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OPSI
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 16932102     EISSN : 26862352     DOI : https://doi.org/10.31315/opsi
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Jurnal OPSI adalah Jurnal Optimasi Sistem Industri yang diterbitkan oleh Jurusan Teknik Industri UPN “Veteran” Yogyakarta sebagai wahana publikasi hasil karya ilmiah, penelitian rekayasa teknologi di bidang Teknik Industri, Sistem Industri, Manajemen Industri dan Teknologi Informasi.
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Articles 255 Documents
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Improving sustainability index through the implementation of total productive maintenance for the bending process in electrical manufacturing Shumaesi, Rifdah; Safitri, Dian Mardi; Witonohadi, Amal
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.13176

Abstract

The significant losses caused by breakdowns in the bending machine directly affect Company X. Machines that frequently experience breakdowns can also adversely affect machine efficiency due to the need for component replacements, which contradicts the principle of sustainability aligned with the Sustainable Development Goals (SDGs) aimed at achieving a better and more sustainable future for all. The objective of this study is to improve the sustainability index (SI) through Total Productive Maintenance (TPM) and to calculate the Overall Equipment Effectiveness (OEE). These three aspects are closely related to enhancing company productivity in terms of production and sustainability. The study includes calculations of machine effectiveness using OEE and the assessment of company losses using the Six Big Losses framework. The results indicate that the average OEE over one year is 55%, which is below the international ideal standard and the company's target of 70%. Additionally, the sustainability index value obtained is 82.36%. Improvements are proposed based on the pillars of Total Productive Maintenance, focusing on safety, health, and environment, planned maintenance, and autonomous maintenance, all while considering sustainability aspects.  Furthermore, the study suggests enhancements to the 6S foundation within the company, adhering to sustainability aspects in line with SDG 12. By implementing a Total Productive Maintenance strategy that considers Safety, Health, and Environment, Planned Maintenance, and Autonomous Maintenance, and by advancing the 6S concept, the sustainability index value can be increased to 80.91%. Moreover, the effectiveness of the bending machine can be improved to 70.34%.
Application of the VDI 2221 method in the design of 3D printer machines utilizing additive manufacturing technology Irwan, Hery; Fattah, Muhammad Rusydi; Tarigan, Ryan Dana Gidion; Aritonang, Fauzan Maulana Siddiq; Sumarya, Edi
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.13477

Abstract

In the era of Industry 4.0, digital transformation integrates advanced technologies such as the Internet of Things (IoT), artificial intelligence, and data-driven manufacturing, driving advancements in science and technology. Within this framework, this study focuses on the design and fabrication of a cantilever-type 3D printer aimed at producing a prototype capable of efficient and functional 3D object printing. Additive Manufacturing (AM) technology, particularly Fused Deposition Modeling (FDM), enables the conversion of digital designs into physical products through the layer-by-layer deposition of material. The 3D printer is designed using the VDI 2221 methodology, which encompasses four key phases: task clarification, conceptual design, embodiment design, and detailed design. A key consideration during the design process is the use of filaments derived from plastic bottle waste to mitigate environmental impact. The results identify the Cartesian model variant (Variant 1) as the optimal solution, selected based on functional performance, cost efficiency, and ease of assembly. This machine achieves nozzle temperatures up to 270°C and bed temperatures up to 80°C, with a total production cost of Rp 3,657,000.00. These findings demonstrate the potential of 3D printing technology to advance plastic waste recycling and promote the development of more sustainable 3D printing solutions.
Utilizing machine learning for predictive maintenance of production machinery in small and medium enterprises Prawatya, Yopa Eka; Djanggu, Noveicalistus H; Rahmahwati, Ratih
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.13479

Abstract

Predictive maintenance involves the early detection of potential machine failures and subsequent maintenance to prevent such failures. Machine learning is a pertinent statistical method for predictive maintenance, enabling the early detection of machine failures and the implementation of preventive measures through a model. The development of the machine learning model commences with data collection from the machine, encompassing vibration, acceleration, machine temperature, and machine sound, facilitated by a microcontroller equipped with sensors. Subsequently, the data undergoes cleaning, including removing outliers or missing values and standardization. Data is partitioned into 70% allocated for training and 30% for testing. After determining hyperparameters and their values through hyperparameter tuning, the training data is utilized to train machine learning models, such as K-nearest neighbor, decision tree, and random forest models. Post-training, the models are evaluated using the remaining test data, employing performance metrics such as accuracy, precision, recall, and F1-score. The random forest model excels due to its utilization of a substantial number of trees for predictions and the full exploitation of the variables which F1-score is 91.22%. The best-performing model is subsequently deployed into a monitoring system, providing real-time machine condition predictions. The deployment results validate the accurate prediction of machine failures.
A machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing Purnama, Dwi Adi; Alfiqra, Alfiqra; Cahyo, Winda Nur
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.13960

Abstract

The aviation industry, a pillar of global transportation, is under constant pressure to increase productivity and efficiency while maintaining strict quality requirements.  Airctraft defects in production can result in significant financial losses, lead to costly rework, delays, and even safety risks. This study proposes a framework to improve productivity and efficiency in aircraft manufacturing and analyze quality control using machine learning, Six Sigma, and the QCDSME (Quality-Cost-Delivery-Safety-Morale) method. The DMAIC (Define-Measure-Analyze-Improve-Control) stage is a reference in the implementation steps of the Six Sigma method of the Airbus A320. The sigma value in this study was obtained on average for 40 periods of 4.61 sigma and a DPMO of 1225.69. At the analyze stage, a fishbone diagram is used to find the root cause of the problem.  Furthermore, a machine learning analysis was performed using the text mining method to identify the most common product components that frequently have defects in Airbus A320 and identify the main factors causing defects, by the human factor.  The enhance stage suggests a rise in overcoming challenges with the QCDSME method. Overall, it was discovered that the number of defects fell while the sigma improved and this method can enhance industry performance.
Intelligent products pricing in dynamic competition based-on Stackelberg game theory Purnomo, Muhammad Ridwan Andi
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14284

Abstract

Optimising product price is essential in dynamic competitive markets to maximise the total profit of all players and secure their survival in the market. This study addresses the intelligent optimisation of product prices in a competitive environment using Stackelberg game theory (SGT), where both a leader and follower player are considered. The objective is to determine the optimum selling prices for five main products to maximise the profits of all the players. Novel aspects of this study are the integration of optimisation models of all of the players and incorporation demand prediction accuracy into the optimisation process, ensuring that the predicted demand resulting from optimised prices aligns with historical demand data—a factor that has been disregarded by prior studies. Genetic Algorithm (GA) is employed for the optimisation algorithm due to the complexity of the model that involves numerous parameters and decision variables. The results demonstrate that the proposed products selling prices not only enhances the total profits of all of the players but also ensures that the predicted demand pattern closely fits the historical demand data pattern, validating the effectiveness of the approach.
Analyzing the impact of disaster-related factors on student preparedness using Structural Equation Modelling Putri, Arinda Soraya; Martin, Adryan Rizky; Setiawan, Eko; Fahmi, Afiqoh Akmalia; Nugroho, Munajat Tri; Putri, Evitania Salmadita; Pratama, Yuda Aditiya
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14321

Abstract

This study aims to analyse the relationship of disaster factors to the level of preparedness of students in Central Java in facing disasters. This research uses quantitative methods, because data collection in research is related to numbers and uses statistical analysis. The quantitative approach in this study uses the Structural Equation Modelling (SEM) method. Sampling was carried out by purposive sampling method or an assessment that was taken if it met certain criteria in accordance with the research topic, the sample used was 361 respondents. The method used in this research is SEM analysis, which is a multivariate statistical technique that analyses the relationship between variables. Independent variables include knowledge, attitude, policy, environment, training and religiosity. The dependent variable is preparedness. The results of research from independent variables that have an effect and have a positive direction are the attitude variable with a T-statistic value of 7,357, the training variable with a T-statistic value of 4,839 and the religiosity variable with a T-statistic value of 2,352. Variables that have a positive direction, but no effect are the knowledge variable and the policy variable.
A Quality Function Deployment model: Application design for sauce bottle washer Rahmawati, Berty Dwi; Bayogi Putra Pradana , Igh; Rachman , Buna Rizal
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14659

Abstract

Small and Medium Enterprises (SMEs) play a crucial role in employment generation and contribute substantially to national economic development, particularly in developing countries. This study examines an SME in Yogyakarta that produces, on average, 175 crates (4,200 bottles) of sauce and 5 crates (1,200 bottles) of sweet sauce daily. The bottle washing process is currently performed manually, requiring up to 3,737 minutes per day. This inefficiency increases labor dependency and delays production due to the unavailability of clean bottles. To address this issue, the study adopts the Quality Function Deployment (QFD) method to translate customer needs into technical specifications for product development. The House of Quality (HoQ) framework is utilized to identify twelve critical product attributes that inform the design of a sauce bottle washer. The resulting design comprises 17 components, including a main frame, dynamo, washing basin, bottle holder, bearing units, inner brush shafts, pulley shafts, brushes, pulleys (2.5-inch and 10-inch), gears, V-belts, chains, caps, wheels, switches, and stoppers. The implementation of this washing system successfully reduces the total bottle washing time by 720 minutes per day. This improvement enables the SME to meet its daily production targets more effectively and contributes to increased operational efficiency.
Development of an IoT-based Augmentative and Alternative Communication (AAC) for stroke patients using QFD Anto , Fitrah Japunk Lucky; Widiyanti, Syafira; Prayogi , Agung; Ismianti, Ismianti; Prian Tahalea, Sylvert; Wahyu Adventri Wibowo , Astrid; Amalia Permadi , Vynska
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14693

Abstract

Stroke is one of the leading causes of death globally, after heart disease and cancer. It often results in brain damage that impairs the function of certain body parts, including the ability to communicate. Communication disorders in stroke patients can severely impact mental health and quality of life, with studies showing that 53% of affected individuals experience depression. This study aims to design and develop an assistive communication tool tailored to the needs of stroke patients. The Quality Function Deployment (QFD) method was employed to ensure that user requirements are thoroughly addressed in the design process. Data were collected through observations and interviews with nurses and families of stroke patients. The House of Quality (HoQ) analysis revealed three main priorities for development: an integrated information system, blink-based control, and a feature to detect patient needs. The resulting solution is IISAAC (IoT-based Integrated System with Augmentative and Alternative Communication), a communication system that uses eye blink signals detected via an EOG sensor. These signals are converted into specific messages and sent directly to the nurse’s WhatsApp application. IISAAC enables more effective communication between patients, caregivers, and medical staff, helping to address the communication barriers commonly experienced by stroke survivors.
Development of smart logistic framework for blood donor information system based on Internet of Things Yulistiyanto , Mohamad Tri Angga; Mansur, Agus; Nur Cahyo, Winda; Ramadhan, Fadhil Adita
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14732

Abstract

In 2021, the amount of blood production by the Indonesian Red Cross was higher than the existing needs. This can result in wasted blood bags. The availability of abundant blood bag supplies is not balanced with access to information on the availability of these blood bags. This study aims to build a smart logistics system that can provide information on the availability of blood bags in real-time. This study utilized a quantitative and explorative approach and employed the waterfall Software Development Life Cycle (SDLC) method using PHP and the CodeIgniter 3 framework, which was integrated with IoT devices such as sensors and GPS for real-time monitoring. In addition, this system will provide a tool to predict the number of donors needed, as well as estimate the number of donor quotas required during upcoming blood donation activities. This study has succeeded in developing a blood donor information system that adopts the smart logistics concept to control blood bag stock. The resulting system is capable of reducing the risk of shortages and obsolescence while enhancing synergy among stakeholders. The design of a system that facilitates openness of information on the level of blood needs and simplification of the donor system is expected to increase community responsibility in creating sustainable blood supply availability.
Optimizing multi-item EPQ under defect and rework: A case in the plastic molding industry Nafisah, Laila; Sinaga, Rika Apriyanti Magdalena; Soepardi, Apriani; Salma, Melati; Irianto , Irianto
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.14740

Abstract

Product availability is a key indicator of service performance and is closely linked to production planning. Inaccurate decisions in lot sizing may lead to either overstock or stockout, resulting in substantial financial losses. Classical Economic Production Quantity (EPQ) models generally assume perfect quality and ignore real-world factor such as defects, rework, and backorders. This study proposes an extended EPQ model for multi-item production systems that integrates random defect rates, rework, and backordering within a single framework. Unlike previous studies that focus on single-item scenarios or deterministic defect rates, this model reflects a more realistic setting faced by companies by accounting for stochastic defects, the cost of crushing and rework, and customer backorder fulfillment. The model aims to determine the optimal lot size and production cycle that minimize the total inventory-related costs. The proposed model is validated using real case data from a plastic molding company. Results show that the model yields cost savings of 0.19% compared to the current company policy. Although modest, these savings are significant when scaled across production periods. More importantly, the model demonstrates strong adaptability to operational constraints and provides a practical decision-support tool for industries managing multiple products, quality variation, and uncertain demand.

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