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All Journal Jurnal Ilmiah Edunomika (JIE)
Endy Muhardin
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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FREE OCR IMPLEMENTATION WITH GOOGLE CLOUD VISION AND TELEGRAM FOR ACCOUNTING APPLICATIONS Endy Muhardin; Hairul Umam
JURNAL ILMIAH EDUNOMIKA Vol. 10 No. 1 (2026): EDUNOMIKA
Publisher : ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jie.v10i1.19259

Abstract

Digital transformation in accounting practices demands high efficiency in processing transaction data from physical documents to digital systems. This study explores the implementation of Optical Character Recognition (OCR) technology using the Google Cloud Vision API integrated with the Telegram Bot platform for free automation of accounting data input. The main problems faced by micro and small enterprises (MSMEs) are the high cost of automated accounting software and the complexity of manual input that is prone to human error. The methodology used is the development of a serverless- based system that connects the Telegram interface as a document scanner with Google's machine vision for text extraction. The results show that this integration is capable of extracting data from receipts and invoices with an accuracy level above 90% under normal lighting conditions. In conclusion, the use of free tools and cloud-based infrastructure can democratize advanced accounting technology for small economic actors, reduce administrative workloads, and increase the validity of financial reports.
QUEUE PROBLEMS WITH HETEROGENEOUS ARRIVAL AND SERVICE PROCESSES Hairul Umam; Endy Muhardin
JURNAL ILMIAH EDUNOMIKA Vol. 10 No. 1 (2026): EDUNOMIKA
Publisher : ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jie.v10i1.19260

Abstract

Queuing problems in modern systems are increasingly complex due to the emergence of high variability in customer arrival patterns and service capacity. This study examines the phenomenon of queues with heterogeneous arrival and service processes, where the assumptions of Poisson distribution and standard exponential service times are no longer sufficient to describe the system reality. Using a stochastic modeling approach, this study analyzes how heterogeneity in customer characteristics and differences in server performance affect system performance metrics such as average waiting time and queue length. The methodology used involves discrete-time and continuous-time Markov chain analysis to model workload fluctuations. The results show that ignoring heterogeneity factors often leads to inaccurate capacity estimates, which lead to operational inefficiencies. This study recommends dynamic resource allocation strategies and adaptive prioritization policies to mitigate the negative impacts of system variability.
Co-Authors Hairul Umam