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INDONESIA
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 5 Documents
 1 
Search results for , issue "vol. 14 no. 1 (2026): in process" : 5 Documents clear
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.

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