Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences PENA SAINS Journal of Industrial Engineering Management Jurnal Pengabdian Pada Masyarakat METHABDI
Arminas, Arminas
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
MACHINE LEARNING MODELS FOR PREDICTING STRESS VALUE IN THE TENSILE STRENGTH OF BIOFILMS FROM STARCH AND HAIR WASTE Utami, Hermin Hardyanti; Fitrah, Muhammad Aqdar; Yusriadi, Yusriadi; Ardiansah, Ardiansah; Arminas, Arminas; Lestari, Mega Fia; Towolioe, Sherly
JURNAL PENA SAINS Vol 11, No 2 (2024): Jurnal Pena Sains
Publisher : Program Studi Pendidikan IPA, Fakultas Ilmu Pendidikan, Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jps.v11i2.26227

Abstract

Biofilms, structured communities of microorganisms, have emerged as a subject of significant interest across various industries due to their unique biodegradable and sustainable characteristics. Hair waste is an incredibly rich source of keratin, and this abundance makes it a promising candidate as a fundamental building block for the development of biodegradable plastics. This study focuses on sustainable biofilms derived from biodegradable materials, specifically a unique combination of starch and hair waste. Machine Learning models, implemented in RapidMiner, were utilized to predict the tensile strength of these biofilms, with the goal of enhancing quality control in their production. Neural Networks and Deep Learning methods were employed to compare their predictive capabilities, assessing both their strengths and limitations. Through rigorous data collection, feature identification, and detailed data analysis, critical factors influencing the quality of the biofilms were identified. The results revealed the remarkable predictive accuracy of the Neural Net model, particularly for Ratio 40, while the performance of the Deep Learning model varied across different ratios. The lower RMSE of the Neural Net model indicated a more precise alignment between the predicted and actual values, distinguishing it as the superior model. This research contributes to the advancement of sustainable biofilm development, offering eco-friendly solutions through the use of unconventional materials. Both models offer valuable predictive capabilities, and the choice between them may depend on the specific requirements and contexts of the application. In conclusion, the performance of the Neural Net and Deep Learning models in predicting stress in tensile strength varies across different ratios.
THE MAINTENANCE INTERVAL OF PREBREAKER CRITICAL COMPONENTS USING RELIABILITY-CENTERED MAINTENANCE IN PT XYZ Haming, Puadi; Arminas, Arminas; Fajri, Nofias; Efendi, Dodi
Journal of Industrial Engineering Management Vol 8, No 1 (2023): Journal of Industrial Engineering and Management Vol 8 No 1
Publisher : Center for Study and Journal Management FTI UMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33536/jiem.v8i1.1268

Abstract

PT XYZ is a crumb rubber company that produces SIR 10 and rubber smoke sheets (RSS). As the biggest crumb rubber-produced company in Indonesia PT XYZ makes the production process sustainable. The often problem in this company is machine breakdown that occurs downtime value to be high. The highest downtime value from October 2020 until March 2021 was the Prebreaker Machine whose total downtime value was 1875 minutes. The Reliability Centered Maintenance (RCM) method is used for decreasing downtime value. The RCM is integrated with Failure Mode Effect Analysis (FMEA) for analyzing The Highest Risk Priority Number (RPN). The research results that the optimal maintenance time of the Prebreaker Machine is 51 hours which means the Prebreaker Machine is maintained after operation for 51 hours.
Edukasi Keamanan AWS: Mengamankan Akun, Data, dan Kepatuhan Cloud dengan AWS IAM Hamdani, Ibnu Mansyur; Syahadi, Adi; Julyaningsih, A. Hermina; Arminas, Arminas
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 1 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

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

Abstract

Cloud computing service security is a crucial aspect in managing data and information technology infrastructure. Amazon Web Services (AWS) provides security features such as AWS Identity and Access Management (IAM) to control access to resources. However, the lack of user understanding in implementing this system increases the risk of data leaks and cyber attacks. This community service activity aims to improve digital security literacy, especially in the use of AWS IAM. The training was held online on February 2, 2025 via Zoom and was attended by 24 participants from a total of 30 registrants. The methods used include theory and direct practice using an AWS Academy account. Evaluation was carried out through pre-tests and post-tests as well as a Google Form survey. The results showed an increase in understanding with an average pre-test score of 60 increasing to 85 in the post-test. In addition, 92% of participants stated that the material presented was relevant, 88% felt the training was practically useful, and 90% were more confident in managing their AWS accounts. These results show that education about AWS security is essential to reduce the risk of misconfiguration and improve the security of cloud-based systems.
Development of a hydraulic jack system bending tool for improved manufacturing efficiency Suyuti, Muhammad Arsyad; Nur, Rusdi; Muttaqin, Ahmad Nurul; Arminas, Arminas; Sudirman, Zainal
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1072-1082

Abstract

This article presents the design, fabrication, and testing of a hydraulic sheet metal bending tool. The main objective was to create a tool capable of bending sheets of various thicknesses, ranging from 2 to 4 mm, with high precision and minimal operator effort. The design incorporates a hydraulic ram for easy operation, allowing multiple plates to be bent in a short period of time. Key calculations, including bending force, spring load, and hydraulic force, are performed to ensure the efficiency and safety of the tool. Experimental results show that the tool is able to achieve the desired bending angles, with minimal spring return, and can handle up to three 10 cm wide sheets in approximately 10 minutes. The performance of the tool has been proven by tests, and the results confirm that it can meet the requirements of industrial sheet metal bending. Based on these results, the tool demonstrates its effectiveness in small and medium-scale operations, providing a cost-effective solution for sheet metal production.
Co-Authors A. Hermina Julyaningsih Ahmad Nurul Muttaqin Aqdar Fitrah, Muhammad Ardiansah, Ardiansah Efendi, Dodi Fajri, Nofias Hamdani, Ibnu Mansyur Haming, Puadi Mega Fia Lestari Muhammad Arsyad Suyuti Rusdi Nur Sudirman, Zainal Syahadi, Adi Towolioe, Sherly Utami, Hermin Hardyanti Yusriadi Yusriadi