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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) IJIES (International Journal of Innovation in Enterprise System)
Murman Dwi Prasetio
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Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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Design and Development Classifications A Defect in Clay Tiles Using A Method of Support Factor Machine (SVM) Rais Yufli Xavierullah; Murman Dwi Prasetio; Denny Sukma Eka Atmaja
Jurnal Rekayasa Sistem & Industri Vol 7 No 02 (2020): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v7i2.420

Abstract

Quality control is a system that can assist a company in maintaining and maintaining product quality sothat product defects do not occur. PT. XYZ is a company in the clay tile industry. Every month, PT. XYZhas products due to defects with an average of 2225 precarious. One of the problems that occurred at PT.XYZ is an inspection process that only uses sight. The use of sight can carry the risk of increased operatingcosts due to faulty examinations, failure to get business, and rework. With the development of technology,it can overcome this problem by finding artificial detectors using measurement methods, imagepreprocessing, and algorithms to detect defect. In this study using the Support Vector Machine (SVM)method in classifying defects. Taking pictures directly in this study using raspberry pi and making thealgorithm system using pyhton software. This study uses a linear kernel in the SVM algorithm. The resultsin this study concluded that the highest accuracy rate was 88.6% using a linear kernel.
Object/Product Identification for Stock Taking Activities using Object Recognition Concept Muhammad Nashir Ardiansyah; Prafajar Sukssesanno Muttaqin; Murman Dwi Prasetio; Nia Novitasari
Jurnal Rekayasa Sistem & Industri Vol 8 No 01 (2021): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v8i1.455

Abstract

Stock taking activity is as a routine product inspection activity to check the inventory accuracy and helpreduce the risks of stealing, damage, and obsolete inventories. This activity can be categorized as timeconsuming and expensive activity. In addition, this activity needs a lot of concentration and prone tohuman errors and mistakes. This study aims to replace human manual inspection in terms of object typeand quantity with objects identification to reduce errors, time, and costs. Digital image processing in theform of Object Recognition is used in this study to determine the type of object and the number of objects.The results showed that the detection rate of a single product reached 90% which was influenced by theangle of an image and the detection rate of object quantity reaches 81% in average in real environmentwith a certain condition. It is expected that costs of inventory inspection and warehousing activities can bereduced, as well as the improvement in terms of efficiency and effectiveness can be achieved.
Identical Parallel Machine Scheduling to Minimize Makespan Using Suggested Algorithm Method at XYZ Company Naura Maisazahra; Murni Dwi Astuti; Murman Dwi Prasetio
International Journal of Innovation in Enterprise System Vol. 6 No. 1 (2022): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v6i01.146

Abstract

XYZ company produce the various shape of motor spare parts product. The company has threeidentical parallel spot welding machines that use a random method of production scheduling, basedon machine capacity without any sequence of jobs, and only use daily production targets given tooperators. Based on the data, the actual scheduling of the machines has a very large completion timedifference between each machine, or the machine loading is uneven. As a result, the makespanbecomes longer with a value of 440000 seconds (26 days). This research aims to minimize the existingmakespan by giving proposed scheduling, using the suggested algorithm method, which has a smallnumber of iterations and has an optimal result. The method begins with the longest processing timesequence rule which is used as the upper bound for the first iteration, then continued to calculate thelower bound and machine workload. The calculation stops at the 15th iteration because the completiontime value exceeds the lower and upper bound so that the optimal scheduling taken is scheduled inthe 14th iteration with a makespan value of 914412 seconds (16 days). The proposed scheduling canminimize the makespan from the actual schedule by 38%.
Analyzing Digital Twin Adoption in Aluminum Plants Using Technology Readiness Index 2.0 Yusuf Nugroho Doyo Yekti; Wiyono Sutari; Nur Ichsan Utama; Murman Dwi Prasetio
International Journal of Innovation in Enterprise System Vol. 8 No. 2 (2024): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v8i02.269

Abstract

In the era of industrial digitalization, Digital Twin technology offers a potential solution to enhanceefficiency and innovation. This study investigates the readiness of workers in an Indonesian aluminumcasting plant to embrace new technology, considering their perceptions of optimism, innovation,discomfort, and insecurity. A semi-quantitative approach was employed, utilizing the TechnologyReadiness Index (TRI) 2.0. The research involved four representatives selected by the industry'sleadership. Questions were specifically designed to assess the readiness for Digital Twin technologyin the aluminum casting plant. The data were analyzed using content analysis to determine the overallreadiness for new technology and the characteristics of key personnel for Digital Twin technology.The overall Technology Readiness Index (TRI) 2.0 score indicated a grand mean of 3.75 (SD: 0.74),suggesting that the employees generally tend to be skeptical but slightly inclined towards a positiveattitude in accepting Digital Twin technology for implementation in the aluminum casting plant.
Co-Authors Denny Sukma Eka Atmaja Muhammad Nashir Ardiansyah Murni Dwi Astuti Naura Maisazahra Nia Novitasari Nur Ichsan Utama Prafajar Sukssesanno Muttaqin Rais Yufli Xavierullah Wiyono Sutari Yusuf Nugroho Doyo Yekti