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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 9 Documents
 1 
Search results for , issue "vol. 14 no. 1 (2026): in process" : 9 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.
ANALYSIS OF EV ADOPTION IN INDONESIA FOLLOWING THE WITHDRAWAL OF INCENTIVES USING DYNAMIC SYSTEM SIMULATION Prabaswara, Amsal Haria; Pambudi Tama, Ishardita; Eunike, Agustina
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 withdrawal of electric vehicle (EV) incentives in Indonesia in early 2026 has raised concerns over achieving the national adoption target of 2 million units by 2030. This study develops a system dynamics model to project EV adoption following the incentive withdrawal, incorporating key factors such as charging time, economic attractiveness, driving range, and charging infrastructure availability. The model, constructed using Vensim PLE, consists of a Causal Loop Diagram and a Stock-and-Flow Diagram, validated through dimensional-consistency, extreme-condition, and behaviour-reproduction test. Simulations of four scenarios from 2026 to 2035 reveal that combining fiscal incentives with charging infrastructure expansion (Scenario 3) yields the highest EV volume, annual sales, attractiveness, and number of charging stations. However, none of the scenarios achieve the 2 million target by 2030; Scenario 3 reaches it in 2033, one year earlier than the base run. These findings underscore the need for simultaneous fiscal and infrastructural support to accelerate EV adoption in Indonesia.
DESIGN OF SMART URBAN FARMING BASED ON THE INTERNET OF THINGS USING RASPBERRY PI 4 TO SUPPORT THE REALIZATION OF FOOD SELF-SUFFICIENCY Lustyana, Astuteryanti Tri; Wahyudi, Slamet; Fanani, Angga Akbar
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

Urban agriculture could be a response to limited land availability, access to food, and increasing need for local food production in highly populated cities. However, rooftop farming is subject to temperature differences, water limits, and limited operator supervision and needs constant monitoring. In this study, a pilot-scale smart urban farming system comprising a rooftop greenhouse, water recycling mechanism, drip irrigation, Raspberry Pi 4-based sensing and control, Firebase cloud database and Django online interface was constructed and evaluated. The system was put on the roof of Universitas Brawijaya, Industrial Engineering Building. Functional testing, sensor-to-cloud transmission, dashboard verification, and irrigation discharge measurements were used for performance evaluation. During a 24-h test with a sampling interval of 5 s, 15,443 of the 17,280 predicted records were successfully received, resulting to an upload success rate of 89.37%, a missing-data rate of 10.63% and an average interval of 5.59 s. Some 50,000 recordings were processed over the five-day monitoring period of the greenhouse. A five minute pump operation delivered roughly 32 mL per dripper, i.e. 0.1067 mL/s and the reaction delay between the first and the last dripper was between 20 and 26 seconds. The results indicate that the system can provide real-time monitoring and simple automation for small rooftop gardening.
INTEGRATING LEAN WASTE ANALYSIS INTO ROBOTIC INDUSTRIES: INSIGHTS FROM THE WASTE ASSESSMENT MODEL Satriyono, Raden Danang Aryo Putro; Faozi, Ekan; Fahmi, Afiqoh Akmalia; Wibowo, Ari Mukti
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 rapid growth of high‑tech manufacturing demands rigorous waste‑reduction strategies to sustain competitiveness. This research applies the Waste Assessment Model (WAM), a systematic tool for identifying and ranking the seven classic lean wastes, to PT Stechoq Robotika Indonesia, a research and development driven robotics firm. Using the three‑step WAM process (Seven Waste Relationship, Waste Relationship Matrix, and Waste Assessment Questionnaire), data were collected from production‑line operators and analyzed through matrix scoring and questionnaire weighting. Quantitative results reveal that Defect (19.9 %), Overproduction (17.1 %), and Process (17.1 %) are the most influential waste sources, while the WAQ highlights Motion (24.4 %), Transportation (22.4 %), and Process (17.1 %) as the dominant waste types to target. The study demonstrates that waste from defects, overproduction, and process steps significantly propagates secondary wastes, with inventory waste emerging as the most affected (17.8 %). These insights provide a data‑driven roadmap for lean‑focused interventions, aligning with broader industry practices on waste prioritization.
ASSOCIATION BETWEEN PERCEIVED OCCUPATIONAL SAFETY AND HEALTH IMPLEMENTATION AND PERCEIVED ACADEMIC FUNCTIONING AMONG INDUSTRIAL ENGINEERING STUDENTS: A CROSS-SECTIONAL STUDY Sudjono, Hary; Novareza, Oyong; Sholihah, Qomariyatus; Lukodono, Rio Prasetyo
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

University learning environments include classrooms, laboratories, workshops, and field-based activities, making occupational safety and health (OSH) an important component of educational quality. This study examined the association between perceived OSH implementation and perceived academic functioning among industrial engineering students. A cross-sectional survey was conducted with 75 students from the 2023, 2024, and 2025 cohorts at Universitas Brawijaya, Indonesia. Before the main survey, a preliminary instrument test was conducted with 50 different respondents, with no overlap between the preliminary and main samples. The questionnaire assessed environmental safety and health, personal protective equipment availability, OSH supervision, OSH training, student safety awareness, emergency preparedness, and perceived academic functioning related to concentration, comfort, productivity, safety, and academic improvement. Internal consistency analysis, descriptive statistics, Pearson correlation, and ordinary least squares regression with heteroskedasticity-robust standard errors were applied. The instruments demonstrated high internal consistency across the assessed constructs. Perceived OSH implementation showed a strong positive association with perceived academic functioning, indicating that students who perceived better OSH implementation also tended to report better academic functioning. However, the strength of this relationship should be interpreted cautiously because both constructs were measured from the same respondents at the same time using the same response format, and some conceptual overlap exists between the measures. Discriminant validity between the composites was not established. Therefore, the findings support a strong association between broad perceptual constructs rather than a causal or clearly construct-distinct effect.

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