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Journal of Engineering and Management in Industrial System
Published by Universitas Brawijaya
ISSN : 23383925     EISSN : 24776025     DOI : -
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Journal of Engineering and Management in Industrial System is a peer reviewed journal. The journal publishes original papers at the forefront of industrial and system engineering research, covering theoretical modeling, inventory, logistics, optimizations methods, artificial intelligence, bioscience industry and their applications, etc.
Arjuna Subject : -
Articles 369 Documents
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ASSESSING CONTRIBUTORY FACTORS IN POTENTIAL HAZARD NATURAL GAS PIPELINE FAILURE Hasnan, Ahmad; Darmawan, Zefry; Iskandar, Ade; Aswin, Aswin; Septian, Diar Azzis
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 11 No. 1 (2023)
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jemis.2023.011.01.3

Abstract

Increased energy demand in Indonesia is accompanied by increasing demand for natural gas, where in the next 50 years natural gas is predicted to be the number one energy source in Indonesia, pipeline is the cheapest way to distribute natural gas, in this way, length of pipeline infrastructure will increase year by year. it is still very much needed by both the household, industry, and power plants. The longer the pipeline, the risk of pipeline failure also increases, it is necessary to understand what factors have the most influence on pipeline failure, the method used is to create a factor matrix from a modified Muhlbauer, MICMAC is used to test the strength of the relationship between significant factors causing the potential hazard of pipeline failure. based on their influences and dependencies. The value of the dependency relationship between factors is determined from discussions with several pipeline experts in Indonesia, who work in related fields, the result is that there are three main factors that contribute major potential hazards, without being influenced by other factors, that is determining safety factors in the design process, depth pipeline and the existence of SOP in the pipeline system. One factor, namely depth, can be eliminated because there are government regulations requiring natural gas pipelines to be buried in the ground at a certain depth.
ANALYSIS THE EFFECTIVENESS OF CNC TURNING MACHINES TYPE XTRA 420 USING THE OVERALL EQUIPMENT METHOD EFFECTIVENESS (OEE) Arifin, Arifin; Tama, Ishardita Pambudi; Sumantri, Yeni
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 11 No. 1 (2023)
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jemis.2023.011.01.5

Abstract

The development of the manufacturing industry is increasing every year, of course this makes competition in the manufacturing industry increasingly stringent. This research was conducted at PT. Tjokro Bersaudara Gresik, focused on CNC Turning machines with the type CNC Lathe Machine XTRA 420, namely machines used to produce various types of automotive parts and based on data collected regarding the effectiveness of the machine, it shows that the machine has not fully worked effectively. This is indicated by the presence of downtime data, engine speed reduction data, and product data that does not meet specifications. To find out how good the effectiveness of a machine is, it can measure the OEE value of the machine.. It can be concluded that the effectiveness rate (OEE) of CNC Turning machines in the January-August 2022 period is between 54.16% to 59.91% with an average of 57.55% (still below the ideal OEE value of 85%) with a percentage six big losses of 42.45%.
ELECTRIC VEHICLE ROUTING PROBLEM USING ADAPTIVE SIMULATED ANNEALING Pamungkas, Prayoga Yudha; Zahabiyah, Rifdah; Shabrina, Nadiah Ghina
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 11 No. 1 (2023)
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jemis.2023.011.01.4

Abstract

Road transport is a major CO2 emission contributor globally. To tackle the challenge of reducing world carbon emissions, alternative technologies for the automobile industry are widely researched. The automotive industry has started to shift from Internal combustion engine (ICE) vehicles to electric vehicles (EVs), where EVs are the future of the automotive industry in terms of reducing greenhouse gas emissions and air pollution. EV manufacturers are continuously looking for opportunities to optimize the supply chain processes, aiming for supply chain resilience.  In this study, we present an Electric Vehicle Routing Problem (EVRP) to achieve the best decision, which is an extension of the traditional Vehicle routing problem (VRP) which in particular finding the shortest route for electric vehicles. The objective function is to find the best travel route that minimizes travel distance. Each route serves a set of customer nodes that starts and ends at a given depot node. We take battery capacity and charging stations as the constraints. In addition, the use of homogenous fleets and single depot are considered in this paper. A hybrid metaheuristic approach is used to find the best solution with the Adaptive Simulated Annealing algorithm. The use of adaptive in simulated annealing generates a higher probability of finding the best operators, which results in better solutions. A comparison of results from various metaheuristic methods is also presented in this paper to get the best method for the EVRP based on a benchmark dataset. This paper ends with recommendations for creating a routing plan that is resilient to disruptions to distribution.
ONLINE PARTIAL DISCHARGE MEASUREMENT FOR CONDITION-BASED MAINTENANCE OF HV POWER CABLES IN RAILWAY INFRASTRUCTURE Endharta, Alfonsus Julanto; Kim, Jongwoon; Kim, Yongseon
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 11 No. 1 (2023)
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jemis.2023.011.01.6

Abstract

Partial discharge (PD) measurement as one of well-known method to evaluate the condition of high voltage (HV) power cables has been studied over many decades. Cable insulation failure could result in a power outage, which could then cause a loss of service in the transportation system and even dangerous events like fire accidents. It is of a great interest to railway infrastructure operators to monitor and identify the cable faults before any possible accident occurs. The paper focuses on the diagnostic problem to detect the HV cable fault based on the Phase Resolved Partial Discharge (PRPD) patterns. Classification models, such as Random Forest and Convolutional Neural Network, are considered to classify the pattern of PRPD based on the mostly occurring PD types in HV cables, such as corona, surface, and void patterns. Experiments are performed and the PRPD data from the experiments are collected. The optimal model is applied in the online monitoring program which will be used continuously to evaluate the cable condition and arrange the optimal schedule for maintenance. According to the analysis, both algorithm perform well in the PRPD pattern categorization, with accuracy up to 83.45%. This indicates that due to the more effective behavior, PD assessment with PD sensors is preferable.
INTEGRATION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM AND HYPOTHESIS TESTING IN CHICKEN EGG INVENTORY PREDICTION Santosa, Sesar Husen; Hidayat, Agung Prayudha; Siskandar, Ridwan; Rizkiriani, Annisa
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

Accumulation of egg stock in warehouses is currently the biggest problem of product loss due to damage to egg agents. This problem occurs partly because egg agents in Bogor cannot predict final inventory availability, so the number of egg orders exceeds storage capacity and is damaged due to being stored in the warehouse for too long. A prediction model for the final stock availability of eggs (crates) was developed. The prediction model for the final stock availability was based on the number of egg agents ordering from suppliers and the selling price of eggs using the Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model uses three variables: orders to suppliers, selling prices, and inventory, with 50 training data points and 30 testing data points, namely, orders to suppliers, selling prices, and inventory. Based on the results of training and testing data, it was found that the range of the Sugeno fuzzy model for the supplier order variable was 150 - 350 cases, and the selling price was IDR 24,000/kg - 29,500/kg with a fuzzy triangular membership set. The simulation results show testing and training data with epoch = 100 using fuzzy Sugeno, and the error value = 13.7. The results of the training model's determination test (R2) obtained a value of R² = 81.02%, and the testing model had a value of R² = 81.23%. The determination value (R²) above 80% indicate that the model is suitable for predicting the final stock amount for egg agents.
DESIGNING ISO-BASED FAILURE MANAGEMENT FRAMEWORK TO ENHANCE COMPLAINT MANAGEMENT Wahyudi, Rahman Dwi; Hadiyat, Mochammad Arbi
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

Complaints are a critical indicator that can reveal problems or failures in internal processes and require quick recovery. Therefore, complaint management must be well-designed. However, this will not be meaningful if failure, a cause of complaint, is not managed well. This article aims to establish a framework for failure management that can enhance the effectiveness of complaint management. Failure is a probability that can be prevented, anticipated, and managed. Failure management frameworks must be designed in accordance with global standards to achieve acceptance. In addition, the failure management framework must be flexible, robust, and comprehensive. Based on the definition of failure, ISO 31000:2018 provides a suitable basis for developing a failure management framework to enhance complaint management. Industry can utilize the findings to improve complaint management effectiveness by increasing stakeholder involvement in anticipating failures. This involves managing, assessing, preventing, handling, monitoring, reviewing, and recording failures to facilitate continuous improvement.
ENHANCING HUMAN-COMPUTER INTERACTION EDUCATION THROUGH INTERACTIVE LEARNING TOOL: A CASE STUDY ON FITTS’S LAW Dianita, Orchida; Trapsilawati, Fitri
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

This paper presents the development and evaluation of an interactive educational tool designed to teach Fitts's Law in a Human-Computer Interaction (HCI) course for undergraduate engineering students. The Fitts' Law experiment tool allows students to modify target amplitude (A) and width (W) and observe the resulting Index of Difficulty (ID), Movement Time (MT), and model fitting, including the R-squared (R²) values through the additional plotting tool. Through this student-centered approach, students engage actively with the core concepts of motor behavior and information theory in user interface design. Findings suggest an improvement in students' output through the evaluation of their performance, engagement, conceptual understanding, data literacy and model interpretation, and reflection and perceived learning. The majority of students' remarks over 4 out of 5 maximum scores for all category's performance indicates the effectiveness of interactive learning material in HCI content to strengthen students' understanding and comprehension. This work positions interactive simulation as a necessary approach to overcome challenges in global HCI education by enhancing practical understanding of foundational models.
OPTIMIZATION OF THE OPTICAL DISTRIBUTION CABINET ON THE OPTICAL DISTRIBUTION POINT LINE USING THE INTEGER LINEAR PROGRAMMING (ILP) METHOD Kurnia, Hibarkah; Nuryono, Arif; Wiyatno, Tri Ngudi; Sulaeman, Asep Arwan; Zulkarnaen, Iskandar
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

The continuous use of software and hardware network resources results in overlapping installations at work, which results in uncontrolled operational costs in densely populated conditions, which becomes a dilemma if the arrangement is not organized. This study aims to optimize the position of the Optical Distribution Cabinet (ODC) and Optical Distribution Point (ODP) distribution lines in a distributed Fiber to the Home (FTTH) network as well as the position of customer placement on the ODP, so that the use of fibre optic cables can be minimized. This research method uses a combination of FTTH and Integer Linear Programming (ILP) where the data processing uses LINGO software. The results show that the optimal FTTH network configuration uses a 12-core fibre optic cable, ODP 1:8 passive splitters, and ODC 1:4 splitters. The proposed optimization model successfully reduced the total network deployment cost from Rp78,205,400 to Rp49,085,400, resulting in a cost efficiency improvement of approximately 59.37%. The findings also indicate that the ODP 1:8 configuration is more preferable than ODP 1:4 because it requires fewer cables, reduces network complexity, improves spatial aesthetics, and provides better scalability for future customer expansion. The practical implication of this study is that the proposed ILP-based optimization model can support telecommunications companies in designing FTTH infrastructure more efficiently, reducing installation and maintenance costs, minimizing unnecessary cable deployment, and improving long-term network scalability in densely populated urban environments. The model can also serve as a decision-support tool for future FTTH network expansion planning and infrastructure investment strategies.
ARTIFICIAL INTELLIGENCE IN SUPPLIER SELECTION AND EVALUATION: METHODOLOGICAL APPROACH AND FUTURE RESEARCH DIRECTIONS Utami, Ayu Dwi; Hartini, Sri; Handayani, Naniek Utami; Sari, Diana Puspita; Ulkhaq, Muhammad Mujiya
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

The use of artificial intelligence (AI) in the procurement sector, especially to select and evaluate suppliers, is currently developing along with the increasingly complex supply chain network and accessibility to large amounts of data. Supplier selection and evaluation methods that are commonly used are conventional methods such as multi-criteria decision-making (MCDM) methods and fuzzy-based approaches, which rely heavily on human assessment and are less adaptive to changes in supply chain environmental conditions. The authors conducted a systematic review following the PRISMA guidelines to evaluate the development of AI utilization in supplier selection and evaluation methods. A total of 21 articles published between 2015 and 2025 in the Scopus database and meeting the set inclusion criteria were used for analysis. The results show the development of AI methodologies, ranging from soft computing approaches to hybrid models and machine learning methods. AI roles in decision-making has also transitioned from being a data processing tool to acting as an automated decision maker using predictive models. However, this study also identifies several challenges, such as dominance of static models, limited use of unstructured data and ESG metrics, and practical implementation in real world situation. This research presents a comprehensive categorization of AI methodologies and roles in decision-making framework, aiming to improve the construction of more transparent and robust AI-driven procurement systems. The findings contribute to theory and managerial practices by explaining how AI can be used to improve and automate decision making process, to support more data-driven procurement strategies.

Page 37 of 37 | Total Record : 369

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