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All Journal Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri JURNAL INOVASI TEKNIK INDUSTRI Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan
Hamdi, Lazuardi Fatahilah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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POTENSI SENSOR OPTIK DAN UV UNTUK DETEKSI MATA UANG: TINJAUAN PUSTAKA Wibowo, Eko Ari; Widyastuti, Widyastuti; Hamdi, Lazuardi Fatahilah; Chanafi, Galih Tri; Fadhi, Wildan Afdalul; Betanursanti, Ida
Jurnal Inovasi Teknik Industri Vol 4, No 1 (2025): JURNAL INOVASI TEKNIK INDUSTRI
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Muhammadiyah Gombong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26753/jitin.v4i1.1707

Abstract

Penelitian ini bertujuan untuk mengkaji potensi penggunaan sensor optik dan ultraviolet (UV) dalam deteksi mata uang, dengan fokus pada autentikasi dan identifikasi nominal uang. Penggunaan metode Tinjauan Pustaka pada 13 artikel terindeks Scopus yang dikaji dan dipilih berdasarkan kriteria inklusi serta eksklusi yang ketat. Hasil review menunjukkan bahwa sensor optik digunakan untuk mengidentifikasi nominal mata uang dengan membaca pola cetakan mikro, sedangkan sensor UV diterapkan untuk memverifikasi keaslian uang dengan mendeteksi watermark atau serat tersembunyi yang hanya terlihat di bawah sinar ultraviolet. Kombinasi kedua teknologi ini terbukti meningkatkan akurasi dan kecepatan dalam mendeteksi uang, serta memberikan kontribusi dalam meningkatkan aksesibilitas bagi penyandang tunanetra. Penggunaan gabungan sensor optik dan UV memungkinkan alat deteksi yang dikembangkan untuk memiliki fungsi ganda, yakni identifikasi nominal dan verifikasi keaslian uang dengan tingkat keandalan yang lebih tinggi. Penelitian lanjutan perlu dilakukan untuk mengoptimalkan kombinasi kedua sensor tersebut guna meningkatkan deteksi uang yang lebih akurat dan efektif dalam berbagai kondisi pencahayaan. Selain itu, penelitian juga perlu fokus pada kemudahan penggunaan dan aksesibilitas teknologi untuk penyandang tunanetra dalam transaksi keuangan secara mandiri.
Edukasi dan Pendampingan Individu Penggunaan Alat Pendeteksi Nominal dan Keaslian Uang Berbasis Optik–UV bagi Penyandang Tunanetra Eko Ari Wibowo; Widyastuti Widyastuti; Muhammad Nur Wahyu Hidayah; Wildan Afdalul Fadhi; Hamdi, Lazuardi Fatahilah
Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan Vol. 3 No. 1 (2026): Februari: Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/unggulan.v3i1.3075

Abstract

People with visual impairment face barriers in cash transactions, particularly in identifying banknote denominations and verifying authenticity, which can reduce independence and increase vulnerability to fraud. This issue is also closely linked to the financial inclusion agenda, as access to reliable information on the value and authenticity of cash is a prerequisite for safe transactions among vulnerable groups. This community service program aimed to improve users’ competence through individualized, home-visit–based education and mentoring on an optical–UV sensor–based banknote denomination and authenticity detector with audio feedback. The program was implemented in collaboration with the Kebumen branches of PERTUNI and ITMI from October to December 2025. The intervention stages included an initial needs assessment, structured training using a concise module, hands-on practice through transaction scenarios, and follow-up mentoring. Evaluation employed a pre–post knowledge test, a practical performance checklist, and a usability questionnaire. Results indicated that the mean knowledge score increased from 55.1 to 80.7, and the success rate of denomination identification improved from 60.7% to 90.0%. This approach is relevant as an individualized mentoring model for blind communities when group-based training is difficult to implement.
A Lightweight Machine Vision Pipeline for Screen-Printing Defect Detection in MSMEs Using Low-Cost Image Acquisition Munandar, Galih Mahardika; Fatkhurrohman, Tiyan; Hamdi, Lazuardi Fatahilah
JTI: Jurnal Teknik Industri Vol 12, No 1 (2026): Juni 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v12i1.39516

Abstract

This study addresses the need for affordable visual inspection support in micro, small, and medium enterprises (MSMEs) engaged in screen-printing production. Although machine vision and deep learning have been widely applied in manufacturing quality control, many existing systems are designed for relatively controlled industrial settings and require stable cameras, lighting, computing resources, and technical expertise. This condition limits direct adoption by small MSMEs, where image acquisition is often performed with operator-level devices under variable lighting and background conditions. This study designed and evaluated an initial low-resource visual inspection pipeline consisting of low-cost image acquisition, five-class defect labeling, MobileNetV3-based transfer learning, performance evaluation, and TensorFlow Lite conversion. The dataset consisted of 160 screen-printing images grouped into five classes: good, misalignment, bleeding, pinholes, and ghosting. The preliminary evaluation yielded 24.38% multiclass accuracy and a loss of 2.5635, indicating that the model could not yet reliably distinguish detailed defect categories. The converted TensorFlow Lite model was 5.43 MB, indicating that the technical conversion path was feasible. A binary quality-control interpretation produced 75.63% accuracy, but 27 defective images were still predicted as pass QC. Therefore, the pipeline cannot be treated as a final quality-control decision system. The main contribution of this study is empirical evidence that image-acquisition quality, dataset sufficiency, class separability, and training configuration are critical bottlenecks in developing lightweight deep-learning-based inspection for low-resource MSME environments.
Co-Authors Chanafi, Galih Tri Eko Ari Wibowo Fadhi, Wildan Afdalul Fatkhurrohman, Tiyan Ida Betanursanti Muhammad Nur Wahyu Hidayah Munandar, Galih Mahardika Wibowo, Eko Ari Widyastuti Widyastuti Widyastuti Widyastuti Wildan Afdalul Fadhi