cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Kormil Saputra
Contact Email
tintaemasjournal@gmail.com
Phone
-
Journal Mail Official
tintaemasjournal@gmail.com
Editorial Address
Sultan Salahudin Street, Tj Karang, Kec. Sakarbela, Kota Mataram, Nusa Tenggara Barat, Indone
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
Frontier Advances in Applied Science and Engineering
Published by Tinta Emas Publisher
ISSN : -     EISSN : 30265339     DOI : https://doi.org/10.59535/faase
Core Subject : Health, Science, Agriculture, Engineering,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics
Mission and Exploration: Frontier Advances in Applied Science and Engineering (FAASE) is committed to being a compass for cutting-edge research, guiding scholars, researchers, and practitioners as they traverse the diverse and ever-evolving landscapes of applied science and engineering. Our mission is to provide a comprehensive platform that fosters the exchange of innovative ideas, transformative discoveries, and impactful insights across a multitude of interconnected disciplines. Focus Frontier Advances in Applied Science and Engineering (FAASE) is dedicated to advancing the frontiers of knowledge and innovation in the realm of applied science and engineering. Our focus is on providing a platform for researchers, scholars, and practitioners to explore, share, and contribute to the latest advancements and insights in a wide range of disciplines. Scopes Applied Science: Frontier Advances in Applied Science and Engineering (FAASE) is a multidisciplinary platform that welcomes original research, review articles, case studies, and scientific data in various fields of applied science. Our scope includes, but is not limited to: Mathematics, physics, chemistry, and statistics Geology and geography Horticulture, agriculture, and post-harvest technology Study of pests and plant diseases Agricultural practices and innovations Food technology, nutrition, and halal food science Post-harvest technology and preservation methods Plantation management and sustainable practices Agricultural economy and community development Nutrition, family resources, and societal well-being Advancements in agricultural extension services Forestry science and sustainable resource management Animal husbandry, nutrition, and feed technology Technology and processes for livestock product development Animal biotechnology and genetic advancements Fisheries management and aquaculture practices Biotechnology applications in fisheries Engineering & Technology: FAASE also provides a platform for researchers, scholars, and practitioners in the field of engineering and technology. We welcome contributions that encompass, but are not limited to: Civil engineering and urban spatial planning Environmental engineering and sustainability practices Urban and regional planning for resilient communities Architectural engineering and innovative designs Industrial engineering and process optimization Mechanical engineering and innovative machinery Chemical engineering and sustainable processes Material engineering and advanced materials research Nuclear engineering and safety advancements Electrical engineering and technological innovations Technical information management and knowledge dissemination Computer engineering, systems, and hardware advancements Information systems and digital infrastructure Software engineering, development, and applications Computer science and cutting-edge algorithms Instrumentation and precision measurement techniques Telecommunications and networking advancements Our scope encompasses contributions that address real-world challenges, drive innovation, and align with the Sustainable Development Goals (SDGs). FAASE is dedicated to promoting interdisciplinary collaboration and supporting research that has a meaningful impact on society and the environment.
Arjuna Subject : Fisika dan Astronomi - Fisika dan Astronomi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025)" : 5 Documents clear
Magnetic Susceptibility of Pumice in the Area Around Mount Singgalang, West Sumatra Nadifa Salsabila Rifiana; Hamdi Rifai; Ciira wa Maina; Eti-mbuk Akanbi; Sándor Nagy
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.384

Abstract

Mount Singgalang is a volcano that has erupted after 1600. Several types of volcanic rocks found around the Mount Singgalang area are Basalt, Andesite, Tuff Breccia, Lava Breccia, and Pumice tuff. Where, andesite and tuff pumice Mount Singgalang originated from the maninjau eruption. Pumice has several minerals, namely cristobalite, feldspar, quartz, obsidian, cristobalite, and tridymite. Pumice has one of the magnetic minerals, namely hematite (Fe2O3), but it is not known how much magnetic mineral content in it. This study aims to determine the abundance of magnetic minerals (magnetic susceptibility) in pumice around Mount Singgalang, West Sumatra. The method used is the rock magnetism method, where this method can investigate the magnetic properties of a material. The instrument used is the Bartington Magnetic Susceptibility Meter. The magnetic susceptibility value of Pumice (Pumice) in the area around Mount Singgalang, West Sumatra, where is the abundance of magnetic minerals in Pumice Rock TDK 20-02 > TDK 20-03 > TDK 20-10. The TDK 20-02 sample has a lot of superparamagnetic grains due to its low susceptibility value and high-Frequency Dependent Susceptibility, while the TDK 20-10 sample has little or almost no superparamagnetic grains due to the high susceptibility value and low-Frequency Dependent Susceptibility.
The Effect of Night Sky Quality in the Glanggang Tourism Village Area in Order to Diversify Astronomy Tourism A Yasinta; Morshed Hadwan; Sutrisno Sutrisno; Mamadou Simina Dramé; Dessalegn Ayehu Mekuriaw
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.445

Abstract

Indonesia's economic sector is experiencing rapid growth with the development of tourism. Eco-friendly tourism introduced with the resource of dark skies without light is called astronomy tourism. The brightness of the night sky is closely related to determining the location of astronomy tourism. Measuring the brightness of the night sky plays an important role for observing celestial objects. The research was conducted in Glanggang Village, Pakisaji District, Malang Regency. This research is useful to provide information on whether or not the place is suitable as an astronomy tourism location, besides adding to the attractiveness of tourism by enjoying the beauty of celestial objects. The method used in measuring sky brightness is the Sky Quality Meter type SQM-L. Observations were made for 8 days with a time duration of every 15 minutes. The data is analyzed using the correlation between time and sky brightness value using MPSAS (Magnitude per Square Arc Second) unit. Based on the observation, the highest sky brightness value is 21.4 MPSAS, indicating that the sky brightness value at night is quite high, so light pollution is quite low. According to the results of the observation data, it is concluded that Glanggang Village can be used as an astronomy tourism location.
Multi-Stage Computer Vision Framework with Ensemble Learning for Real-Time Glass Packaging Defect Detection in Industrial Applications Jonah Alfred Mekel; Rick Resa Wahani; Motulo, Firmansyah Reskal; Alfred Noufie Mekel; Tineke Saroinsong; Tammy Tinny V. Pangow; Jerry Heisye Purnama; Jedithjah Naapia Tamedi Papia
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.572

Abstract

Transparent glass packaging inspection presents significant challenges for automated quality control systems due to optical complexities including reflections, refractions, and low-contrast defect patterns. This research develops a comprehensive multi-stage computer vision framework integrating specialized algorithmic modules with ensemble machine learning for real-time defect detection in industrial glass packaging lines. The framework implements four specialized detection stages: (1) meniscus-corrected liquid level measurement using dual-camera validation and polynomial surface fitting, (2) seal integrity assessment through Circular Hough Transform combined with geometric, texture, and color feature extraction, (3) lid positioning evaluation via calibrated geometric centroid analysis with tolerance-based classification, and (4) multi-method contamination detection integrating color aberration analysis, histogram-based particle detection, and morphological operations. The system employs an ensemble classification architecture combining modified MobileNetV2 convolutional neural network with Random Forest classifier, optimized for edge computing deployment. Industrial validation at PT AQUWAR Bintang Semesta demonstrated 91.6% overall detection accuracy with 347 milliseconds average processing time per container across 2,847 test samples spanning multiple defect categories. The modular framework architecture enables independent optimization of detection stages while maintaining real-time processing capabilities, providing a robust foundation for transparent packaging quality control in high-volume manufacturing environments.
Experimental Investigation of Square Pin Fins Geometry Variations with Aligned and Staggered Arrangements Under Forced Convection Heat Transfer Nimesh Limbasiya; Sagar Mane Deshmukh
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.622

Abstract

This research was conducted to determine the heat transfer characteristics of pin fins with variations in square, diamond, and combination (diamond-square) geometries. The heat transfer that occurs in the pin fins is a type of forced convection heat transfer. The research experiment used a TD1005 free and forced convection equipment with inline and staggered pin fin arrangement. The dimensions of the pin fin used are a height of 73 mm and a side of 10 mm, and a base plate measuring 101 mm x 101 mm. The material used is type 6061 aluminum. The maximum convection coefficient values for all power variations are in variations of square geometry and staggered arrangement, as well as for the Nusselt number, which has the same correlation with the convection coefficient results. The performance of the use of fins indicated on the effectiveness value for all variations suggests that it is suitable for use on the device, however, the efficiency of the pins demonstrates that the combination (diamond-square) for aligned and staggered arrangement produces the best results.
Fake Signature Recognition Using Convolutional Neural Network with ResNet-18 Architecture Abdul Aziz Al Basyir; Kenan Almas; Big Daya Yudha Asmara
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.626

Abstract

Signatures are widely used as a personal identity marker and as a key element in validating official documents. Although each individual’s signature is generally distinctive, genuine signatures may vary in shape and size across different instances, while forged signatures can appear visually similar to authentic ones. These factors make manual verification subjective, time-consuming, and prone to error, particularly when handling large volumes of documents. This study developed an automatic signature recognition system using deep learning, specifically a Convolutional Neural Network (CNN) with a ResNet-18 architecture. The dataset consisted of 1,038 signature images, and model performance was evaluated across multiple training epochs. The best result was obtained using the Adam optimizer at epoch 25, achieving an accuracy of 59.5% on the evaluated data. The findings indicate that high visual similarity among signature images can substantially affect classification outcomes, suggesting that offline signature recognition remains challenging when formulated as a simple binary image classification task. Future work should consider improving the training protocol and evaluation scheme to better capture subtle discriminative features between genuine and forged signatures.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2025) Vol. 3 No. 1 (2025) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023)