Jurnal Rekayasa Sistem Industri

Integrating Sustainable Development Principles in Learning Mathematics to Stimulate Sustainable Skills in Future Generations

Santi Arum Puspita Lestari, Fitria Nurapriani, Dwi Sulistya Kusumaningrum — Fri, 26 Apr 2024 00:00:00 +0700

This research constitutes a literature review employing a qualitative approach, analyzing scholarly articles, books, and other documents related to sustainable development. This article aims to summarize and analyze previous studies concerning sustainable development in the context of mathematics education, as well as strategies that can be employed to integrate the principles of sustainable education. Integrating the principles of sustainable development into education, including mathematics education, is crucial in fostering a more environmentally responsible society and promoting sustainability across all sectors. However, its implementation remains limited. Educators face various challenges, including a lack of time, resources, and understanding of sustainable education, along with a dearth of supportive teaching materials. The principles of sustainable development can serve as a framework for developing curricula and teaching practices that are more sustainable. Educators can select mathematical problems related to environmental or social issues, discuss relevant mathematical concepts in connection with these problems, and help students comprehend the impact of mathematical decisions on the environment and society. Integrating the principles of sustainable development into mathematics education not only aids in producing a generation with sustainable skills but also motivates students to learn mathematics in more engaging and meaningful ways. A learning approach centered around sustainable development can be an effective way to prepare students for a sustainable future. The article also underscores the necessity for curriculum development, training, and professional advancement for educators.

Proactive Manufacturing Strategy for Managing the Multiproduct Production Plan

Nawang Widiatmaka, Winda Nur Cahyo — Fri, 26 Apr 2024 00:00:00 +0700

The case study focuses on a multi-product company in Indonesia that aims to develop a proactive manufacturing strategy to tackle production disruptions caused by process uncertainty. Data from 30 periods are analyzed to evaluate the company's weekly production plan performance, revealing consistent quantity fulfillment but significant discrepancies in the types of products produced compared to the plan. Process uncertainty, especially regarding quality, leads to disruptions such as uneven and inadequate material supplies. Statistical Process Control (SPC) tools, like Pareto analysis, are used to identify priority quality issues, forming the basis for Fault Tree Analysis (FTA) to trace root causes and determine intervention points. Each machine's limited set of valves and pipes poses challenges for thorough cleaning during downtime and quality management outside working hours. Introducing backup valves and pipes per machine can mitigate these issues and improve production efficiency. Companies can implement procurement strategies for pipes and valves to prevent dirty prints in the primary process, ensuring smooth material supply and minimizing disruptions. Although actual production plan achievement hasn't reached 100%, this strategy has the potential to increase product sales by 3.31% by reducing the rejection ratio of dirty prints. Ultimately, proactive procurement of spare pipes and valves is expected to increase production efficiency by 4%, indicating enhanced production schedule stability.

Actors' Behaviours in Responding to Green Incentives in the Food and Beverage Industry: A Scoping Review

Paulina Kus Ariningsih — Fri, 26 Apr 2024 00:00:00 +0700

Understanding the actors' behavior is an essential step to recognizing the standpoint of each actor to generate a strategy that facilitates collaboration and healthy coopetition in creating a green and sustainable impact. By defining a suitable strategy, the optimization of collaboration and coopetition in a sustainable food and beverage supply chain can be performed through game theory optimization. Therefore, this study aims to understand the description of the actors' behaviors corresponding to sustainable incentives in the food and beverage industry in existing game-theory optimization literature. This study was carried out by conducting a literature scoping of two significant databases: Scopus and Web of Science. 1816 articles were identified in the initial selection, and 25 were deeply evaluated and mapped. The literature scoping review shows that the supply chain's performance and interaction are related to the actors' actions, which have an independent interest, though they share a common desire to maximize its benefits. The main financial incentives are found in the game theory literature. However, the other incentives are not yet explored thoroughly. In the near future, it is suggested to conduct research on formulation of ethical and social incentives in order to be utilized in game theory optimization.

Life Cycle Cost Evaluation of Lab-Scale Modified Asphalt Mixture Production

Christian Gerald Daniel, Fadhil M. Firdaus, Mazmuria Irene Imanuella — Fri, 26 Apr 2024 00:00:00 +0700

This paper compares the life cycle costs (LCC) of various asphalt mixture technologies added with polymeric substances that modify either the binder or the whole mix and evaluates the hotspot of each mix type with the scope of cradle-to-gate from the raw materials purchase to the production of 7 kg samples. The polymer-modified binder yields the largest total cost of IDR 12,197, followed by the polymer-modified warm and hot asphalt mixture with a difference of up to 3% and 14.5%, and the standard hot mix gives the lowest result (IDR 9,344) of 23.4% smaller than the largest. Raw material price holds the major contribution with approximately 82.7%, while electricity consumption accounts for 30%. Oven heating contributes the most to the calculated impact from the lab production by 40 to 60%, followed by the mixing activity by a 10 – 30% difference. Conclusively, the LCC of bituminous mixture production is heavily influenced by raw materials price and electricity consumption, and the standard hot mix asphalt generates the lowest total cost, despite having a higher cost than the warm mix in the production stage.

Airport Inspector Position Selection: an Analytic Hierarchy Process (AHP) Approach

Yusak Sutikno, Hendri Saputra — Fri, 26 Apr 2024 00:00:00 +0700

Airport Inspector is a strategic position responsible for carrying out technical guidance activities in regulation, control, supervision, investigation and operational safety services in the airport sector. Therefore, assessing and selecting personnel with appropriate abilities, competencies, and performance is essential to ensure that the right candidate fills this position so that services can be provided optimally. The current selection process is considered to be only administrative, does not reflect the expected competencies and performance, and is less transparent, so the results cannot be accounted for. This research solves this problem by optimizing the selection process using one of the Multi Criteria Decisions Method (MCDM) approaches, the AHP method, where this approach has never been used before. Through AHP, the selection process can be carried out in a more structured, objective, transparent, and intuitive manner as expected. This research optimizes the existing assessment criteria so that all parties can still accept the results: Formal Education, Competency, Years of Service, and Work Performance. The result shows that Work Performance has the highest weighting value (0.513), followed by Competency (0.267), Work Period (0.119), and Formal Education (0.101). The synthesis value of all criteria/sub-criteria shows that Candidate 3 is the highest (scale 1 of 1), then Candidate (0.742 of 1), Candidate 4 (0.726 of 1), and Candidate 1 is the lowest (0.665 of 1).

GIS-Based Optimization for Gas Distribution Route Design: A Case Study of PT Gagas Energi Indonesia

Nur Layli Rachmawati — Fri, 26 Apr 2024 00:00:00 +0700

PT Gagas Energi Indonesia is a company engaged in the trading and natural gas business, one of which is Gaslink C-Cyl. Route determination for Gaslink C-Cyl distribution is carried out subjectively based on the proximity of locations between customers, thus creating inefficient routes, affecting the number of vehicles needed, and causing high transportation costs. Based on this problem, this study aims to minimize transportation costs by determining the optimal route and number of vehicles needed using the Multi-Trip Vehicle Routing Problem Time Windows Simultaneous Pickup and Delivery (MTVRPTW-SPD). This problem is solved using Geographic Information System (GIS) based optimization. To evaluate GIS-based optimization performance, comparison between existing condition and optimization are done for 50 demand points. Then, two scenario was developed, 100 and 200 demand points to explain the decision implication related to demand uncertainty. Based on the optimization process for 50 demand points gives better solution than existing condition in term of number of fleets needed, total distance travelled, and total transportation cost which result 3 units, 46,5%, and 43,5% respectively.

Increasing Machine Effectiveness Using the Overall Resource Effectiveness, Failure Mode Effect Analysis, and Total Productive Maintenance Methods

Wahyu Eka Putra Sanjaya, Annisa Kesy Garside, Rahmad Wisnu Wardana — Fri, 26 Apr 2024 00:00:00 +0700

PT XYZ is a company engaged in the wood industry which is marketed for export. In carrying out the production process losses often occur in multi drill machines thereby reducing the effectiveness of the machine. These losses arise because the machine is often adjusted and experiences breakdowns. Therefore, this study will analyze the effectiveness of the multi drill machine using the Overall Resource Effectiveness (ORE) method. Furthermore, fishbone diagrams, Failure Mode Effect Analysis (FMEA), and Total Productive Maintenance will be used as a method for providing improvement suggestions to increase machine effectiveness. From the calculation results, the ORE value is 59.58% (below the standard, which is 85%). Furthermore, by using the fishbone and FMEA methods, 3 failure modes with the largest RPN values were taken. Proposed improvements to increase the effectiveness of multi-bore machines are the application of autonomous maintenance, quality maintenance, preventive maintenance, and provision of safety stock.

The State of The Knowledge of Cloud Computing Technology for Manufacturing Automation Systems

Prianggada Indra Tanaya — Fri, 26 Apr 2024 00:00:00 +0700

The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning and exploring the practical aspects of implementing these X-as-a-Service (XaaS) model in manufacturing setups. The research also acknowledges the shift from legacy Distributed Numerical Control (DNC) systems to modern solutions like MTConnect and Open Platform Communication (OPC) for data exchange in automated manufacturing systems. Emphasizing the important of data collection and real-time monitoring, the study highlights the role of Industrial Internet of Things (IoT) sensors deployed at various points of manufacturing system components (machine tools, spindles, cutting tools, production units, etc.). These sensors capture real-time production and condition data, enabling informed decision-making in manufacturing systems. This research not only presents the latest knowledge but also offers insights into the challenges, strategies, and methodologies involved in the successful integration of cloud-based technology into manufacturing automation systems. It also aims to serve as a valuable resource for manufacturers, researchers, and industry professionals navigating the transformative journey toward cloud-powered manufacturing.

Application of SHERPA for Performance Identification and Human Error in Fishing Vessel Main Engine Operation

Fri, 26 Apr 2024 00:00:00 +0700

Occupational accidents due to human errors in the operation of main engines on fishing vessels need to be considered because they can be fatal to the ship. This study aims to identify and analyze critical activities in the operation of fishing vessel main engines using the Systematic Human Error Reduction And Prediction Approach (SHERPA) method. The analytical method is to collect data by observation and interviews to fulfill the SHERPA tabulation. SHERPA analysis is closely related to using Hierarchial Task Analysis (HTA) diagrams to determine the amount of activity generated in the operation of fishing vessel main engines. In addition, work intensity analysis is applied to determine the stages that need to be considered in the operation of the main engine. Based on the resulting analysis, the highest work intensity is at the operating stage of the main engine. At the same time, the critical activity level is in closing the fuel faucet and the cooling system water faucet, which are included in the activity category of turning off the main engine. The findings of this study provide insight into handling critical activities in the operation of the main engine so that a strategy is needed to reduce human error in operation. The strategy for reducing this activity is strictly supervising and modifying or adding tools to make it easier for operators. The investigation results can inform the intensity and critical points of human error in the operation of fishing vessel main engines to reduce the occurrence of work accidents.

Static and Dynamic Analysis of Tranducer Structure in Low-Cost Dynamometer for Measuring Cutting Force of Orthogonal Lathe Process

Fri, 26 Apr 2024 00:00:00 +0700

Cutting forces in turning process are usually measured using dynamometer and they can be used to evaluate the quality of the cutting process. Commercial dynamometers on the market today offered with quite expensive prices. Therefore, many researchers are trying to design dynamometers at more affordable prices. One of the most important components in designing a dynamometer is tranducer. This article discusses the design and static and dynamic analysis of a full octagonal shaped ring tranducer for measuring cutting forces in orthogonal cutting. The tranducer design is analysed statically using the Finite Element Method (FEM) and dynamically using the Experimental Modal Analysis (EMA) method to determine its structural strength. The results of the static analysis of the strength of the tranducer structure were able to withstand static loads of 224 to 388 N. It is because the tranducer stress did not exceed the yield strength of the material which is 233 MPa. While the dynamic analysis of the tranducer structure using EMA shows that the natural frequency, the damping ratio, the stiffness constant, the modal mass, and the damping coefficient 3851 Hz, 18.5 x 10⁶ N/m, 1.04%, 32 g, and 16 N/s/m, respectively. With these dynamic parameter, the tranducer design for this low-cost dynamometer is safe and reliable when used in the turning process with spindle rotation speeds reaching 20 Krpm.

Roles of Natural Language Processing in New Product Development Process: Literature Review

Gerald Shan Benediktus Simanullang, Jin Ai The — Fri, 26 Apr 2024 00:00:00 +0700

Customer satisfaction is a key success factor for a business. To provide products that meet customer satisfaction, companies must be able to understand the customers’ needs and desires. Technological developments nowadays have helped companies to understand customer desires more easily so that companies can provide products that satisfy their customer. Natural Language Processing (NLP) is a technology that allows computers to process human language. NLP is also commonly referred as text-mining. NLP has been utilized in the New Product Development (NPD) process. We compiled studies related to NLP and NPD and conducted a literature review to map out how far NLP has been utilized in NPD processes. We found that in this era of Big Data, current NLP studies most often have the goal to process text data from online reviews on e-commerce and from social media. By using NLP, large amounts of data can produce valuable Voice of Customer (VOC) information for product development. We also found that NLP technology also has been utilized in other NPD processes that do not involve VOC, such as the design stage, document processing, and extraction of requirements in the NPD process.

The Impact Analysis of Incremental Discount on Muti-Item Inventory Model Considering Perishable and Capcity Constraint Factors

Roland Silitonga, Leo Rama Kristiana, Philio Abel — Fri, 26 Apr 2024 00:00:00 +0700

The retail industry engaged in the pharmaceutical sector such as pharmacies sells various types of medicinal products and medical chemicals with fluctuating demand characteristics. This is due to the emergence of diseases that cannot be predicted with certainty. Inventory management is an important aspect to ensure the availability of medicines when needed while still being able to minimize losses both from retained capital on goods and potential expiration. Medicinal products themselves have a certain shelf-life, so products that have expired cannot be used again and cause losses to company. The form of inventory management policy is the order lot size, time between orders and reorder points. To determine the size of the order lot, in addition to the expiration aspect, the limitations of the warehouse as a place of storage and purchase discount factors need to be considered. The existence of a discount policy from suppliers can be used to minimize purchasing costs which are a component of inventory costs. The form of the discount policy that is generally given is the all-unit discount, but for certain types of products the policy given is an incremental discount. The purpose of this study is to build a multi-item probabilistic inventory model by considering the expiration factor, warehouse capacity constraints, and purchasing discount policies. This research will compare two discount policies, namely incremental discount and all unit discount. Based on the results of the sensitivity analysis, it is known that the model is sensitive to the parameters of unit discount provisions, good goods fraction, and holding costs.

Sentiment Analysis of MyPertamina Customer Reviews on Twitter with Machine Learning and Deep Learning Methods

Fri, 26 Apr 2024 00:00:00 +0700

To ensure that the distribution process of subsidized fuel is more well-targeted, PT Pertamina has developed an application called MyPertamina. The increasing number of MyPertamina users has led to an increasing number of reviews related to the use of MyPertamina. Reviews of MyPertamina fill various social media channels, including Twitter. However, the analysis of user perceptions through social media has not been optimal. Therefore, a better user sentiment mapping is needed. This study was conducted to answer this need by building a text mining model and designing a prototype that can extract and analyze sentiments from tweets related to MyPertamina. This research adopts the CRISP-DM methodology, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data obtained for model development reached 6,920 tweet data. Each data was classified into one of three sentiment categories, namely positive, negative, and neutral. After data preparation, 2,057 data were used for model development. The models tested in this study consist of Support Vector Machine (SVM), Multinomial Naïve Bayes, Gaussian Naïve Bayes, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms. The model that produced the best evaluation score and was selected for prototype development is the SVM model with an accuracy score of 83.74%, weighted precision of 83.96%, weighted recall of 83.74%, and weighted F1-score of 83.72%. The prototype is used for extracting and predicting sentiment for new datasets, which can then be visualized in the form of graphs and word clouds according to the user's needs.

Optimizing Human Resources Allocation in Bank XYZ: A Simulation-Based Approach During the COVID-19 Pandemic

Bonfilio Elyan Kusferyano, Jin Ai The, Ign. Luddy Indra Purnama — Fri, 26 Apr 2024 00:00:00 +0700

In 2020 Covid-19 Pandemic strikes the whole country. It forces Organization to change their work habits from Work from Office (WFO) to Work from Home (WFH). It is also triggered by Indonesia Government policy to keep Social Distancing and start Work from Home (WFH). Bank XYZ response by change their working policy to allow employee Work from Office and Work from Home (Hybrid). Then each employee has a different work schedule every week based on their position and Line Manager discretion. Change of amount employee who work from office affects its performance obviously, especially operational function in this case HR Data Management Function. Bank XYZ has not make a further assessment, planning or working simulation about the optimal number of WFO employees yet. By direct observation, processing time data measurement, and memo arrival time data measurement, we make a model simulation based on Arena Software to find most suggested number of WFO employee. The simulation result shows that 7 employees is the best employee number consisting of 1 Team Lead, 1 Analyst, 1 Analyst Assistant, 2 Administrator and 2 Archive Personnel. This number is less than existing employee number before, 14 employees.

Machine Learning for Quality Control in Traditional Textile Manufacturing

Vina Sari Yosephine, Tabitha Hanna, Marla Setiawati, Ari Setiawan — Fri, 26 Apr 2024 00:00:00 +0700

This research is centered on the practical implementation of machine learning and computer vision technologies to enhance production quality control within the traditional textile industry. The traditional textile sector, known for labor-intensive practices, has slowly adapted to digital transformation. We present a practical case study from Bandung, Indonesia, to validate the effectiveness of our approach in real-world textile manufacturing. By emphasizing machine learning and computer vision, this research narrows the gap between traditional textile practices and digitalization, offering tailored solutions for manufacturers seeking to excel in today's rapidly changing global market. The findings provide valuable insights into the challenges and opportunities of using machine learning and computer vision for production quality control in traditional textile manufacturing. The machine learning models in the study showed good accuracy, ranging from 75% to 100% under various lighting conditions in real-world textile manufacturing environments, confirming their suitability for practical quality control applications.