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Jurnal Sistem Teknik Industri
Published by Universitas Sumatera Utara
ISSN : 14115247     EISSN : 25279408     DOI : -
Core Subject : Science, Engineering,
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering
Jurnal Sistem Teknik Industri (JSTI) of Universitas Sumatera Utara, Faculty of Engineering, Department of Industrial Engineering, was published in 1998. Until now, the number of publications has reached 21 volumes, each of which is published by TALENTA Publisher twice a year . Each volume has two publishing numbers, namely January issue numbers and July issue numbers.
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Articles 222 Documents
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Lean Manufacturing Implementation Through Fuzzy Multi Criteria Decision-Making: A Literature Review Hutagalung, Amelia Hafsah; Ishak, Aulia; Ginting, Rosnani
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.20531

Abstract

Lean manufacturing is a systematic approach to reduce waste and increase productivity. In its implementation, lean manufacturing is often faced with complexity in its application due to inappropriate decision-making. Therefore, a more structured approach, such as the integration of the Fuzzy Multi Criteria Decision-Making (FMCDM) method, is a solution to assist decision-making within the lean framework and ensure proper lean implementation. This research aims to explore the application of FMCDM by using a Systematic Literature Review (SLR) approach, successfully analyzing 185 articles published between 2000 and 2025 focusing on integrating FMCDM and lean manufacturing. The results of the literature review show that the integration of FMCDM and lean manufacturing is still limited but has increased since 2023, with FMCDM being used for three primary purposes, namely evaluation of factors and risks in lean implementation, selection of appropriate lean tools and strategies, and assessment of lean level. This research identifies research gaps and offers practical recommendations to improve lean manufacturing effectiveness through a more adaptive and structured decision-making approach. Thus, this research is expected to significantly contribute to developing lean manufacturing theory and practice, particularly in FMCDM-based decision-making.
Humanity-Centered Design in Sustainable Product Design: Literature Review Sabri, Geubrina Hikmah; Ginting, Rosnani; Napitupulu, Humala
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.20838

Abstract

Product design has emerged as a crucial element in developing innovative solutions that prioritize not only functionality and aesthetics but also sustainability. The Human-Centered Design (HCD) approach has long been employed to ensure that user needs remain the primary focus throughout the design process. However, global challenges such as climate change, social inequality, and the long-term impacts of products necessitate a more comprehensive approach. Consequently, the concept of Humanity-Centered Design has been developed as an extension of HCD, emphasizing the importance of considering the entire ecosystem, other living beings, and long-term consequences in the product design process. This research utilizes a narrative literature review method to examine the Humanity-Centered Design (HuCD) approach within the context of sustainable product design. The review findings indicate that Humanity-Centered Design introduces five key principles, which include a focus on fundamental issues, a holistic ecosystem perspective, long-term outlook, continuous iteration, and community participation. This approach has been shown to enhance the relevance, acceptance, and sustainability of solutions across various sectors, including health technology, manufacturing, and digital domains.
Production Process Quality Inspection with Machine Learning Approach Pradana, Ari; Matondang, Nazaruddin; Anizar, Anizar
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.21005

Abstract

Technological developments in the industrial world encourage innovation in the inspection process, one of which is the application of artificial intelligence with machine learning. CV. XYZ is a palm oil machine component fabrication workshop that still applies manual quality inspection. Manual inspections are prone to errors, depend on human skills, and take a long time. This research aims to develop an automated inspection system using the YOLO (You Only Look Once) model which is a convolutional neural network (CNN) based algorithm for product defect detection. The manual inspection used is considered inconsistent, error-prone, and time-consuming. The use of machine learning is able to identify product defects such as geometry defect, porous defect, and surface defect. Evaluation of model performance using confusion matrix, loss graph, and precision recall curve. The results obtained show that the model has detection accuracy with a mAP50-95 value of 74.5%, mAP50 of 88.5%, and detection time of 0.0084 seconds per image.
Work Posture Evaluation of Manual Onion Finishing After Mechanical Peeling in a Food Processing Industry Alfi, Rizki; Sawitri, Rahma; Nadiyah, Khairun; Maryam
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.21350

Abstract

Automation is increasingly applied in food-processing industries to enhance production efficiency. However, in onion-processing operations, peeling machines often fail to completely remove the outer skin, requiring workers to perform manual re-peeling to ensure raw-material quality. This task involves repetitive hand motions and forward-leaning postures, which may contribute to musculoskeletal strain. This study aims to evaluate the work posture of manual onion re-peeling workers using the Nordic Body Map (NBM) and Rapid Upper Limb Assessment (RULA) methods. Four workers participated in the study through direct observation, photo documentation, and questionnaire administration. NBM results indicated that the most frequent complaints occurred in the the shoulders, upper arms, elbows, forearms, wrists, and lower back. The average RULA score of 6 indicates a high-risk posture requiring prompt ergonomic intervention. The strong alignment between NBM and RULA findings reinforces the accuracy of the ergonomic diagnosis. This study highlights the need for ergonomic improvements such as workstation height adjustment, provision of lumbar-supported seating, micro-break scheduling, and simple tool redesign to reduce MSD risks and improve productivity in food-processing operations.
Synergizing Ergo-Technology by Integrating Physical, Cognitive, and Organizational Ergonomics for Sustainable Work System Design in Industry 4.0 Angga Yuda Sakti; Liza Dwi Eftiza Khairunniza
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.21901

Abstract

The manufacturing industry still relies heavily on manual labor to take advantage of human flexibility. As work-related injuries increase, the importance of ergonomics in engineering is becoming more recognized. However, in small and medium-scale manufacturing, the application of ergonomics has not yet received sufficient attention. The aim of this study is to review literature on the application of ergonomics in work system design, identify common approaches, analyze the impact of ergonomics, and provide recommendations for more effective implementation. A descriptive qualitative approach with a literature review of Google Scholar-indexed articles was used. The analysis concludes that ergonomics application in manufacturing is growing, with a predominant use of quantitative methods. Additionally, integrating physical, cognitive, and organizational ergonomics has positively impacted innovation and job satisfaction. Based on these findings, future research should focus on developing integrated ergonomic interventions, long-term studies, and economic analyses to improve ergonomics implementation.
Bobabox SME Development Strategy Based on the Influence of Supply Chain Resilience Variables on Company Performance Dhede Pristi Afrinda; Matondang, Nazaruddin; Hidayati, Juliza
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.22904

Abstract

Supply chain resilience refers to the capacity of a supply chain to develop adequate responsiveness, readiness, and recovery strength so it can handle risks and disruptions and restore its operations to their previous condition or achieve an even better state. Bobabox faced challenges such as fluctuating sales, the closure of six outlets, shifting customer demand, and increasing competition. This study involved 81 boba drink outlets in Medan using purposive sampling. Data were collected through questionnaires and an interview with the Bobabox CEO, then analyzed using Smart-PLS 4 to test relationships between variables. SWOT and QSPM methods were applied to formulate strategies. The findings show that supply chain resilience significantly influences company performance. The IE matrix placed Bobabox in quadrant V (hold and maintain), suggesting that the company should preserve its current market position and focus on efficiency rather than aggressive expansion. The study identified 11 internal factors (IFE score: 2.73636) and 8 external factors (EFE score: 2.67857). The SWOT matrix produced nine alternative marketing strategies. Based on the QSPM results, the most recommended strategy is reducing supplier dependency through multi-sourcing to mitigate geopolitical risks (W2, T3).
Combination Between Design for Assembly and Design for Serviceability Method in Concurrent Engineering: A Literature Review Harahap, Akbar Gading Alfadli; Ginting, Rosnani; Ishak, Aulia
Jurnal Sistem Teknik Industri Vol. 28 No. 1 (2026): JSTI Volume 28 Number 1 January 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i1.20505

Abstract

Increased competition in the industry encourages companies to focus on customer satisfaction to maintain competitiveness. The combination of Design for Assembly (DFA) and Design for Serviceability (DFS) methods is a common concern for researchers, especially in product development. This research describes the integration of DFA and DFS methods to solve product problems, starting from the method development stage, the interrelationship between the two methods, etc. The successful application of these methods is evident through high efficiency and improved product quality. Key factors involved management cooperation, use of quality tools, and teamwork. Leading journals highlight product cost reduction through Design for Serviceability and DFA techniques. Emphasis on sustainability, especially by integrating sustainability goals in product design, evidenced positive environmental impact and market response. Research also highlighted the need for identification and prioritisation of customer requirements and optimisation of production process parameters. The integration of modelling methods and design systems, such as in the development of a modelling system for metal casting processes, demonstrated success in achieving effective product development goals. Overall, this research provides an in-depth understanding of the potential and benefits of applying DFA and DFS in efficient, high-quality and sustainable product development, providing a foundation for further development and implementation by practitioners and researchers.
The Use of Machine Learning Algorithms for Supply Chain Optimization at PT. XYZ Manik, Diomen Syahputra; Matondang, Nazaruddin; Panjaitan, Nismah
Jurnal Sistem Teknik Industri Vol. 28 No. 1 (2026): JSTI Volume 28 Number 1 January 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i1.22207

Abstract

Increased demand fluctuations pose a major challenge in supply chain management, particularly in the fast-food beverage industry like PT. XYZ. This research aims to build and evaluate a demand forecasting model based on machine learning, considering multivariate variables such as product price, seasonal trends, weather, per capita income, population, and historical sales data. The three algorithms used are Random Forest Regressor, Gradient Boosting Regressor, and Prophet Time Series Model. This research method employs a quantitative approach with descriptive-predictive analysis based on time-series data. Model evaluation was conducted using MAE, MSE, RMSE, and MAPE metrics. The research results indicate that Prophet has the highest accuracy (MAPE: 2.33%) and excels in capturing seasonal trends, while Random Forest ranks second (MAPE: 2.47%) with an advantage in comprehensively handling multivariate variables. Gradient Boosting yields the lowest accuracy (MAPE: 2.70%). The conclusion of this study recommends the use of Prophet for short-term seasonal-based predictions, while Random Forest is more suitable for medium to long-term strategic planning. The combination of the two has the potential to become an accurate and adaptive hybrid approach for optimizing the demand forecasting system at PT. XYZ.
Forecasting Demand for Cardboard Boxes Using Some Forecasting Method at PT XYZ Gozali, Lina; Wildan; Sukania, I Wayan; Ali, Ahad; Susanto, Sani
Jurnal Sistem Teknik Industri Vol. 28 No. 1 (2026): JSTI Volume 28 Number 1 January 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i1.22252

Abstract

In the manufacturing sector, accurate demand forecasting is essential for effective material planning and inventory management. PT. XYZ, a company specialising in the production of corrugated carton boxes, currently faces challenges aligning raw material procurement with market demand due to the use of subjective, non-systematic forecasting methods. This research proposes applying statistical forecasting techniques to develop a more reliable and automated forecasting system. The study utilises historical monthly sales data collected over a one-year period, which are analysed using time series forecasting methods. The models are assessed based on key forecasting error metrics, including mean absolute deviation, mean squared error, and mean absolute percentage error. The model construction, data processing, and visualisation, thereby improving efficiency and reducing manual intervention. The findings reveal that combining seasonal statistical models with programming tools enhances forecast accuracy and supports data-driven decision-making within the organisation. This forecasting system can assist the planning division of PT. XYZ is optimising raw material allocation, reducing excess inventory, and preventing material shortages. In conclusion, the study recommends that PT. XYZ implements the decomposition forecasting model as a practical solution for improving the quality of its sales data. The research contributes to the development of forecasting systems tailored for industrial environments with fluctuating, seasonal demand.
Integration of Six Sigma, Fault Tree Analysis, and Design of Experiment in Welding Quality Improvement Sembiring, Praja Dinata; Anizar; Napitupulu, Humala
Jurnal Sistem Teknik Industri Vol. 28 No. 1 (2026): JSTI Volume 28 Number 1 January 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i1.23607

Abstract

The quality of water wall welding in boiler fabrication remains a critical issue, as a high welding defect rate of 25–30% can lead to leakage, increased repair welding, and reduced reliability of welded joints. This study aims to systematically reduce welding defects and improve the quality of water wall welded joints by integrating Six Sigma, Fault Tree Analysis (FTA), and Taguchi Design of Experiments (DOE). Six Sigma with the DMAIC framework was applied to evaluate process performance and define critical quality characteristics, while FTA was used to identify the dominant root causes of welding defects. The analysis revealed that suboptimal GMAW welding parameters—specifically welding current, root gap, groove angle, and travel speed—were the main contributors to defect formation in water wall welded joints. Taguchi DOE was subsequently employed to determine an optimal and robust combination of welding parameters. The results show that the optimal parameter setting increased the tensile strength of the welded joint to 495.08 MPa and improved the signal-to-noise ratio by 1.36 dB, indicating enhanced welding quality and process stability.The optimized parameters were implemented through an updated Welding Procedure Specification (WPS), enabling the improvement results to be consistently applied in production. This study demonstrates that the integrated Six Sigma–FTA–DOE approach provides an effective and systematic solution for improving water wall welding quality in boiler manufacturing.

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