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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 162 Documents
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Implementation of Agile Scrum in the Digital Transformation of Insurance Claims Services at Integrated Ports Irfan Hs, Muh; T, Thoyyibah
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.41827

Abstract

Purpose: This study develops a web-based insurance claim information system at PT Jasaraharja Putera within an integrated port environment. The conventional claim process, which relies on manual communication such as WhatsApp and email, often causes verification delays, document loss, lack of transparency, and risks of duplicate claims or fraud. The system aims to accelerate and simplify claim submission, particularly during reporting and initial verification by field officers, through real-time integration with the company’s core systems. Additionally, digital monitoring features enhance oversight of the claims process, reducing duplication and fraud potential. Methods: This research employed a descriptive qualitative method with an Agile Scrum system development approach. Data collection involved interviews with field officers and the claims team, analysis of existing business processes, system trials, and distribution of questionnaires to users. System development was conducted iteratively over several sprints, with each sprint producing features that were tested and evaluated based on user feedback. Results: Accelerate the claims input and verification process in the field. Increase transparency of claims status through a real-time monitoring dashboard. Reduce the risk of lost documents, human error, and potential duplicate claims. Achieve high user satisfaction levels based on questionnaire results at each development sprint. Novelty: This research introduces a real-time, integrated web-based claims system for port operations, applying Agile Scrum and monitoring features to prevent fraud. It offers practical solutions to public insurance claim inefficiencies and theoretical insights for advancing insurance information systems.
K-MEANS WITH PARTICLE SWARM OPTIMIZATION FOR ERROR REDUCTION IN MICRO, SMALL, MEDIUM ENTERPISE CRAFT IN YOGYAKARTA Athallah Naufal Muthahhari; Lisna Zahrotun
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40664

Abstract

Purpose: Micro, Small, and Medium Enterprises (MSMEs) in the handicraft sector of Yogyakarta face significant challenges regarding capital access and marketing optimization. This study aims to group 146 MSMEs based on business characteristics to support the formulation of targeted empowerment strategies. Methods/Study design/approach: A quantitative approach was employed using survey data processed through encoding and normalization. The research utilized the K-Means algorithm, optimized with Particle Swarm Optimization (PSO) to determine the optimal number of clusters, and evaluated the model using Sum of Squared Error (SSE) and Mean Absolute Error (MAE). Result/Findings: The results show that the K-Means method optimized with PSO significantly outperforms the standard K-Means algorithm. Specifically, the optimized model achieved an SSE of 51.676 and an MAE of 0.116, compared to the standard K-Means algorithm which produced a higher SSE of 54.555 and an MAE of 0.124. Novelty/Originality/Value: The novelty of this study lies in the application of PSO to minimize clustering errors specifically within the Yogyakarta handicraft sector context. These findings offer a highly accurate, data-driven foundation for policymakers to design effective MSME development programs. 

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