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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Watasendjaja, William
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Computer Science & IT

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Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear Watasendjaja, William; Chandra, Billy; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14332

Abstract

As the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics.
Enterprise Architecture for the Cruise Industry: A TOGAF-ADM and ArchiMate-Based Approach Watasendjaja, William; Chudra, Glenny; Yohannis, Alfa
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15218

Abstract

Despite its growth and resilience over the last decades, the cruise industry faces significant challenges in its strategic, operational, and technology domains. The unique complexity of the industry requires cruise companies to adopt a structured approach to enterprise transformation. To address this problem, this aims to provide an Enterprise Architecture (EA) blueprint for the cruise industry. Using a case study of a leading cruise line, CruiseX, this study analyzes the operational model of the cruise line and apply two industry-leading standards: The Open Group Architecture Framework (TOGAF) and the ArchiMate modelling language. This study applies the four core phases of TOGAF Architecture Development Method (ADM) from the initial phase of Architecture Vision (Phase A), through the definition of Business Architecture, Information System Architecture, and Technology Architecture (Phase B to D). The ArchiMate language is utilized to visualize the core business processes, information systems, and technology architecture. By using TOGAF ADM as the technical guidelines and ArchiMate as the modeling language, the result of this study is a blueprint of core business processes, application and data that support each business processes, and the underlying technology infrastructure, that provides a structured framework and serves as an actionable tool for implementing enterprise architecture in cruise industry. This research also extends the application of TOGAF and ArchiMate to the under-research cruise industry domain. The study’s limitations include the reliance on publicly available data, the limited scope of business processes, and the lacks of practitioner validation, suggesting clear directions for future research.
Co-Authors Billy Chandra Chudra, Glenny Ito Wasito Yohannis, Alfa