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All Journal Jurnal Teknik ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAREE (Journal on Advanced Research in Electrical Engineering) Sewagati
Muhammad Attamimi
Departemen Teknik Elektro Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Pelatihan Video Editing untuk Menunjang Pengembangan Perangkat Live Streaming Studio Portable Cerdas di Pondok Pesantren Qomaruddin Gresik Ronny Mardiyanto; Ruth Johana Hutagalung; Riky Tri Yunardi; Riza Agung; Syahri Muharom; Djoko Purwanto; Rudy Dikairono; Muhammad Rivai; Muhammad Attamimi; Totok Mujiono; Devy Kuswidiastuti
Sewagati Vol 7 No 6 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v7i6.737

Abstract

Permasalahan yang ada di beberapa pondok pesantren di Jawa Timur, khususnya di Pondok Pesantren Qomaruddin Gresik adalah adanya kesenjangan teknologi dan potensi daerah yang belum dikelola secara profesional. Metode dakwah yang selama ini dilakukan masih menggunakan metode konvensional, yaitu berkumpul di sebuah tempat (masjid/mushola), kemudian para santri menyimak ajaran-ajaran yang disampaikan oleh seorang Kiai atau Ustaz. Digitalisasi setiap kajian/pengajian akan menyebarkan nilai media dakwah menjadi lebih luas. Monetisasi melalui platform seperti Youtube, Facebook, atau media lain dapat menjadi pemasukan tambahan bagi pondok pesantren. Pengabdian kepada masyarakat ini yaitu memberikan pelatihan video editing yang akan mendukung pengembangan perangkat live streaming studio portable cerdas dan menggali Informasi untuk memastikan solusi yang diterapkan tepat dan relevan dengan kebutuhan yang ada. Kegiatan ini telah dilakukan pada hari Selasa, 11 Juli 2023 di Pondok Pesantren Qomaruddin Gresik. Hasil survei kepuasaan menunjukkan bahwa kegiatan ini sangat bermanfaat dan perlu dilanjutkan.
Rancang Bangun Sistem Pendeteksi Keaslian Uang Kertas Rupiah Menggunakan Sinar UV dengan Metode Machine Learning Zulfiansyah, Alvin Daffa Kumara; Kusuma, Hendra; Attamimi, Muhammad
Jurnal Teknik ITS Vol 12, No 2 (2023)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v12i2.118320

Abstract

Sistem pendeteksi keaslian uang kertas rupiah merupakan metode yang dibutuhkan karena uang merupakan materi penting sehingga sering terjadi tindak kriminalitas. Metode yang sering digunakan adalah image processing untuk scaling dan color conversion, segmentasi untuk mendapatkan pola tertentu, atau dengan memperhatikan ciri fisik uang kertas rupiah, seperti tekstur dan pola terawang. Namun, metode-metode tersebut dapat dikatakan kurang efektif apabila terjadi peningkatan pemalsuan uang kertas rupiah dalam bentuk fisik. Sehingga penulis merancang sistem counting yang menggunakan sinar ultraviolet untuk menampilkan hidden pattern sebagai fitur khusus mendeteksi keaslian uang dengan metode machine learning dan menambahkan sistem counting buatan sendiri dengan fitur rotation invariant untuk melakukan pendeteksian lebih dari satu lembar uang kertas rupiah. Perpaduan image processing dan machine learning pada penelitian ini mampu memberikan hasil pengujian prototipe dengan metode k-NN dan CNN memberikan persentase keberhasilan prediksi sebesar lebih dari 90% dibandingkan dengan SVM, Random Forest, dan Naive Bayes yang kurang dari 85%. Mekanik prototipe ini mampu melakukan pendeteksian kurang dari 4 detik dengan 10 lembar uang kertas yang dideteksi. Dengan meningkatkan metode detection yang lebih expert, seperti deep learning dengan model yang fleksibel jika ingin digunakan dalam pendeteksi mata uang selain rupiah.
Real-Time Deep Learning and OCR for Medication Recognition in Visually Impaired Users: A Systematic Literature Review Qory Hidayati; Hendra Kusuma; Muhammad Attamimi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.112489

Abstract

Medication misidentification remains a safety risk for visually impaired and low-vision users who manage medicines independently. This systematic literature review synthesizes eligible studies on real-time medication recognition systems on real-time medication recognition systems that combine deep learning (DL), optical character recognition (OCR), and accessible feedback mechanisms. Following PRISMA 2020, searches were conducted in IEEE Xplore, Scopus, PubMed, Web of Science, ACM Digital Library, and ScienceDirect for studies published from 2019 to July 2025. Fifty studies met the eligibility criteria. Of these, 30 reported quantitative accuracy and/or latency metrics suitable for comparative extraction. The evidence was synthesized across platform type, recognition technique, evaluation dimension, multimodal integration, and deployment feasibility. Reported accuracies ranged from approximately 70% to 99%, but direct comparison remains limited by differences in datasets, number of medicine classes, image conditions, and metric definitions. Latency evidence indicates that several mobile and embedded systems support sub-second to near-real-time feedback, while cloud-assisted systems may require longer response times. Mobile, wearable, embedded, and multimodal approaches show complementary strengths, yet persistent gaps remain in public datasets, real-world validation with visually impaired users, robust multilingual OCR, transparent quality reporting, and healthcare-system integration. This review contributes a structured synthesis of DL-OCR techniques, accessibility features, and deployment constraints for assistive medication recognition.
Design of mapping system for domestic service robot using light detection and ranging Muhammad Attamimi; Felix Gunawan; Djoko Purwanto; Rudy Dikairono; Astria Nur Irfansyah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8007

Abstract

Service robots are becoming increasingly essential in offices or domestic environments, usually called domestic service robots (DSR). They must navigate and interact seamlessly with their surroundings, including humans and objects, which relies on effective mapping and localization. This study focuses on mapping, employing the light detection and ranging (LiDAR) sensor. The sensor, tested at proximity, gathers distance data to generate two-dimensional maps on a mini-PC. Additionally, it provides rotational positioning and robot odometry, broadening coverage through robot movement. A microcontroller with wireless smartphone connectivity facilitates control via Bluetooth. The robot is also equipped with ultrasonic sensors serving as a bumper. Testing in rooms of varying sizes using three methods (i.e., Hector simultaneous localization and mapping (SLAM), Google Cartographer, and real-time appearance-based mapping (RTAB-Map)) yielded good quality maps. The best F1-measure value was 96.88% achieved by Google Cartographer. All the results demonstrated the feasibility of this approach for DSR development across diverse applications.
Co-Authors Ahmad Fathur Muhtadin Aldy Helnawan Astria Nur Irfansyah Devy Kuswidiastuti Djoko Purwanto Felix Gunawan Hasby Fahrudin Hendra Kusuma Kelvin Liusiani Kusuma, Hendra Kusuma, Hendra Siswanto Mufid Naufal Baay Muhammad Rivai Muharom, Syahri Qory Hidayati Riza Agung Ronny Mardiyanto, Ronny Rudy Dikairono Ruth Johana Hutagalung Takashi Omori Takayuki Nagai Takayuki Nagai Totok Mujiono, Totok Trulien Jeremiah Elmer Yunardi, Riky Tri Zulfiansyah, Alvin Daffa Kumara