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International Journal of Global Operations Research
ISSN : 27231747     EISSN : 27221016     DOI : https://doi.org/10.47194/ijgor
International Journal of Global Operations Research (IJGOR) is published 4 times a year and is the flagship journal of the Indonesian Operational Research Association (IORA). It is the aim of IJGOR to present papers which cover the theory, practice, history or methodology of OR. However, since OR is primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of OR to real problems are especially welcome. In real applications of OR: forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community OR, education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation. Topics Covered: Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support System Applied Operations Research in Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Applied Operations Research inGovernment Heuristics Industrial Engineering Information Management Information Technology Inventory Management Knowledge Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Applied Operations Research in Marketing Engineering Markov Chains Mathematics Actuarial Sciences Military and Homeland Security Networks Operations Management Organizational Behavior Planning and Scheduling Policy Modeling and Public Sector Applied Operations Research inPolitical Science Production Management Applied Operations Research inPsychology Queuing Theory Revenue & Risk Management Services management Simulation Applied Operations Research inSociology Applied Operations Research inSports Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation And so on
Arjuna Subject : Umum - Umum
Articles 174 Documents
Application of Conditional Trajectory Generation on Stewart Platform Robot as a CNC Machine Drive Khoerunnisa, Ahshonat; Nur Jamiludin R; Setiawan, Aan Eko; Yuningsih, Siti Hadiaty; Hòe Nguyễn Đình
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.433

Abstract

The development of industrial automation technology in recent decades has been very rapid. One of the technologies that supports industrial automation is robot manipulators. Robots can work with high precision, speed, and safety so that by using robots, industrial processes become more productive. The type of robot itself is divided into two, namely serial and parallel structures. Robots with parallel structures tend to be less studied, developed, and used in industry compared to serial structures even though there are several advantages of these parallel structures. Parallel structures have a kinematic configuration with a closed chain type, or it can be interpreted that each arm is connected to the point of origin. This relationship will result in robots having high precision and speed. Kinematic parallel manipulators perform better when compared to serial kinematics in terms of angular accuracy, acceleration at high speeds, and high stiffness. Therefore, this type of robot is very suitable for use in industries that require high-speed applications. In this study, a robot system was developed as a driving force for a CNC machine with its movements using a trajectory tracking control system. This system was chosen because this control has a point where each point contains position and speed information that is certainly needed for the CNC machine movement system.
Decision Support System for Indibiz Package Selection Using K-Means Clustering and Analytic Hierarchy Process Martika, Karina; Tosida, Eneng Tita; Yanti, Yusma
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.434

Abstract

The rapid development of digital business in Indonesia has encouraged telecommunication providers to improve their services, particularly for small and medium-sized enterprises (SMEs). PT. Telkom Indonesia, through its Indibiz program, offers a wide variety of internet packages to support business operations. However, the diversity of available packages often leads to decision-making difficulties for both customers and internal stakeholders when determining the most suitable service based on customer needs, business scale, and financial capability. This study proposes a web-based Decision Support System (DSS) for Indibiz package selection by combining K-Means Clustering and the Analytic Hierarchy Process (AHP). K-Means is used to segment customers based on sales and usage behavior, while AHP prioritizes criteria such as speed, price, and call quota to produce recommendations. A dataset containing 6,192 Indibiz sales records from July to November 2023 was analyzed. The hybrid model was then implemented into a web-based application that enables decision-makers to visualize clustering results and determine package recommendations interactively. The experimental results demonstrate that the combination of K-Means and AHP produces more objective and consistent recommendations compared to manual selection. The DSS can help both customers and PT. Telkom Indonesia improve decision efficiency and reduce subjective bias in selecting internet packages.
Decision Support System for Letter Follow-Up Using K-Means Clustering, Principal Component Analysis, and Analytical Hierarchy Process Alif, Ilham Radan; Tosida, Eneng Tita
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.435

Abstract

Several institutions still face challenges in managing incoming letters due to the absence of a structured classification system. The follow-up process is often carried out based on the order of arrival without considering the urgency or importance of the content, leading to document accumulation and difficulties in determining priorities. To address these issues, this study aims to develop a Decision Support System (DSS) that assists in classifying letters and provides strategic recommendations for follow-up prioritization. The K-Means method is used to cluster letter data based on attribute similarities, supported by Principal Component Analysis (PCA) for dimensionality reduction. Furthermore, the Analytical Hierarchy Process (AHP) is applied to generate strategic recommendations for each cluster of letters. The research data were obtained from the institution’s budget management letter registry, which includes attributes such as institution name, department, description, program, activity, sub-activity, and sub-detail. The results indicate that the K-Means method is less optimal for clustering complex letter data, with a silhouette score of 0.208 and a Davies–Bouldin Index (DBI) of 1.793. However, the AHP method achieved a consistency ratio (CR) below 0.1, demonstrating the reliability of the generated recommendations. Overall, the developed system effectively provides accurate and efficient recommendations for letter follow-up prioritization, thereby improving decision-making processes within the institution.
Inventory Replacement Decision Support System Using Clustering and Analytical Hierarchy Process (AHP) Methods Nurjaman, Rusli; Tosida, Eneng Tita; Situmorang, Boldson Herdianto
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.436

Abstract

The Center for Human Resources Development of Transportation Apparatus (PPSDMAP) still faces obstacles in manual inventory management, resulting in a long time required to determine which items are suitable or need to be replaced. This study aims to develop a web-based Decision Support System (DSS) to assist the inventory replacement decision-making process effectively and efficiently. The K-Means Clustering method was used to group inventory data based on age, condition, and value (price) attributes using 230 inventory data from January 1–November 30, 2023. The test results produced a Davies-Bouldin Index (DBI) value of 0.435 with six optimal clusters. Furthermore, the Analytical Hierarchy Process (AHP) method was used to determine the priority of handling strategies for less suitable or unsuitable inventory groups, with a Consistency Ratio (CR) below 10%, indicating a good level of consistency. The results of the study indicate that the developed system can assist PPSDMAP in grouping inventory objectively and support inventory replacement decision-making in a systematic, efficient, and measurable manner.

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