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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Embedded Systems, Security and Intelligent Systems
Janna, Nurhikmayana
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Computer Science & IT

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IMPLEMENTASI TEMPLATE MATCHING DAN SEGMENTASI CITRA UNTUK DETEKSI KEABSAHAN DAN DENOMINASI UANG KERTAS PADA RASPBERRY PI Janna, Nurhikmayana
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6547

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem otomatisasi untuk mendeteksi keaslian dan denominasi uang kertas Rupiah berbasis Raspberry Pi dengan metode template matching dan pemrosesan citra menggunakan OpenCV. Latar belakang penelitian ini didasarkan pada masih maraknya peredaran uang palsu yang berpotensi merugikan pelaku Usaha Mikro, Kecil, dan Menengah (UMKM). Dengan menggunakan kamera smartphone dan sinar ultraviolet, sistem dapat mendeteksi nominal dan keaslian uang berdasarkan fitur seperti serial number dan benang pengaman. Pengujian menunjukkan akurasi deteksi keabsahan dan nominal sebesar 91,5%. Hasil ini menunjukkan efektivitas metode yang digunakan dalam membantu transaksi keuangan yang lebih aman dan berpotensi untuk diimplementasikan secara luas sebagai solusi deteksi uang palsu yang hemat biaya dan praktis bagi pelaku UMKM. This study aims to design and implement an automation system to detect the authenticity and denomination of Rupiah banknotes based on Raspberry Pi with the template matching method and image processing using OpenCV. The background of this study is based on the rampant circulation of counterfeit money that has the potential to harm Micro, Small, and Medium Enterprises (MSMEs). By using a smartphone camera and ultraviolet light, the system can detect the nominal and authenticity of money based on features such as serial numbers and security threads. Testing shows an accuracy of detecting the validity and nominal of 91.5%. These results show the effectiveness of the method used in helping safer financial transactions and has the potential to be widely implemented as a cost-effective and practical counterfeit money detection solution for MSMEs.
Automatic Floor Stain Detection with Image Processing: A Practical Comparison of OpenCV and RGB Grayscale Conversion: Deteksi Noda Lantai Otomatis dengan Pemrosesan Citra: Komparasi Praktikal OpenCV dan Grayscale RGB Syaharuddin, Achmad Zulfajri; Indasari, Sri Suci; Janna, Nurhikmayana; Hilmi, Andi Afdhal
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.9805

Abstract

This research develops an image processing-based floor stain detection system using grayscale conversion and binary thresholding. Two conversion approaches are compared: (i) a simple RGB grayscale formula and (ii) a built-in OpenCV function. The system uses a fixed intensity threshold of T=80 and classifies a floor as “dirty” if the detected area exceeds 20% of the image. Experiments are conducted on three floor types (plain, dark, patterned), five stain types (coffee, oil, ink, plastic, chalk), and two lighting conditions. Results show that the performance of both methods is very close with an average difference of ≈0.07% and a maximum of 0.6%; the simple formula is suitable for resource-limited devices, while OpenCV is more robust to color/lighting variations. The main contributions of this paper are (1) a practical comparison of two grayscale conversion pathways for cleanliness monitoring, (2) a simple decision rule based on the percentage of dirty area that aligns with cleanliness perception, and (3) an analysis of implementation implications for real-time systems in cleaning robots/IoT. Future directions include adaptive thresholding and morphology integration to improve reliability in dynamic environments. (Replace the current abstract paragraph containing T=80 and the 20% rule with the version above. The 20% policy reference is already explained in your manuscript).
Co-Authors Hilmi, Andi Afdhal Indasari, Sri Suci Syaharuddin, Achmad Zulfajri