cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hasan
Contact Email
jurnal.opsi@upnyk.ac.id
Phone
-
Journal Mail Official
eko_nsby072@upnyk.ac.id
Editorial Address
d.a Jalan Babarsari 2 Tambakbayan Yogyakarta 55281
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
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.
Arjuna Subject : -
Articles 296 Documents
 26   27   28   29   30 
Large neighborhood search for route and fleet optimization in frozen food distribution Simbolon, Jonathan Andrepa; Trismi Ristyowati; Soepardi, Apriani; Irwan Soejanto; Yuli dwi Astanti; Puryani; Chaeron, Mochammad
OPSI Vol 18 No 2 (2025): OPSI - December 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.v18i2.15743

Abstract

This study develops an optimization model to enhance the distribution efficiency of a frozen food distributor. The company faces operational inefficiencies due to excessive fleet capacity and conventional route assignment methods, which increase travel distances and overall distribution costs. To address these challenges, an extended Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) model is proposed, which integrates heterogeneous fleet characteristics and prioritizes customer service constraints. The model is solved using the Large Neighborhood Search (LNS) metaheuristic to determine optimal routing and fleet allocation strategies. The optimized model achieves a 15.95% reduction in total travel distance and a 21.84% decrease in total distribution costs compared with the company’s current operations. The findings confirm the effectiveness of the LNS-based CVRPTW approach in improving logistics performance and provide practical insights for companies seeking to minimize distribution costs through strategic route planning and fleet management.
Feature-based classification of sugarcane quality using the K-nearest neighbor algorithm Indrianti, Nur; Iqbal, Muhammad; Rustamaji, Heru Cahya; Ferriyan , Andrey; Mulyono , Panut; Ananta, Moh. Ais
OPSI Vol 18 No 2 (2025): OPSI - December 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.v18i2.16000

Abstract

The rapid advancement of artificial intelligence has enabled practical, data-driven approaches to agricultural quality assessment. However, many existing methods rely on complex sensor systems that are costly and difficult to deploy in the field. This study proposes a lightweight and interpretable K-Nearest Neighbor (KNN) model for non-destructive evaluation of sugarcane milling feasibility using five easily measurable physical attributes: relative distance ratio, internode length, mean diameter, circumference, and weight per centimeter. Samples with Brix less than 16 are categorized as not feasible for milling, while Brix equal to or greater than 16 are classified as possible. A dataset of 1,889 Bululawang samples collected in Malang, East Java, Indonesia, was evaluated across twenty-two scenarios that varied the train-test split, normalization method, distance metric, and neighborhood size. The optimal configuration, consisting of an 80:20 split, Standard normalization, the Minkowski distance metric, and k=75, achieved an accuracy of 78%. The findings confirm that physical measurements can serve as effective predictors of sugarcane quality and support data-driven inspection and sustainable resource utilization in line with SDGs 2, 9, and 12.
Application of MEAD method for occupational disease risk control in hotel housekeeping department Saputra , Manggala Rasendriya; Lucitasari, Dyah Rachmawati; Al-Bana , Nuzila Putri
OPSI Vol 18 No 2 (2025): OPSI - December 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.v18i2.16028

Abstract

Housekeeping activities in the hospitality industry are essential for service quality but are frequently associated with occupational health risks that may reduce productivity and increase employee health problems. Most existing prevention efforts focus on task-level hazards, with limited consideration of organizational and macro-level factors. Therefore, this study applies the Macroergonomic Analysis and Design (MEAD) method to address occupational disease risks in a hotel housekeeping department. Four major risk factors were identified: environmental exposure to chemical odors and waste; equipment and facility limitations, including restricted trolley mobility and inadequate Personal Protective Equipment (PPE); worker-related health complaints, reported by 67% of workers for skin and respiratory symptoms and by 50% for eye irritation, abrasions, and skin redness, with overlapping symptoms; and organizational factors related to insufficient operational supervision. Using the ten-stage MEAD framework, the housekeeping work system was redesigned through administrative controls, PPE provision, and safety awareness interventions. Post-implementation evaluation using Job Safety Analysis (JSA) showed a clear reduction in health complaints, with respiratory and skin irritation decreasing from frequent to rare and eye irritation and abrasions declining from occasional to non-occurring. These results demonstrate that MEAD is an effective framework for improving occupational health and safety in hotel housekeeping operations.
Eye-tracking study of generation z’s visual attention to traffic signs Fatin Saffanah Didin; Alam Fathurochman; Nur Miswari; Andrianta Diamas Bangun; Bonifasius Arya Satya Hanoman
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.14170

Abstract

The number of accidents in Indonesia reached 116 thousand cases in 2023, including in University areas. Most of the traffic victims are Generation Z. The impact of road accidents includes minor injuries, serious injuries, deaths, and material losses. In order to achieve the golden generation in 2045, Generation Z can achieve driving safety on the highway by ensuring a level of understanding of the meaning of traffic signs to drive safely. The purpose of this study is to investigate the visual search pattern of Generation Z on traffic sign design using an ergonomic approach. This study uses an experiment with three conditions, namely displaying warning signs, prohibitions. The results showed that the values of blink duration, dwell time, and total fixation duration did not have significantly different values for each sign condition, but the blink frequency values for the three sign conditions had significantly different values. As many as 91% of the gaze map direction moves from the top left to the center of the traffic sign. Counseling through interactive visual media will be more effective and appropriate for Generation Z. This results could help government and the National Police of Indonesia to minimize the risk of accidents.
Minimization of waiting time in coal loading and unloading processes to reduce demurrage using value stream mapping Apriani Sarempa; Pudji Astuti; Dadang Surjasa
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.14346

Abstract

Demurrage issues pose a significant challenge that affects operational efficiency in the coal transportation industry, particularly in the  coal transhipment process using mother vessel and barges. This study aims to identify and reduce waiting times that cause demurrage. Waste identification leading to demurrage was conducted through a structured Value Stream Mapping (VSM) approach consisting of current-state mapping, waste identification and analysis, process activity mapping, and future-state mapping to develop improvement recommendations for coal loading and unloading operations. The results from the Port of Loading (POL) showed a significant improvement in operational efficiency after the proposed improvements were incorporated into the future-state process. Before the improvements, value-added activities contributed 28.20% of the total time and increased to 84.97%. The total loading time was reduced from 10.04 days to 3.17 days. At the Port of Discharge (POD), value-added activities increased from 47.27% to 77.24%, and the unloading time decreased from 14.58 days to 4.89 days. This improvement was achieved through better coordination, equipment maintenance, and optimization of the loading and unloading process, which significantly enhanced overall efficiency and reduced demurrage costs. This study highlights the importance of process optimization in the coal shipping industry, offering valuable insights to reduce delays and improve profitability.
Enhancing product quality in precast concrete spun pile through the Six Sigma DMAIC Welly Mahardhika
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.14778

Abstract

Defects in precast concrete spun pile production pose risks to structural reliability, operational efficiency, and project costs. The Company has experienced recurring defects that increase the cost of poor quality and reduce customer satisfaction, highlighting the need for a systematic improvement approach. This study applies the Six Sigma DMAIC methodology to analyze and reduce defects using production data from 25,206 units manufactured between 2020 and 2022. Statistical analysis using P-chart control limits, DPMO calculations, and sigma-level evaluation revealed an overall defect rate of 0.63%, dominated by Broken PC Bars, Cracks in the Stock Area, and Dry Concrete, which together contributed 62.5% of total defects. The initial process performance averaged 3.4 sigma (approximately 2,150 DPMO), which improved to 4.67 sigma following the proposed corrective actions. Root cause analysis identified human factors, unstable machine calibration, procedural inconsistencies, and material variability as the primary contributors. Improvement recommendations include operator training, biweekly machine maintenance, digital standardization of work procedures, and stricter material control. The findings demonstrate the effectiveness of a data-driven DMAIC approach for enhancing product quality in precast manufacturing. Future improvements may incorporate IoT-based monitoring and stronger supplier collaboration to support long-term process stability and operational excellence.
Forecast-guided simulation for optimizing cargo handling personnel requirements Gunawan Gunawan; Miltiades Salvador Da Costa Sarmento; Okto Dinaryanto; Marni Astuti; Bangga Dirgantara Adiputra; Riani Nurdin; Yasrin Zabidi; Uyuunul Mauidzoh
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.15140

Abstract

The increase in the number of cargo handling at the Ahmad Yani Airport Domestic Cargo Terminal has reached 217.18% from 2009 to 2019, but decreased by 47% in 2020 due to the COVID-19 pandemic. It will affect the service in cargo handling, especially the number of personnel and facilities. This research was conducted to determine the optimal number of Cargo handling personnel at a logistics company in 2024. The forecasting methods used are Linear Moving Average, Double Exponential Smoothing Brown's, and Simple Linear Regression, where these methods will be compared to see which method has the smallest error value. Then a cargo handling simulation model will be created for this company in 2024 to find out the optimal number of Cargo handling personnel at the logistics company uses ARENA Simulation Software. The results show that in 2024 with the simple linear regression method (which has the smallest error) around 7,816,594.9 kg of Cargo is predicted as incoming Cargo, and around 7,847,581.01 kg of Cargo is predicted as outgoing Cargo in that year at the logistics company. And from the simulation model, it is found that to optimize the number of cargo handling personnel, the number of additional personnel required is 1 Aviation Security, Dispatcher, and Transporter.
Design of a liquid organic fertilizer composter device utilizing pineapple waste based on voice of customer and technical responses Budiani Fitria Endrawati; Muhamat Rafli Enga Kelen; Chandra Suryani Rahendaputri
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.15420

Abstract

Pineapple peel waste in Kutai Kartanegara Regency poses a significant environmental burden with a food waste accounting for 73.92%. One method which can be employed to reduce this pineapple peel waste is by producing liquid organic fertilizer (LOF). This research aims to design a LOF composter device utilising pineapple peel waste which tailored to the needs and preferences of local farmers. Quality function deployment (QFD) was used by collecting voice of customer (VOC) and translating it into technical response (TR) as the basis of the design. The resulting LOF composter device’s design, incorporate mobility features using wheels and brake plates, equipped with fermentation temperature monitoring via a digital monitor or sensor, and a transparent container for visual monitoring. Stainless steel was selected for its corrosion resistance. Furthermore, the device is designed to be equipped with semi-automatic or automatic shredder blade for optimal texture and a mixer blade designed for even distribution throughout the container. This device is expected to provide an effective solution for managing pineapple peel waste while producing high-quality LOF.
Implementation of YOLO11 for accurate waste detection Evrita Lusiana Utari; Restiana Anggraeni; Dhimas Arief Dharmawan; Latifah Listyalina; Herison Surbakti
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.15491

Abstract

Waste management, particularly the waste sorting process, remains a major challenge in urban areas. Manual sorting is often inefficient and prone to human error, highlighting the need for an accurate and automated waste classification system. This study investigates the implementation of the YOLO11 model for detecting and classifying six waste categories—plastic, paper, glass, metal, electronics, and organic—using a bounding box-based object detection approach. The lightweight YOLO11n variant developed by Ultralytics was employed in this study. The dataset was obtained from previous research and subsequently annotated, augmented, and divided into training, validation, and testing subsets. The model was trained for 100 epochs using the default configuration on an NVIDIA GeForce RTX 4060 Ti GPU (16 GB VRAM). During the final evaluation on the unseen test dataset, the model demonstrated reliable generalization and classification performance, achieving an overall macro-average precision of 0.75, an overall recall of 0.87, and a global F1-score of 0.79. The Paper and Plastic classes showed the highest detection performance, with Plastic achieving a flawless recall of 1.00, while the Organic class remained the most challenging due to higher visual variability. These results demonstrate that YOLO11n provides an effective approach for automated waste detection and has the potential to support more efficient and accurate waste management systems.
Sustainable strategy development for organic waste management supply chain in Bekasi Regency Probokusumo; Alfiya Rokhmah; Muhammad Arianto; Andika Bagus Pambudi; Fajar Sucipto
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.15570

Abstract

The rapid population growth and urbanization in Bekasi Regency have led to a significant increase in organic waste, creating a serious challenge in waste management. This study aims to identify and prioritize strategies to improve the performance of the organic waste management supply chain by applying Interpretive Structural Modeling (ISM) and Analytical Hierarchy Process (AHP). Data were collected from six experts in waste management, government institutions, and academia. ISM results revealed that the most fundamental elements driving system performance are land availability, infrastructure readiness, and institutional support. Meanwhile, AHP analysis indicated that the top strategic priority is provision of land and supporting infrastructure (48.6%), followed by technological enhancement (32.3%), and human resource capacity building (19.1%). The findings highlight that limited land availability is the most critical issue in Bekasi, while technology and human resources serve as supporting enablers. This study provides managerial insights for local governments and stakeholders in designing sustainable and context-specific strategies for organic waste management.

Page 28 of 30 | Total Record : 296

 26   27   28   29   30 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol 19 No 1 (2026): OPSI - June 2026 Vol 18 No 2 (2025): OPSI - December 2025 Vol 18 No 1 (2025): OPSI - June 2025 Vol 17 No 2 (2024): ISSN 1693-2102 Vol 17 No 1 (2024): ISSN 1693-2102 Vol 17, No 1 (2024): ISSN 1693-2102 Vol 16 No 2 (2023): ISSN 1693-2102 Vol 16, No 2 (2023): ISSN 1693-2102 Vol 16, No 1 (2023): ISSN 1693-2102 Vol 15, No 2 (2022): ISSN 1693-2102 Vol 15, No 1 (2022): ISSN 1693-2102 Vol 14, No 2 (2021): ISSN 1693-2102 Vol 14, No 1 (2021): ISSN 1693-2102 Vol 13, No 2 (2020): ISSN 1693-2102 Vol 13, No 1 (2020): ISSN 1693-2102 Vol 12, No 1 (2019): ISSN 1693-21020 Vol 12, No 2 (2019): ISSN 1693-2102 Vol 12, No 1 (2019): ISSN 1693-2102 Vol 11, No 2 (2018): ISSN 1693-21020 Vol 11, No 2 (2018): ISSN 1693-2102 Vol 11, No 1 (2018): ISSN 1693-2102 Vol 10, No 2 (2017): ISSN 1693-2102 Vol 10, No 1 (2017): ISSN 1693-2102 Vol 9, No 01 (2016): ISSN 1693-2102 Vol 9, No 2 (2016): ISSN 1693-2102 More Issue