and a TDS sensor, along with an Android application as the monitoring interface. The design results show that the system can monitor hydroponic conditions in real time, powered by solar panels. This system is expected to improve the efficiency of nutrient management and support the implementation of smart agriculture in limited land.
Developing an IoT-Based Smart Mini Hydroponic Greenhouse Prototype Arya Nanda Eka Putra; Marti Widya Sari; Prahenusa Wahyu Ciptadi; R. Hafid Hardyanto; Banu Santoso
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 5 No. 1 (2026): 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.v4i2.9325

Abstract

The development of urban agriculture faces challenges related to limited land availability, water scarcity, and inefficient plant management. These conditions encourage the adoption of smart agricultural technologies that operate automatically and efficiently. This study aims to develop an IoT-based smart mini greenhouse prototype suitable for urban environments with limited space. The research method used is an experimental approach focusing on system design and implementation. The system employs an ESP32 microcontroller as the main control unit and is equipped with pH, Electrical Conductivity, and water temperature sensors to monitor the quality of hydroponic nutrient solutions. Sensor data are automatically processed to control circulation pumps and nutrient correction pumps via relay modules, and the data are transmitted to a web-based monitoring system in real time. The test results show that the system can monitor water quality parameters stably and automatically perform irrigation and nutrient correction according to predefined threshold values. The implementation of this system improves the efficiency of water and nutrient management and reduces reliance on manual monitoring. These results indicate that the IoT-based smart mini greenhouse has the potential to serve as a practical and sustainable solution for supporting urban farming in areas with limited land.
A Correlational Analysis of Moodle-Based Learning Analytics: URL Access and Assignment Activity as Performance Indicator Isnaeni Nurrohmah; Kartikadyota Kusumaningtyas; Daniel Wardhana
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 5 No. 1 (2026): 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.v4i2.9420

Abstract

The widespread adoption of Learning Management Systems (LMS) in higher education has shifted student engagement indicators from physical observation to digital footprints. While comprehensive, LMS activity logs are often underutilized due to their complexity. This study leverages learning analytics to investigate whether specific types of digital engagement—categorized as passive or active interaction—correlate with academic achievement in an online learning environment. A correlational research design was employed, analyzing secondary data from a Moodle-based LMS for a 2025 course at Sekolah Tinggi Multi Media. Student activity was extracted from logs and aggregated into two independent variables: Total URL Count (passive interaction, representing material access) and Total Assignment Count (active interaction, representing all assignment-related activities). The dependent variable was the students' final course grade. Data analysis involved Pearson and Spearman correlation tests to measure linear and monotonic relationships. Analysis of 15,670 log events from 44 students revealed distinct correlations. Passive interaction (URL access) showed a moderate positive correlation with final grades (Pearson r = 0.41; Spearman ρ = 0.59). In contrast, active interaction (assignment activity) demonstrated a very strong positive correlation (Pearson r = 0.78; Spearman ρ = 0.80). The findings confirm that LMS activity logs are valuable predictors of academic success. Critically, active interaction is a significantly stronger and more reliable indicator of academic performance than passive material consumption. This suggests that for early intervention and student success prediction, educators and learning analytics systems should prioritize monitoring task-oriented engagement metrics over mere content access.

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