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Setyo Eko Atmojo
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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 11 Documents
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Search results for , issue "Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal" : 11 Documents clear
Comparison of 3D Printing Technologies for Polymer-HA Bone Scaffolds: A Systematic Review Toward Hybrid Fabrication Strategies Kumarajati, Dhananjaya Yama Hudha; Herianto; Herliansyah, Muhammad Kusumawan; Kusmono; Tontowi, Alva Edy
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.v4i2.8230

Abstract

Bone scaffold fabrication using 3D printing faces a fundamental dilemma: the trade-off between mechanical strength and biological functionality. To address this challenge, a systematic literature review (SLR) of 28 primary research articles was conducted to compare various hydroxyapatite-based scaffold fabrication technologies. The analysis confirms a clear trade-off: Fused Filament Fabrication (FFF) excels in mechanical strength, Digital Light Processing (DLP) in architectural precision (<100 µm), and Direct Ink Writing (DIW) in flexibility for bio-functionality, proving no single method is ideal. The main conclusion is that hybrid fabrication strategies—intelligently integrating the strengths of multiple technologies—offer the most promising approach to creating functional scaffolds with an optimal balance of strength and bioactivity for future clinical applications.
Technology Trends, Innovations, and Future Research Directions in 3D Printing (Additive Manufacturing): A Systematic Literature Review Santoso, Banu; Dhananjaya Yama Hudha Kumarajati; Herianto; Alva Edy Tontowi; Muhammad Kusumawan Herliansyah
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.v4i2.8232

Abstract

3D printing or Additive Manufacturing (AM) technology has experienced rapid growth in the past five years, driven by the integration of new technologies such as artificial intelligence (AI), bio- and nano-composite materials, and blockchain-based security systems. This study aims to analyze technology trends, key innovations, and predict future research directions in AM using a Systematic Literature Review (SLR) approach to 80 Scopus/WoS indexed articles. The results show that AI plays a central role in improving production efficiency and accuracy, while material innovations expand AM applications to the medical and aerospace sectors. In addition, the application of 4D printing and blockchain is beginning to form a new paradigm in intelligent and decentralized manufacturing. The 2025–2030 research roadmap compiled from these findings shows a strategic focus on adaptive AI, multifunctional bioinks, modular manufacturing systems, and full integration between AM, blockchain, and smart materials. This study not only identifies research trends and gaps but also offers strategic contributions to the development of future AM technologies in a more adaptive, sustainable, and secure manner.
A Comprehensive Review of AI, Machine Learning, Deep Learning, and GANs Integration in Additive Manufacturing: Trends, Applications, and Challenges Santoso, Banu; Herianto; Wangi Pandan Sari; Alva Edy Tontowi
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.v4i2.8233

Abstract

The integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative Adversarial Networks (GANs) into Additive Manufacturing (AM) has opened new horizons for intelligent, efficient, and adaptive production processes. This paper provides a comprehensive review of current trends, diverse applications, and emerging challenges in the convergence of these technologies within AM systems. We explore how AI-driven techniques contribute to real-time monitoring, defect detection, process optimization, and design generation, enhancing the overall quality, precision, and scalability of 3D printing. ML and DL approaches enable predictive modeling and adaptive control, while GANs offer promising capabilities in generative design and synthetic data augmentation. The review highlights key research contributions, technological advancements, and industrial implementations, mapping the landscape of intelligent AM. Moreover, it discusses the limitations of data availability, model interpretability, computational requirements, and integration complexities. Finally, the study identifies future directions for research, including hybrid AI models, physics-informed learning, and sustainable AM development. By synthesizing multidisciplinary insights, this paper aims to guide researchers and practitioners toward more intelligent, automated, and sustainable additive manufacturing frameworks through the strategic adoption of AI and its subfields. Keywords: Additive Manufacturing, Machine Learning, Artificial Intelligence, 3D Printing, Deep Learning
Analysis Comparison of Depression Levels Based on Gender and Academic Factors of Students Verdiana, Miranti; Nugroho, Eko Dwi; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Algifari, Muhammad Habib
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.v4i2.7975

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to evaluate the association between gender and depression status, point-biserial correlation to examine the relationship between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students' mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments.
Analysis of Factors Influencing User Retention on the Community Feature of The Asian Parent Application Ningrum, Almayanti Susilia; Pamikatsih, Mae Niken; Fernanda, Rois Ali; 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.v4i2.7980

Abstract

The community feature in parenting apps like The Asian Parent plays a crucial role in building digital social interactions among users, especially parents. Active community engagement is a crucial factor in maintaining user retention. This study aims to analyze the factors influencing user experience retention in the community feature within The Asian Parent app using the User Experience Questionnaire (UEQ) approach. The research method involved distributing questionnaires to active app users, covering six user experience dimensions: attractiveness, clarity, efficiency, accuracy, stimulation, and novelty. The collected data were analyzed descriptively to determine the tendency of user perceptions towards each dimension. The analysis results show that hedonic dimensions, especially stimulation and novelty, have the greatest influence on user retention, followed by pragmatic aspects such as efficiency and clarity. These findings suggest that emotional experience and cognitive satisfaction play a significant role in encouraging users to remain active in the app community. This research is expected to provide insights for app developers to design more engaging, adaptive, and sustainable community features to increase user loyalty.
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.

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