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INDONESIA
Jurnal Sistem dan Manajemen Industri
Published by Universitas Serang Raya
ISSN : 25802887     EISSN : 25802895     DOI : -
Core Subject : Engineering,
Engineering Industrial & Manufacturing Engineering Transportation
This journal aims to publish the results of research in the field of Industrial Engineering is published twice a year, managed by the University of Serang Raya. The scope of Sciences covers Operations Research, Manufacturing System, Industrial Management, Ergonomics and Work System, Logistics and Supply Chain Management, and other scientific studies in accordance with scope field of Industrial Engineering research.
Arjuna Subject : -
Articles 261 Documents
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Responding to the potential environmental impact to extend battery life: Preliminary study of informal actors' readiness Annie Purwani; Siti Mahsanah Budijati; Hayati Mukti Asih; Tatbita Titin Suhariyanto; Choirun Nisa
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10472

Abstract

The increasing number of electric motorcycles in Indonesia has created new challenges related to battery waste management, potentially impacting human health, the environment, and resource scarcity. This study aims to assess the potential life cycle impacts of a 1.0 kWh electric motorcycle battery product system under repair treatment for two widely used battery types: NMC and LFP. The study used a Life Cycle Assessment (LCA) method with gate-to-gate assessment. The assessment results for both battery types with repair treatment had the greatest impact on the midpoint of freshwater eutrophication. Based on the normalized results of the five impact categories assessed, the repair treatment on LFP type batteries showed better environmental performance than the NMC type. The balancing process was found to have the greatest environmental impact for both battery types. This study confirms that recycling management by informal actors is a significant solution. The repair treatment solution contributes to providing benefits in extending the battery life cycle and does not have the potential for significant environmental impacts.
Comparative analysis of optimization methods for cut order planning in apparel manufacturing Yulison Herry Chrisnanto; Julian Evan Chrisnanto; Jeremia Oktavian
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10502

Abstract

This study seeks to address the complicated optimization challenge inherent in cut order planning (COP) in the clothing manufacturing business, emphasising fabric consumption, computational economy, and production accuracy. Three optimization approaches were compared: adaptive heuristic scoring optimizer (AHOPS), hybrid metaheuristic optimization with simulated annealing (HIMOSA), and gradient-based penalty-driven (GBPD). The results show that the GBPD method achieved the highest fabric utilization (87.13%), the fewest amount of fabric layers (12), and the maximum computational efficiency (0.022 seconds), significantly outperforming both conventional methods and alternative advanced approaches. AHOPS and HIMOSA, on the other hand, required more layers (15) and produced lower fabric utilization (around 69.70%), with HIMOSA demonstrating noticeably greater computational needs (0.527 seconds). The adaptive heuristic scoring mechanism and the combination of gradient descent and machine learning predictions, which successfully handled the combinatorial difficulties of COP, are responsible for GBPD's exceptional performance. These results offer useful information to manufacturers looking for scalable, effective optimization solutions. They also point to potential avenues for future research, such as extending the applicability of GBPD to more intricate production scenarios and further honing machine learning models for increased efficiency and adaptability.
Early fault detection system for sugar mill machines through various machine learning approach Thabed Tholib Baladraf; Taufik Djatna; Agriananta Fahmi Hidayat; Akhmad Fatikhudin; Helynda Mulya Arga Retha; Zulfikar Dabby Anwar
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10530

Abstract

The milling machine is a crucial aspect of the sugarcane agroindustry production system; a disturbed milling machine will cause a decrease in production efficiency, sap quality degradation, and excessive energy consumption. An early fault anomaly detection system through machine learning is a solution to overcome the problems in sugarcane milling machines. The purpose of this research is to propose a system architecture design for early fault anomaly detection in sugarcane agroindustry milling machines and to evaluate the performance of various machine learning models on historical sensor data, identifying the most promising approach. This study proposes a novel anomaly detection framework for sugarcane milling machines to advance smart monitoring in agro-industrial systems. Using an empirical dataset of 7,673 sensor instances (temperature, vibration, pressure, and humidity), and applying several machine learning algorithms (logistic regression, decision tree, and random forest), the framework integrates multi-sensor data to improve fault prediction and reduce downtime. The results showed that the random forest had the best accuracy, at 98.13%, followed by the decision tree, at 97.87%, and logistic regression, at 89.70%. Feature contribution analysis reveals that the vibration signal is the most dominant contributing factor among other features. The results show that machine learning is a potential approach for predicting faults in sugarcane milling machines, which can help the sugarcane agriculture industry make informed decisions in the event of disturbances in these machines.
Ambidextrous IoT governance to support EnergyCo’s digital transformation based on COBIT 2019 traditional and DevOps Prima Audina Wibowo; Rahmat Mulyana; Hanif Fakhrurroja
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10803

Abstract

The accelerating digital transformation in the energy sector demands robust governance mechanisms for emerging technologies, particularly the Internet of Things (IoT). This study examines the governance challenges faced by an energy company in Indonesia as it strives to manage IoT ecosystems while meeting regulatory requirements and achieving organizational objectives. Despite IoT’s critical role in enabling digital transformation, limited Research has explored IoT governance frameworks grounded in COBIT 2019, especially within the energy domain. To bridge this gap, this study develops an ambidextrous IoT governance framework by integrating the Traditional and DevOps Focus Area mechanisms from COBIT 2019. The framework is designed to balance stability and adaptability in managing IoT-related risks. A Design Science Research methodology is employed, complemented by a case study approach involving interviews, questionnaires, and internal document analysis to ensure contextual relevance and data saturation. The study identifies and evaluates governance priorities by aligning Governance and Management Objectives (GMOs) with national regulations, design factors, and prior research findings. Based on gap analysis using seven components of the selected GMO, DSS (Managed Security Services), the study proposes targeted improvements to IoT governance. These include strengthening leadership accountability, advancing cybersecurity competencies, and enhancing system monitoring capabilities. The implementation of these improvements is projected to elevate the DSS maturity level from 3.29 to 3.86, supporting its digital transformation agenda in alignment with COBIT 2019. This Research contributes to the literature by offering a structured, context-aware IoT governance framework and providing actionable insights for practitioners seeking to govern IoT initiatives within complex, regulated environments.
A hybrid fuzzy SWARA-COPRAS framework to evaluate sustainable co-firing in coal power plants: A case study from Indonesia Dana Utama; Berlinda Diami
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10859

Abstract

The transition to sustainable energy encourages various countries, including Indonesia, to adopt Co-Firing technology to reduce carbon emissions without requiring significant investments in power plant infrastructure. The selection of appropriate biomass materials, based on sustainability dimensions, significantly influences the success of Co-Firing implementation. This study proposes a hybrid framework that integrates the Fuzzy SWARA and Fuzzy COPRAS methods to holistically evaluate Co-Firing alternatives, considering technical, economic, environmental, social, and supply chain aspects. A case study was conducted at a power plant in Indonesia, involving four experts from the industry and academia to assess 23 sustainability sub-criteria and five biomass alternatives. The results indicate that the sub-criteria of water footprint, supplier reliability, local job creation, and co-firing retrofit cost are dominant factors in biomass selection. This research selected Alternative 2 (wood chips) as the most effective biomass material for implementation at power plants in Indonesia. Additionally, sensitivity analysis confirmed that biomass is the most stable alternative to changes in criteria weights, which offers high flexibility in the supply chain and circular economic potential. These findings contribute theoretically to developing multi-criteria decision-making methods based on fuzzy logic and practically support policymakers and industry in planning sustainable and adaptive Co-Firing strategies in the face of uncertainty.
Discrete-event simulation of truck–excavator systems in surface mining using a finite-source closed-loop queuing model Danang Setiawan; Haswika Haswika; Qurtubi Qurtubi; M. Zikra Zizo Alfieta
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.11003

Abstract

Truck-excavator interaction in surface mining is often modeled as finite-source, closed-loop queuing systems. An optimization-based approach is typically used, assuming deterministic and homogeneous fleet configurations. This paper aims to contribute to the current literature by implementing a simulation-based approach, discrete-event simulation (DES), to analyze a finite-source closed-loop queuing model in a surface mining operation. The case study used was coal overburden removal activities, which operate under a first-come, first-served discipline, and loop through four phases: loading, hauling, dumping, and returning. Under the current fleet configuration, the overburden removal activity is experiencing a 19,17% production shortfall and a match factor (MF) of 0.74. An MF below 1 indicates an under-truck system, where the excavator often idles while waiting for the trucks to arrive.  Three scenarios were tested using the validated DES model: (1) the as-is scenario with four trucks and one excavator, (2) variations of truck quantity, and (3) a route improvement scenario to reduce travel time. Simulation results indicate that adding five trucks yields the highest productivity (533.86 BCM/hour), utilization (92.48%), and MF (0.91), while the route improvement scenario achieved nearly comparable performance (513.94 BCM/hour, 88.86% utilization, MF = 0.88) with lower resource. Although the current case study operates under a homogeneous fleet with a single excavator, this study also tests the DES model under heterogeneous fleet configurations and a multi-server setup involving two excavators. These findings highlight the DES capability in modeling and analyzing a queuing system under a finite-source closed-loop, both for homogeneous and heterogeneous fleet configurations.
Design and empirical evaluation of a web-based road safety learning platform for improving knowledge on safe driving Reine Denientya; Maya Arlini Puspasari; Ceria Farela Mada Tantrika
Jurnal Sistem dan Manajemen Industri Vol. 10 No. 1 (2026): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v10i1.11987

Abstract

Road traffic injuries remains a serious concern, particularly for young riders, who face a heightened risk of being involved in accidents. Learning can be used to teach people about road safety, yet little is known about how effective online approaches are when they incorporate games, microlearning, and user-friendly design. This study examined RideSafe, a web-based platform that teaches people about road safety through gamified lessons and a simple interface. Thirty-two university students with more than two years of experience took part and were divided into two groups. One group used RideSafe with games and short lessons. The other group used a regular online learning platform. Knowledge was tested before and after the interventions, and participants also rated the usability of each platform. The results showed that both groups showed improved knowledge, indicating that online method can help people increase road safety. However, the group that used RideSafe with games and short lessons achieved greater gains than the comparison one, suggesting that gamified and bite-sized content enhances learning effectiveness. Participants also rated RideSafe as easy to use and reported high satisfaction with its design. These findings indicate that combining games, microlearning, and intuitive design can strengthen online road safety education.  RideSafe demonstrates how such learning can be made more engaging and accessible, offering a model for developing more effective road safety platforms. Continued refinement and evaluation of these approaches are recommended.
Integration of fuzzy SERVQUAL and TRIZ models in service quality method Windi Auliana; Agus Mansur; Muhamad Imron Zamzani; Haswika Haswika; Siti Balkis Mohamed Ibrahim
Jurnal Sistem dan Manajemen Industri Vol. 10 No. 1 (2026): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v10i1.11217

Abstract

Quality service in the field of Occupational Safety and Health (OSH) training is an important factor in supporting effective learning and participant success in the certification process. This study evaluates the quality of OSH training services and proposes solutions to enhance participant satisfaction using PT FTI as a case study. Fuzzy SERVQUAL and TRIZ methodologies were combined to identify inconsistencies between participants’ expectations and perceptions across five dimensions of service quality: physical form, reliability, responsiveness, assurance, and empathy. The results showed three attributes with the highest negative gaps: lack of even-handed attention, unclear information delivery, and perceived irrelevance of training materials. Based on TRIZ principles, the study proposes innovative solutions, such as hybrid training (online and offline), increased information transparency, separation of knowledge and skills evaluation, and rearranging materials to make them more contextual. The combined use of fuzzy SERVQUAL and TRIZ offers a systematic and effective strategy for analyzing and improving training quality.
An ergonomic and systemic evaluation of the effect of sleep quality and lifestyle behaviors on academic performance among industrial engineering students Mira Rahayu; Ni Nyoman Prema Mega; Yusuf Nugroho Doyo Yekti
Jurnal Sistem dan Manajemen Industri Vol. 10 No. 1 (2026): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v10i1.11335

Abstract

University life is a demanding transitional period in which academic workload, social change, and financial pressure often disrupt students’ sleep and daily health habits, with direct implications for learning and academic outcomes. This research examines how sleep quality and lifestyle habits affect the academic success of undergraduate students in industrial engineering, using a human factors and ergonomics framework. Utilizing the Pittsburgh Sleep Quality Index (PSQI) alongside the health-Promoting Lifestyle Profile II (HPLP-II), data from 293 students were paired with institutional cumulative Grade Point Average (GPA) records. The results indicate that 58% of the participants suffered from poor sleep quality (PSQI > 5). A significant negative correlation was observed between PSQI metrics and cumulative GPA (r=−0.312, p<0.01). Conversely, stress management (r=0.267, p<0.01) and health responsibility (r=0.231, p<0.01) positively correlated with GPA. Overall, sleep quality and lifestyle behaviors explained 29.6% of the GPA variance (adjusted R2 =0.296, p<0.001), with all predictors meeting multicollinearity thresholds (VIF < 3.0). No significant gender disparities were observed for sleep quality (p = 0.339) or GPA (p = 0.066), although males scored higher in overall lifestyle habits (p = 0.008). These findings demonstrate that lifestyle and sleep are critical determinants of the performance of industrial engineering students, suggesting that universities should deploy data-driven strategies, including schedule revisions and wellness initiatives, prior to longitudinal causal validation.
A genetic algorithm-based predictive–reactive scheduling for no-wait flow shop with sequence-dependent setup times under dynamic job arrivals Muhammad Ridwan Andi Purnomo; Azmi Hassan
Jurnal Sistem dan Manajemen Industri Vol. 10 No. 1 (2026): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v10i1.11538

Abstract

Modern electronics controller manufacturing operates in highly dynamic environments where production schedules must rapidly adapt to newly arriving customer orders while maintaining production efficiency and delivery performance. However, conventional predictive scheduling alone is often insufficient because schedule revisions are required for unexpected order arrivals, which may increase production completion time and customer lateness. Therefore, this study aims to develop a two-stage predictive–reactive scheduling framework for a no-wait flow shop with sequence-dependent setup times (SDST), involving 20 initial jobs with varying release times and 7 dynamically arriving jobs with distinct due dates. In Stage 1, a Genetic Algorithm (GA) is employed to maximize the number of initial jobs completed before a 300-minute production cut-off, establishing a predictive baseline schedule. In Stage 2, the proposed GA performs reactive rescheduling by integrating the unprocessed initial jobs with the newly arriving jobs to minimize the makespan deviation from the predictive schedule and reduce the new jobs’ lateness. Based on a case study, the results demonstrate that the proposed model and GA optimization effectively balance schedule stability and responsiveness. The results show that while reactive rescheduling introduces a makespan deviation of 183-time units, incoming job lateness is substantially reduced, providing a trade-off between operational continuity and service-level performance.

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