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Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri
Published by Universitas Kristen Petra Surabaya
ISSN : 14112485     EISSN : 20877439     DOI : -
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Jurnal Teknik Industri aims to: Promote a comprehensive approach to the application of industrial engineering in industries as well as incorporating viewpoints of different disciplines in industrial engineering. Strengthen academic exchange with other institutions. Encourage scientist, practicing engineers, and others to conduct research and other similar activities.
Arjuna Subject : -
Articles 418 Documents
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Design and Development of a Power-Free Smart Cooler Box Using Phase Change Materials (PCMs) for Last-Mile Cold Chain Logistics Helvina Aulia Zahra; Ahmad Rusdiansyah; Ratna Sari Dewi; Rindi Kusumawardani; Azzah Dyah Pramata; Fadila Isnaini
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.13-23

Abstract

The growing demand for fresh products requires efficient and sustainable cold chain logistics, especially in last-mile delivery. Conventional refrigerated trucks utilized in last-mile logistics are hindered by high energy consumption, operational costs, and limited flexibility, creating a critical barrier for small-scale cold chain access. To address these inefficiencies, this research develops a Power-Free Smart Cooler Box that integrates Phase Change Material (PCM) technology with a high-performance composite structure to maintain frozen-grade temperatures without active power. The design methodology employed the Theory of Inventive Problem Solving (TRIZ) to systematically resolve the engineering contradiction between maximizing thermal endurance and minimizing system weight. The resulting configuration features a multi-layer insulation wall (Fiberglass/Vacuum Insulated Panel/Polyurethane) and a validated 6-sided PCM layout. This configuration enables the Smart Cooler Box to maintain an internal air temperature of ≤ −10 °C for more than 10 hours without external power. It is also equipped with a built-in IoT-based temperature sensor to ensure real-time traceability. These findings imply that the developed passive cooler provides a scientifically validated, zero-emission alternative that offers a flexible, energy-efficient, and environmentally friendly option for last-mile logistics while ensuring product safety in the final stage of delivery.
An Interpretable Data-Driven Framework for Smart Tunnel Boring Machine Performance Analysis and Energy–Cost Optimization Chairul Salam; Arrina Khanifa
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.24-46

Abstract

Tunnel Boring Machine (TBM) operations are governed by complex and nonlinear interactions among geological variability, machine control parameters, and energy consumption, posing significant challenges for reliable performance prediction and operational optimization. Conventional empirical and physics-based approaches often struggle to capture regime-dependent behavior and parameter coupling under heterogeneous excavation conditions. To address these limitations, this study proposes an integrated and interpretable data-driven framework that combines ensemble machine learning, time-series modeling, unsupervised regime identification, multi-objective optimization, and explainable artificial intelligence within a unified analytical architecture. A multisource dataset encompassing geotechnical, operational, environmental, energy, and economic parameters was analyzed using Extreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting Regression, and recurrent neural networks. Among these, XGBoost demonstrated superior predictive capability, achieving the highest coefficient of determination and consistently lower prediction errors compared with baseline models. Unsupervised clustering identified distinct operational regimes—efficient, intermediate, and aggressive—enabling a structured evaluation of energy–cost trade-offs. Regime-aware optimization further indicated substantial potential for reducing both energy consumption and operational costs relative to high-intensity operating conditions. Sensitivity analysis using SHAP, mutual information, ANOVA, and Sobol indices revealed strong interaction effects among thrust force, torque, and rock strength parameters, highlighting the coupled nature of TBM excavation mechanics. The proposed framework extends conventional predictive modeling approaches by translating data-driven insights into interpretable, regime-based operational strategies. It provides a scalable methodological foundation for the future development of digital twin applications in TBM systems and contributes to more energy-efficient, cost-effective, and sustainable tunneling operations in complex underground environments.
Big Data Analysis of Skill Requirements in the Indonesian Manufacturing Sector: A Semantic Approach Using Large Language Models Rubanto Sidi Hambaly; Muhammad Akbar; Ni Luh Saddhwi Saraswati Adnyani
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.47-58

Abstract

The rapid acceleration of Industry 4.0 has fundamentally reshaped industrial competency demands, resulting in the "skill mismatch" phenomenon and contributing to structural unemployment in Indonesia. Effective labor market analysis is required, but traditional analyses often rely on rigid, retrospective survey methodologies that fail to capture these fast-paced dynamics in real time. This study addresses this gap by introducing a novel data-driven pipeline that validates 2,688 web-scraped job advertisements against official national manufacturing registries: Statistics Indonesia (BPS) and the Mandatory Labor Report (WLKP). This registry-based validation ensures data integrity by filtering out 51.7% of unverified postings, guaranteeing that the analysis is derived exclusively from legitimate firms within the verified manufacturing sector. A semantic approach using the Gemini-based Large Language Model (LLM) was implemented to extract, normalize unstructured data into the ESCO taxonomy, and categorize it. Unlike traditional NLP metrics that often fail to maintain functional relevance, the LLM-based approach successfully preserves professional context. While automated exact matching with the rigid ESCO framework yielded low accuracy (24.3% for titles; 9.8% for skills), expert validation confirmed high semantic accuracy of 81.5% and 85%, respectively. Strategic insights reveal a dual-track workforce structure: vocational graduates require technical dexterity for operational roles, while higher education graduates are sought for strategic oversight. Analysis reveals a dominant focus on operational excellence, with specialized digital demand varying by sector, such as CATIA for high-precision engineering in the automotive sector and Optitex for 3D-digital workflows in the apparel industry. This framework serves as an industrial demand blueprint for curriculum-industry alignment, while offering a synthesized scientific interpretation of the underlying labor market patterns.
Integrated Optimization of Heterogeneous Fleet Deployment, Sailing Speed, and Bunkering Strategy Considering Adaptive Safety Stock Muhammad Syolahudin Abdurrahman; Tresnaningati Sekar Pramesta; Lailatul Rohmah; Suprayogi Suprayogi; Andi Cakravastia; Rully Tri Cahyono
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.59-75

Abstract

Logistics cost inefficiencies often stem from fragmented operational policies. Volatile global fuel prices and unpredictable maritime schedules further complicate matters. Traditional isolated optimization methods frequently fail to ensure supply chain resilience. This study addresses these limitations by developing a Mixed-Integer Linear Programming (MILP) model. The model simultaneously integrates three core strategic decisions: heterogeneous fleet deployment, sailing speed optimization, and bunkering strategy. Inventory thresholds are dynamically adjusted based on real-time sailing conditions and port-to-port consumption rates, moving beyond static buffer assumptions. This model incorporates an adaptive stock mechanism to mitigate energy supply uncertainties at transit ports while minimizing total costs, which diverges from conventional approaches. The mathematical formulation is designed to minimize total operating expenses while accounting for technical constraints, such as fixed time windows and fluctuating cargo capacities. Optimization results show that integrating these variables effectively reduces cost inefficiencies. Quantitatively, the Proposed Scenario reduced Total Cost by 18.89%, saving USD 191,555 per service cycle compared to the Existing Scenario. The integrated approach uncovers a significant trade-off between speed reduction and inventory holding costs, identifying a more balanced operational equilibrium than previous models. The findings demonstrate that applying adaptive safety stock enhances the robustness of the bunkering strategy by aligning minimum inventory levels with fuel consumption across segments between bunkering ports. This study contributes to maritime management theory by synchronizing adaptive fuel inventory management with vessel deployment and speed optimization. There are practical implications for designing more resilient and cost-effective shipping strategies. Finally, this framework serves as a precursor tool for shipping liners to maintain service reliability while navigating the complexities of modern maritime logistics.
The Influence of Information Technology Adoption on Supply Chain Resilience through Purchasing Strategy, Production Flexibility, and Supply Chain Responsiveness Katherine Trisy Achwan; Zeplin Jiwa Husada Tarigan; Zarul Azhar bin Nasir
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.76-91

Abstract

Manufacturing companies have adopted information technology to create integrated systems that facilitate coordination within the company and with external partners. This demonstrates the company's ability to generate supply chain resilience to address current global uncertainties. The study obtained data from 112 respondents from manufacturing companies in Indonesia using purposive sampling. The data were processed using PLS with an outer model test, which met the validity and reliability requirements. The results of the research hypothesis testing showed that information technology adoption plays a significant role in improving purchasing strategy, production flexibility, and supply chain responsiveness. Purchasing strategies that segment purchasing categories and involve suppliers in problem-solving can increase production flexibility but do not directly affect supply chain responsiveness. Production flexibility in a company's operations directly impacts supply chain responsiveness and resilience. Purchasing strategy and supply chain responsiveness contribute to supply chain resilience by developing capabilities. Companies can routinely identify risks and recover from disruptions. Practical contributions can provide management insights into building corporate resilience by aligning purchasing strategies, production flexibility, and supply chain responsiveness to optimize information technology for internal and external integration.
Optimization of Multi-Manned Assembly Line Balancing with Workload Smoothing using Simulated Annealing Based Hyper-Heuristic Ferry Mario Sugiarta; Suharjito
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 2 (2026): December 2026 (Pre-Print)
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.2.103 - 116

Abstract

In most cases, an assembly line with multi-manned configuration will increase efficiency in productivity. However, the complexity of multi-manned assembly can sometimes lead to bottlenecks rather than improvements in productivity. The smoothness index is a crucial performance indicator in the assembly line balancing problem, as uneven smoothness can create bottlenecks and disparities among workers; unfortunately, it is often neglected. This study developed a model that represents the multi-manned assembly line balancing problem that considers workload smoothing. Although an exact method may give the most optimal answer, due to the NP hard nature of the problem, the exact method becomes infeasible as it will take time to solve. Although in some cases, metaheuristic or hyper-heuristic methods may not guarantee the best solution quality, a near optimal solution can be given in a more reasonable time. Data from real-world cases of assembly lines and benchmark datasets were used to test the model and examine the algorithm's performance. It was found that the hyper-heuristic method based on simulated annealing could find an optimal solution for the number of workers and stations, although it required a slightly longer computation time compared to pure metaheuristic methods.
A Multi Objective Parallel Machine Scheduling Model for Bicycle Painting Problem Dave Charlton Limantara; I Gede Agus Widyadana
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.92-102

Abstract

This paper addresses the complex multiobjective parallel machine scheduling problem within a bicycle painting department. The painting process is characterized by sequence-dependent setup times driven by frequent color changes and the critical need to align painting output with assembly shop requirements to prevent stock accumulation. The research develops a mathematical model with two primary objectives: minimizing the sequence mismatch between painting and assembly and minimizing total setup time. Unlike prior research, this study simultaneously addresses sequence-dependent setups caused by color changes, the management of multiple parallel paint lines within daily capacity limits, and the critical requirement that painting output matches the input sequence needed for assembly. Given the NP-hard nature of the problem, a Genetic Algorithm (GA) method is proposed to solve the problem. Other methods such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS), are used to compare with the GA. The methods are evaluated using real-world data from a bicycle company. Results indicate that VNS outperforms GA for the objective value. The objective value score of VNS is 91.23% compared to the Genetic Algorithm 86.72%. However, the GA runtimes are significantly faster, five times better than the VNS.
Front Matter (Cover, Editorial, Table of Content) Jurnal Teknik Industri
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.%p

Abstract

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