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INDONESIA
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 410 Documents
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Coordination Improvement in Inventory Management for Electricity Distribution Materials Rahmadani, Diajeng Anjarsari; Arvitrida, Niniet Indah
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.151-161

Abstract

Electricity is a fundamental necessity. Its demand is characterized by fluctuation and broad geographic dispersion, requiring distribution units of electricity companies to be prepared to serve customer needs across all areas. The growing demand also necessitates expanding distribution networks. Establishing new connections for customers requires various materials, among which power cables are among the most crucial. However, procuring and managing these materials involves multiple stakeholders, leading to inventory management complexities. This study aims to enhance coordination in power cable inventory management by analyzing how improved coordination influences procurement costs through simulation. This study utilizes system dynamics method to understand the system of inventory management and the interactions among its elements. The existing inventory management was simulated and compared with a coordination policy scenario. The coordination policy yields better cost outcomes by managing internal coordination between two divisions, efficiently changing the use of budget and cost related to inventory. These insights can guide company management to apply the policy and enhance inventory performance for primary electricity distribution materials.
Work Systems in A Metal Casting Company Using MEAD Approach Ma'ruf, Farid; Mualief, Rafiq Fajar; Adiyanto, Okka
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.162-176

Abstract

Metal casting MSMEs in Indonesia often experience production inefficiencies, excessive workload, and inadequate occupational health and safety measures, resulting in reduced productivity and high worker fatigue. This study addresses these challenges by employing a macro-ergonomic framework to analyze and redesign work systems, specifically focusing on integrating cardiovascular workload (%CVL) analysis to quantify physiological strain, an approach rarely applied in small-scale industrial settings. The research was conducted at a representative metal casting MSME using the ten-step Macro-Ergonomic Analysis and Design (MEAD) method. Data collection involved direct field observations, organizational assessments, worker interviews, and physiological monitoring using wearable pulse sensors. Workload was evaluated using %CVL and energy expenditure calculations, while noise levels were measured using a sound level meter. Initial findings revealed that the average %CVL among workers reached 38.99%, categorized as “needs improvement,” with notable issues including unsafe working conditions, excessive overtime, and noise exposure exceeding 95 dB. To mitigate these issues, interventions were designed, including developing standard operating procedures (SOPs) for personal protective equipment, improved supervisory practices, and an additional 10-minute work break based on rest time calculations. Post-intervention measurements showed a reduction in average %CVL to 23.35%, bringing most workers below the fatigue threshold of 30%, alongside reported improvements in safety awareness and work satisfaction. The results demonstrate that integrating %CVL-based workload analysis within a macro-ergonomic framework provides a practical and effective solution for enhancing occupational health and productivity in labor-intensive MSMEs. This approach offers a scalable model for policymakers and industry practitioners to address systemic ergonomic deficiencies in similar informal industrial sectors.
Understanding Ride-Hailing Adoption Among Generation Z in Malang: An Integrated TPB-TAM Framework Putri, Amalia Romadhona Ghiffarri; Darmawan, Vertic Eridani Budi; Chen, Yuh Wen
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.177-188

Abstract

This study examines the factors influencing the choice of ride-hailing modes among Generation Z (15-29 years old) in Malang City, given their dominance as the largest user group of ride-hailing services in Indonesia. The analysis will combine the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to understand the key drivers behind their decisions to use ride-hailing services. The study surveyed 406 respondents through purposive sampling, focusing on residents of Malang who actively use ride-hailing services such as Grab, Gojek, Maxim, or inDrive. SEM was used to explain the relationships among variables, whereas MGA-PLS was applied to analyze differences by gender and vehicle ownership. Results also show that Ease of Use (KPN) significantly influences Usefulness (K) of the ride-hailing service. While both variables also positively influence Attitudes toward ride-hailing, leading to a stronger Intention to choose these services. Furthermore, Attitude (S) and Subjective Norm (NS) appear to be significant predictors of Intention (N), which in turn has a substantial impact on Behavior. The MGA-PLS results indicate that females are more influenced by Ease of Use (KPN) and Usefulness (K), whereas car owners tend to rely more on Ease of Use (KPN) when assessing Usefulness (K). These findings not only strengthen the integrated TPB-TAM framework in the context of secondary cities in Indonesia but also offer practical insights for designing more targeted and inclusive ride-hailing strategies based on user demographics.
Modeling Fresh Product Delivery Routes with Heterogeneous Vehicle Routing Problem with Time Windows and Multi-Trips Model: A Case Study Asbowo, Heri; Sirajuddin, Sirajuddin; Ilhami, Muhammad Adha
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.189-200

Abstract

This study develops a Heterogeneous Vehicle Routing Problem with Time Windows and Multi-Trips (HVRPTWMT) model designed to minimize delivery distances for fresh products. The model addresses complex operational constraints inherent in real-world logistics, including time windows, heterogeneous fleets, and multi-trip requirements. A quantitative approach was employed to formulate the HVRPTWMT model, which was then solved using an analytical method to ensure a global optimum solution was found. The model's efficacy was demonstrated through its application to historical data from April 10, 2025, yielding an optimal total distance of 774.45 km across six efficient routes. Sensitivity analysis confirmed the model's robustness and responsiveness to critical parameter changes, such as vehicle capacity, demand fluctuations, and time limits. The developed HVRPTWMT model provides a globally optimal and rule-compliant solution for complex fresh product delivery logistics.
Assessment of Barrier and Selection of Strategies for Loading and Unloading Operations at Ports using Fuzzy BWM-MABAC Salsabila, Nadhea Aurelie; Utama, Dana Marsetiya
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.201-212

Abstract

This study developed an integrated Fuzzy Multi-Criteria Decision Making (MCDM) framework to assess obstacles and prioritize improvement strategies for loading and unloading operations at ports. The Fuzzy Best Worst Method (F-BWM) was applied to obtain consistent criterion weights, while Fuzzy MABAC was used to rank six strategic alternatives. Twenty-five operational sub-criteria, adapted and validated by experts, were used to reflect the port context. Results show that modernizing equipment combined with preventive maintenance is the strongest strategy across various sensitivity scenarios. This study contributes to the field by extending the application of hybrid MCDM to the port sector and by demonstrating how integrated methods enhance the weighting and ranking processes. From a managerial perspective, these findings provide structured decision support for port authorities to allocate resources effectively, prioritize technology-based interventions, and plan for long-term improvements in human resources and infrastructure.
Integrating Lean Manufacturing and Environmental Sustainability: A Framework for the Automotive Component Industry Kemala, Arum Sahidina; Widaningrum, Dyah Lestari
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.213-224

Abstract

Environmental sustainability in the manufacturing industry has garnered widespread attention from researchers and practitioners. Many companies have adopted Lean Manufacturing (LM) principles to enhance their performance; however, they have often failed to reap the full benefits of implementing LM in terms of environmental sustainability. This study aims to build a framework combining LM and sustainability to improve environmental sustainability achievements and apply the framework in a company. The study employed a mixed-methods approach to gain an in-depth understanding of the integration of LM and sustainability in enhancing sustainable environmental performance. This study involved eight respondents in the Delphi, AHP, and FMEA processes (four managers and four supervisors). The findings indicate that the integration of LM and sustainability principles can improve environmental sustainability achievements, as demonstrated by the increase in the Environmental Sustainability Index (ESI = 92.8), which is measured based on three indicators of environmental sustainability: energy efficiency, material use, and water use. This study differs from previous works by operationalizing the PEMIC methodology as a practical framework for sustainable lean implementation in the automotive manufacturing sector. The findings of this study provide implications for the importance of environmental sustainability in achieving sustainable manufacturing performance.
Integrating Real-Time IoT Based Monitoring and Dashboard Design for Closed-House Hen Farming Handojo, Andreas; Setijarso, Edyq; Halim, Siana; Octavia, Tanti
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.237-248

Abstract

This paper presents a comprehensive solution that integrates IoT-based hardware systems with a real-time dashboard specifically designed for closed-house poultry farming, focusing on laying hens. Since laying hens are homeothermic animals, they are sensitive to temperature and humidity. Therefore, it is essential to monitor and control these environmental factors, along with ventilation, in real time. We describe a smart monitoring system that combines sensors, microcontrollers, and Android-based dashboards to provide actionable insights into poultry health and egg production performance. This system tracks various parameters, including temperature, humidity, equipment status, and key production indicators such as Hen Day Production (HDP), Hen House Production (HHP), and Feed Conversion Ratio (FCR). Evaluations conducted with local poultry farmers have shown improved awareness, usability, and the potential for increased operational efficiency. Despite some limitations, the dashboard provides a clear overview and helps inform decisions aimed at enhancing conditions and boosting egg production. This tool enables breeders to monitor and improve the performance of their laying hens and manage feeding strategies effectively. Additionally, it can assist in controlling and enhancing the closed-house environments and ventilation systems. The system's performance and usability were evaluated through User Acceptance Testing (UAT) and production Key Performance Indicators (KPIs), confirming its potential to enhance operational efficiency.
Adaptive Zone-Based Inventory Framework using Self-Supervised Learning for Cost-Efficient Restocking in the Food and Beverage Industry Agung, Anindya Annisa; Juniwati, Juniwati; Mardiono, Intan; Wang, Yu-Chieh
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.2.225-236

Abstract

The food and beverage service industry operates under high demand volatility, requiring inventory systems that are both adaptive and cost-efficient. A central challenge is maintaining product availability without excessive inventory that inflates costs. The objective of this study is to develop a data-driven restocking framework that improves cost efficiency while accounting for real operational constraints. The proposed method integrates K-Means clustering with a decision tree to generate interpretable, rule-based stock recommendations. K-Means clustering was applied as an unsupervised approach to group items into risk-based zones (Green, Yellow, Red), which were then used as labels in a supervised Decision Tree model. The model achieved 99% accuracy and an F1-score of 0.93. When applied to real industry data, it reduced Total Inventory Cost (TIC) by up to 16.9% compared with the company's MOQ-based policy while preserving stable service performance. These findings demonstrate that combining clustering and rule-based machine learning provides a practical, cost-efficient, and interpretable solution for optimizing restocking decisions in complex operational environments.
Front Matter (Cover, Editorial, Table of Content) Industri, Jurnal Teknik
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 2 (2025): December 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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Optimization of XGBoost Hyperparameters using Three Dimensional Learning AVOA for Retail Demand Prediction Noor Ibrahim , Alza; Nada, Dhea Qurrotun; Nurdiansyah, Rudi; Andoko, Andoko
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

Accurate demand forecasting is critical for retail supply chains, particularly in the Fast-Moving Consumer Goods (FMCG) sector, where even small discrepancies between predicted and actual demand can lead to excess inventory or stock shortages. This study proposes a hybrid TDLAVOA–XGBoost model that adaptively optimizes key hyperparameters to improve forecasting accuracy and stability. The analysis is conducted using 990 FMCG inventory records from a publicly available dataset to examine the impact of metaheuristic-based optimization on model performance. The TDLAVOA algorithm identifies an effective hyperparameter configuration (max_depth = 3, learning_rate = 0.01, n_estimators = 100, gamma = 1.97, subsample = 0.57, and colsample_bytree = 0.66), enabling the proposed model to achieve an RMSE of 22.53 ± 0.50 and an MAE of 19.32 ± 0.33. Compared with the default XGBoost baseline, this represents a substantial reduction in prediction error and variability. Comparative results show that TDLAVOA–XGBoost achieves performance comparable to SARIMAX and demonstrates superior accuracy relative to deep learning models, including LSTM and MLP, for limited-sample tabular FMCG demand data. Statistical validation using one-way ANOVA and Tukey’s HSD confirms that the performance differences among models are statistically significant (p < 0.0001). Overall, the findings indicate that TDLAVOA–XGBoost provides a practical and reliable approach for supporting data-driven inventory planning in retail environments.

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