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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rekayasa Mesin Jurnal Pengabdian UNDIKMA J-ENSITEC (Journal of Engineering and Sustainable Technology)
Ichsanudin, Muhammad
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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COMPARATIVE ANALYSIS OF WATER-BASED INHIBITORS CONTAINING CDEA AND TEA AGAINST API 5L GRADE B Prabowo, Imam; Ichsanudin, Muhammad; Fuadi, Muhammad; Mussoddaq, Mochamad; Paundra, Fajar
J-ENSITEC Vol. 11 No. 01 (2024): December 2024
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jensitec.v11i01.11931

Abstract

Corrosion of API 5L Grade B steel is a common challenge in industries where the material is exposed to corrosive environments, such as saline solutions. To combat this issue, corrosion inhibitors are often used to protect the steel. This study explores the effectiveness of two inhibitors—Triethanolamine (TEA) and Cocamide DEA (CDEA)—in reducing corrosion on API 5L Grade B steel. By calculating the corrosion rate and inhibitor efficiency, we evaluated the protective properties of each substance. The results showed that TEA provided better corrosion protection, with a corrosion rate of 0.00045 mpy, compared to CDEA’s rate of 0.0009 mpy. Additionally, TEA demonstrated a higher inhibitor efficiency of 70.97%, while CDEA showed only 41.94%. These findings suggest that TEA is a more effective choice for preventing corrosion in API 5L Grade B steel, offering a viable solution to enhance the material’s durability in harsh environments.
FITNESS FOR SERVICE OF GAS COMPRESSOR PIPING USING FINITE ELEMENT METHOD Ichsanudin, Muhammad; Prabowo, Imam; Istiqomah, Intan
Jurnal Rekayasa Mesin Vol. 16 No. 2 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i2.2093

Abstract

This research presents a Level 3 Fitness for Service (FFS) analysis, focusing on stress evaluation in the suction side of gas compressor piping in a gas-fired power plant in Indonesia, using the Finite Element Method (FEM). To expedite numerical calculations and reduce computational time, the analysis employs line elements within FEM, enabling efficient and timely assessment. The modeled operational conditions include sustained, expansion, and occasional seismic loads, providing a comprehensive view of pipeline behavior under various stress scenarios. The study results indicate that all pipeline segments analyzed show no potential for failure, with code ratios below 1. The sustained load has a code ratio of 0.71 with a maximum stress of 97.64 MPa, an expansion load of 0.66 with a maximum stress of 136.90 MPa, and an occasional seismic load, the highest at 0.70 with a maximum stress of 129.27 MPa. Among all loading scenarios, the compressor suction segment exhibits the highest code ratio, indicating the need for heightened attention in maintenance management. It is recommended that condition monitoring and inspection efforts be focused on this segment to prevent potential failure. The findings conclude that all code ratios from each loading scenario are acceptable, confirming that the pipeline segments meet the required safety standards. This reinforces the reliability of the pipeline system and validates the effectiveness of Level 3 FFS analysis in guiding maintenance strategies and risk management for critical gas pipeline systems.
Pelatihan Pembuatan Eco-Friendly Light-Brick dari Sekam Padi sebagai Bata Ringan Tahan Gempa di Dusun Mriyan Utara Kabupaten Bantul Setyani, Atik; Prabowo, Imam; Arif, Nina Fapari; Ichsanudin, Muhammad; Pranoto, Aditya Wahyu; Tantrianita, Rikma; Pradipa, Raditya Khayru
Jurnal Pengabdian UNDIKMA Vol. 5 No. 4 (2024): November
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v5i4.13264

Abstract

This community service activity aims to enhance the skills of the Karang Taruna in Dusun Mriyan Utara, Bantul Regency, in making eco-friendly light bricks from rice husk as earthquake-resistant lightweight bricks. The method used in this community service involves participatory training, where youth groups actively make light bricks from natural waste. The evaluation instruments for this activity include direct observation and post-tests, which are analyzed descriptively to measure the participants' improvement in skills and knowledge. The results of this community service indicate that the people of Dusun Mriyan Utara are very enthusiastic and have successfully improved their skills in making light bricks, which can be developed as a flagship product of the village and a preventive measure in earthquake disaster mitigation.
Solving sparsity and scalability problems for book recommendations on e-commerce Ichsanudin, Muhammad; Handari, Bevina Desjwiandra; Wijanarko, Bambang Dwi; Hertono, Gatot Fatwanto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4865-4877

Abstract

This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Co-Authors Arif, Nina Fapari Bevina D Handari Hertono, Gatot Fatwanto Imam Prabowo, Imam Intan Istiqomah, Intan Muhammad Fuadi Mussoddaq, Mochamad Paundra, Fajar Pradipa, Raditya Khayru Pranoto, Aditya Wahyu Setyani, Atik Tantrianita, Rikma Wijanarko, Bambang Dwi