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All Journal JURNAL SISTEM INFORMASI BISNIS Pendas : Jurnah Ilmiah Pendidikan Dasar
Larasati, Mitchella Sinta
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Computer Science & IT Economics, Econometrics & Finance Other

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SISTEM OTOMATIS KLASIFIKASI BUKTI PEMBAYARAN MENGGUNAKAN OCR DAN EMBEDDING BERT DENGAN PENDEKATAN MULTI-MODEL PEMBELAJARAN MESIN Larasati, Mitchella Sinta; Suryasatriya Trihandaru; Hanna Arini Parhusip
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 01 (2026): Volume 11 No. 01 Maret 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.40994

Abstract

The verification process of payment receipts in school environments is still predominantly conducted manually, leading to inefficiency and a high potential for human error. This study proposes an automated system for classifying the validity of digital payment receipts by combining Optical Character Recognition (OCR), BERT (Bidirectional Encoder Representations from Transformers) embeddings, and multi-model machine learning approaches. The system integrates EasyOCR for text extraction from payment receipts, BERT for generating semantic text representations, and four classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes (NB), and Logistic Regression (LR). The dataset consists of 185 payment receipt samples, comprising 149 valid and 36 invalid instances, collected via Google Forms and stored in a SQLite database. Experimental results demonstrate that the Multi-Layer Perceptron (MLP) model achieves the highest accuracy of 97% with a test size of 0.2, followed by Logistic Regression with an accuracy of 96.2%, while Naive Bayes exhibits the lowest performance with an accuracy of 85.7%. The proposed system is successfully implemented in a Streamlit-based application, enabling real-time verification of payment receipts with an average processing time of 1.16 seconds per sample.
Smart Catering Canteen School (SCCS) using Streamlit Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Larasati, Mitchella Sinta
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

The school canteen faced service problems of as many as 1000 students in a short break period, especially when everything had to be done manually on business processes, especially payments. The method that has existed so far is to use Google Form to place menu orders and manually pay all verified customers which causes delays and errors. Therefore, this study aims to create a business information system for canteens called the Smart Canteen System (SCCS) which uses Optical Character Recognition (OCR) and Natural Language Processing (NLP) to be able to automate payment verification and provide sequences. This SCCS business information system will convert proof of payment from text to text and processed so that the validity of the proof of payment can be proven. With the Stremlit platform, the management process can be carried out in real time and reports can be carried out immediately. With this verification, SCCS provides the main result, namely efficiency, reducing errors in business processes in the canteen. Work that was originally done manually in 2 days became 5-10 minutes in the same process.
Co-Authors Hanna Arini Parhusip Suryasatriya Trihandaru