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Bigint Computing Journal
Published by Ali Institute of Research and Publication
ISSN : -     EISSN : 30325374     DOI : 10.55537/bigint
Core Subject : Science,
Computer Science & IT
Bigint Computing Journal is a journal that discusses science in the field of computing, namely: Computer Engineering (CE): Computer Engineering/Computer Systems/Information Engineering, Computer Science (CS): Computer Science/Informatics, Software Engineering (SE): Engineering Software, Information Systems (IS): Information Systems, and Information Technology (IT): Information Technology. The Bigint Computing Journal is published two times a year in the January and July editions. The submitted manuscript will be received by the editor and then checked for similarity to the Turnitin application. The review process is carried out using peer review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 4 No 1 (2026)" : 6 Documents clear
Design and Development of a Web-Based Project Management Application Using the Kanban System for Team Collaboration Fakhri Alauddin Tarihoran; Muhammad Irwan Padli Nasution
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1370

Abstract

Lack of coordination and transparency often pose significant challenges in managing project teams. This study aims to develop a web-based project management application that utilizes the Kanban method to improve team efficiency and collaboration. The application was developed using Node.js for the backend, React.js for the frontend, Tailwind CSS for the interface design, and MySQL as the database system. The development process followed the Software Development Life Cycle (SDLC) based on the Waterfall model. The results of black-box testing indicate that all core features — including board, list, and card creation, team management, and task assignment — operate successfully. The application significantly improves team coordination, transparency, and overall work performance.
Leveraging Portfolio Websites as Business Process Management Enablers to Improve Operational Effectiveness: A Case Study of a Technology Service Company Abdul Malik; Muhammad Eka; Razvan Serban
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1482

Abstract

Business Process Management (BPM) is a systematic approach to optimize business processes through continuous improvement cycles. This research aims to analyze the implementation of BPM in improving operational effectiveness at PT. Aguna Poda Natoras through the development of a company portfolio website. Using qualitative research methods through in-depth interviews, observations, and document analysis, this study found that the implementation of BPM through portfolio website development provides significant impact on process efficiency, service quality improvement, and better coordination between departments. This research contributes a novel framework demonstrating how portfolio websites can serve as operational enablers in BPM implementation, specifically in technology service companies, bridging the gap between digital transformation strategy and measurable business outcomes through quantifiable improvements in lead conversion (75%), sales cycle reduction (50%), and response time enhancement (80%). The portfolio website serves as a digital platform that supports marketing processes, client communication, and company branding. Success factors include management commitment, employee involvement, appropriate technology selection, and continuous improvement culture. This research provides practical contributions for companies in designing effective BPM implementation strategies through digital transformation.
Evaluating Digital Transformation Effectiveness in Plantation Operational Administration Using a Framework Based Information System Approach Dea Sevy Yana; Yustria Handika Siregar; Prihasti Prihasti
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1496

Abstract

This study presents an empirical evaluation of the effectiveness of digital transformation of plantation operational administration, through the implementation of the LintraMax Plantation Director (LPD) system at PT. Tolan Tiga Indonesia – Pelabian Estate uses the Digital Transformation Framework (DTF). Different from previous research, which tended to be conceptual, this study was based on the implementation of real systems used operationally by five main administrative functions, namely Payroll Clerk, Material Clerk, Accounting Clerk, Production Clerk, and General Clerk. This study uses a descriptive qualitative method with data collection through observation, in-depth interviews, and documentation, which is analyzed using the Miles and Huberman model. The results of the study show that the application of LPD is able to increase work time efficiency, speed up the reporting process, and increase the consistency and accuracy of administrative data. Based on the assessment using the Digital Transformation Framework, the level of effectiveness of digital transformation is in the Good category, with the main strength in the technology and strategy dimensions, but still requires strengthening in the aspects of work culture and human resource development. The findings of this study provide an academic contribution in the form of empirical evidence of the application of the Digital Transformation Framework in plantation operational administration, as well as practical implications for plantation organizations in designing sustainable digital transformation.  
Implementation of the K-Nearest Neighbor Algorithm for Environmental Security Level Classification Based on Crime Data Muhammad Aidil Affan; Alya Winanda
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1498

Abstract

This study aims to evaluate the effectiveness and performance of the K-Nearest Neighbor (KNN) algorithm in classifying regional security levels based on crime data. Secondary data are used with a quantitative research approach, applying KNN as the classification method and the Confusion Matrix as the evalution metric. The dataset consists of September and October data as training data and November data as testing data, with features including the number of crimes, theft cases, and violence cases. The result show that KNN achieves an accuracy of 96.15%, with a precision of 1.00 for the safe and vulnerable classes, a recall of 1.00 for the safe and alert classes, and 0.80 for the vulnerable class. This study demonstrates that KNN can effectively classify regional security levels and support decision-making based on official crime data.
Measuring Service Quality Gaps in Village Administration: A SERVQUAL-Based Approach to Community Satisfaction Assessmen Aqilla Sintiya; Muhammad Yasin Simargolang; M. Faisal Afiff Tarigan
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1499

Abstract

This study examines the quality of administrative services in Sei Mencirim Village by applying the Service Quality (SERVQUAL) method. A quantitative descriptive approach was adopted, with data collected through questionnaires distributed to residents who actively use village administrative services. The research instrument was developed based on the five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The findings indicate that all service dimensions show positive gap values between community perceptions and expectations, with an overall average gap of 0.85. This result suggests that the services provided by the village administration generally meet, and in several aspects exceed, community expectations. Among the five dimensions, assurance recorded the highest gap value, reflecting strong public confidence in staff competence and service security. In contrast, reliability showed the lowest gap value, although it remained within the satisfactory category. Overall, the study reveals that community members are satisfied with the administrative services provided by Sei Mencirim Village. Nevertheless, improving service reliability—particularly in terms of consistency, accuracy, and timeliness—remains necessary to ensure sustainable service quality. This research contributes empirical evidence on village-level public service performance and offers practical insights for policymakers seeking to improve administrative service delivery through a SERVQUAL-based evaluation framework.
Performance Evaluation of YOLOv8 for Vehicle License Plate Detection Using Standard Object Detection Metrics Kardandi Alfarizi Siregar; Bhagaskara Cahyadi; Legiman Samosir; Supiyandi Supiyandi
Bigint Computing Journal Vol 4 No 1 (2026)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v4i1.1527

Abstract

Vehicle license plate detection is a crucial computer vision task for traffic monitoring, automated parking, and vehicle identification. This study evaluates the performance of a YOLO-based license plate detection system implemented in Python and executed on Google Colab to ensure reproducibility. A public dataset of vehicle images with variations in lighting conditions and viewing angles is used for testing. Performance is assessed using precision, recall, F1-score, mAP@0.5, and mAP@0.5:0.95. The results show a precision of 0.7653 and a recall of 0.6809, yielding an F1-score of 0.7206. The mAP@0.5 reaches 0.7776, while the mAP@0.5:0.95 drops to 0.3572. As a contribution, this work provides a simple and replicable baseline evaluation workflow for YOLO-based license plate detection using standard object-detection metrics. The large gap between mAP@0.5 and mAP@0.5:0.95 indicates that the model often detects the presence of license plates but struggles to localize them precisely under stricter IoU thresholds, highlighting localization sensitivity for small objects under real-world variations. These findings can guide future improvements through dataset diversification, augmentation, and higher-resolution training to enhance bounding box accuracy.

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