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All Journal International Journal of Electrical and Computer Engineering Jurnal Sains dan Teknologi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JTERA (Jurnal Teknologi Rekayasa) CYBERNETICS Jurnal Sistem Cerdas Jurnal Teknologi Terapan JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Industrial Research Workshop and National Seminar Research in Education, Technology, and Multiculture Scientechno: Journal of Science and Technology
Susetyo Bagas Bhaskoro
Politeknik Manufaktur Bandung
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Sistem Antropometri Lingkar Kepala Manusia berbasis Machine Vision Susetyo Bagas Bhaskoro; Sandy Bhawana Mulia; Afiq Hasydhiqi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.20175

Abstract

This study aims to develop an automated anthropometric system based on machine vision, integrated into a medical cyber-physical system (MCPS), to measure human head circumference. Head circumference is a critical parameter in growth monitoring, particularly for detecting abnormalities such as microcephaly and macrocephaly, which can affect cognitive development and overall health. To address this challenge, the study proposed an anthropometric system that enabled automated, accurate, and contactless measurements, accessible in real-time by healthcare professionals. The system was designed using a machine vision approach, incorporating object detection technology and elliptical model-based perimeter estimation to determine head circumference noninvasively. A 1,920 × 1,080-pixel (1080p) camera operating at 30 fps with a 60° field of view was mounted on a three-axis motion mechanism driven by stepper motors to automatically capture frontal and side views of the head. The measurement process began with head detection and bounding box adjustment to obtain head width parameters. Euclidean distance was used for measurement, followed by elliptical geometry modeling to estimate head circumference. Experimental results showed the lowest error rate of 2.29% at a distance of 50 cm under 300 lux lighting conditions. Performance evaluation using a confusion matrix yielded an accuracy of 92.8%, precision of 100%, recall of 97.5%, and F score of 98.7%. The proposed system provides an effective solution for healthcare professionals to perform growth screening quickly, accurately, and safely. It also supports remote healthcare services, particularly in areas with limited access to medical facilities.
Manajemen Pengondisian Suhu Ruangan Berdasarkan Beban Termal Menggunakan Sensor Thermopile Infrared Array Nuryanti; Bhaskoro, Susetyo Bagas; Erliansyah, Muhammad Firza
JST (Jurnal Sains dan Teknologi) Vol. 13 No. 2 (2024): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v13i2.83996

Abstract

Upaya mencapai efisiensi energi, perlu pengurangan penggunaan AC yang tidak efisien, mengakibatkan konsumsi energi tinggi dan emisi karbon. Beban termal adalah total panas yang harus dikeluarkan dari ruangan untuk mempertahankan suhu nyaman, termasuk beban kalor sensibel dan laten. Tujuan penelitian ini untuk menganalisis manajemen pengondisian suhu ruangan berdasarkan beban termal menggunakan sensor thermopile infrared array. Jenis penelitian merupakan studi literatur. Sistem otomatisasi berbasis kamera termal AMG8833 dengan resolusi 8x8 piksel dikembangkan untuk mendeteksi kondisi ruangan secara efisien. ESP32 digunakan sebagai mikrokontroler untuk mengolah data dari kamera termal dan mengubah set point suhu ruangan menggunakan IR Transmitter. Pengaturan suhu dilakukan dengan mengatur fan AC berdasarkan hukum konveksi, sehingga distribusi udara lebih efektif. Sistem ini dilengkapi antarmuka dan LCD untuk pemantauan real-time serta pengubahan suhu langsung. Pengujian menunjukkan bahwa sistem berhasil mengoptimalkan suhu dan kenyamanan termal dengan penghematan daya 0.365 kWh. Sistem ini tidak hanya mengurangi konsumsi daya dan emisi karbon, tetapi juga meningkatkan kenyamanan dan produktivitas penghuni ruangan.
A Machine Vision–Based Automated Wheel Leak Detection System Using Real-Time Object Detection in the Water Leak Testing Process Susetyo Bagas Bhaskoro; Sarosa Castrena Abadi; Aris Budiyarto; Inkreswari Retno Hardini; M. Pribadi Lukman
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.637

Abstract

Water leak testing in automotive wheel manufacturing has traditionally relied on manual visual inspection of bubble formation, introducing subjectivity and limiting repeatability in quality assurance processes. This study developed and experimentally validated a real-time leak detection system based on machine vision, directly integrated with an industrial water leak tester platform. A dataset comprising 686 annotated images was constructed from recorded operational testing sequences and partitioned into 80% training and 20% validation subsets. The network was trained for 150 epochs and deployed within an integrated framework incorporating temporal decision logic and automated event logging to ensure deterministic classification under continuous video streaming. Experimental validation was conducted across five scenarios (A–E), including high-leak, low-leak, no-leak, and in-situ operational testing conditions, totaling 100 trials. The aggregated confusion matrix yielded 60 true positives and 40 true negatives with zero false positives and false negatives, resulting in accuracy, sensitivity, specificity, precision, and F1-score values of 1.0 within the evaluated domain. Receiver operating characteristic and precision–recall analyses confirmed strong class separability and stable decision boundaries. Although the results demonstrated high discriminative performance under controlled and operational settings, further large-scale validation under heterogeneous industrial environments is required to fully assess long-term robustness. The proposed framework provided an automated, objective, and real-time inspection solution aligned with Industry 4.0 principles for intelligent manufacturing systems.
A Bibliometric Mapping of Artificial Intelligence Research in Digital Public Service (2020–2025) Inkreswari Retno Hardini; Susetyo Bagas Bhaskoro; Tivani Shakilla Ervi; Meta Bara Berutu
Research in Education, Technology, and Multiculture Vol 5, No 2 (2026): Research in Education, Technology, and Multiculture
Publisher : Institute of Multidisciplinary Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61436/rietm/v5i2.pp141-159

Abstract

AI ceases to be a matter of discussion as a technological innovation, but also of how governments redesign the provision of public services, deal with information, and react to the more intricate needs of society. Research in the field of AI and digital public service has been accelerating rapidly over the past few years, particularly amid the accelerated pace of digital transformation during the COVID-19 period and beyond. However, the discourse is still dispersed across different fields and research issues, and thus, it is difficult to follow the development of the field as a system. The aim of this paper is therefore to conduct a review of the development and research frontier of AI in Digital Public Service during the period between 2020 and 2025 through a bibliometric approach. The study used bibliographic data acquired via the Scopus database. Data were collected using a structured search strategy that combines Boolean operators and keywords related to artificial intelligence and digital public service. A total of 662 publications were retrieved and visualized using VOSviewer. After the use of a minimum occurrence of five, 216 keywords were chosen and grouped into six thematic clusters. The results show a definite upward trend in the number of publications after 2022, indicating greater academic interest in AI-based governance and digital transformation in the state sector. Several prevailing themes emerged, including machine learning, digital government, public administration, natural language processing, information management, and AI governance. Overall, the findings suggest that the study of AI in digital public service has moved beyond the argument about fundamental digitalization to address significant questions about governance, public value, and the responsible use of AI. Keywords: Artificial intelligence, Digital public service, Digital government, Bibliometric analysis, Smart governance.
Sistem Identifikasi Manusia Bergerak Jatuh Berdasarkan Ekstraksi Suara dan Citra Susetyo Bagas Bhaskoro; Eugenia Angela Salsabillah; Afaf Fadhil Rifa'i
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 4 No 2 (2022): Volume: 4 | Nomor: 2 | Oktober 2022
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v4i2.94

Abstract

Falls are a major health problem around the world, especially in the world of healthcare because patient falls are the top worst problem that continues to occur. Most patients who fall out of bed are not witnessed. This is exacerbated by the various health problems that can result from falling. Remaining on the floor after a fall can cause trauma, serious injury and even death. Therefore, a fall detection system is needed so that people who fall can be given immediate help before they cause serious health problems. So in this study, we will create a fall identification system based on sound and image using the MFCC (Mel-Frequency Cepstrum Coefficients) method for sound extraction and LVQ (Learning Vector Quantization) for classification. Image processing using CNN (Convolutional Neural Network) method. In this system, both do not work together, but image processing works after sound processing. The system is able to detect falls with an overall accuracy of 93.3% for 15 times of sound and image processing tests.
Sistem Identifikasi Jumlah Produk Berbasis Pengolahan Citra dengan Algoritma YOLO pada Proses Pengepakan Industri Manufaktur Susetyo Bagas Bhaskoro; Hadi Supriyanto; Syamsul Falah
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 6 No 1 (2024): Volume: 6 | Nomor: 1 | April 2024
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v6i1.114

Abstract

Machine vision is a technology commonly used in modern industry for image-based autonomous analysis and inspection. Machine vision helps the process of product analysis and inspection in the industry faster than manual analysis and inspection. This study applies machine vision to an image processing-based product number identification system in the manufacturing industry packing process using the YOLOv4 algorithm and evaluation of the confusion matrix system. The results of the identification are stored in a database and displayed on the website to facilitate the monitoring process. This system has carried out several tests, especially testing the main function of the system, namely product calculations, carried out 10x experiments. Then, testing variations in light intensity with a range of 20 – 225 lux and variations in height with a range of 48 – 68 cm with 10 trials each. From the tests that have been carried out, an evaluation of the confusion matrix is ​​applied and produces an accuracy and precision of 100% and an error of 0%. The average computing speed of this system is 6.95 FPS with the help of CUDA.
Sistem Otomatisasi Pokayoke Kanban Cek di PT Denso Indonesia Agus Setiawan Setiawan; Susetyo Bagas Bhaskoro; Abdur Rohman Harits Martawireja
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 5 No 1 (2023): Volume: 5 | Nomor: 1 | April 2023
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v5i1.118

Abstract

Pada Bulan Juni 2020 terdapat claim di customer TMMIN (PT Toyota Motor Manufakturing Indonesia) untuk produk horn assy. Claim-nya adalah part number pada kartu identitas customer berbeda dengan part number horn assy. Oleh karena itu dibuatlah otomatisasi sistem pokayoke kanban cek dengan QR code scanner sebagai media indikator untuk mendeteksi kanban salah (campur) untuk memudahkan pekerjaan pengecekan kanban oleh foreman dan inspektor menggunakan visual basic, arduino, dan konveyor, dimana data dari kanban akan terekam dalam database di komputer. Otomatisasi sistem pokayoke pengecekan kanban terintegrasi di lokasi foreman dan inspektor, berhasil menurunkan rasio produk NG (Not Good) dari pengecekan manual, pada area foreman dari 3.3% menjadi 0% dan pada area inspektor dari 1.7% menjadi 0%.
Deteksi dan Interpretasi Tulisan Tangan Bahasa Indonesia melalui Pemrosesan Citra dan Optical Character Recognition (OCR) Susetyo Bagas Bhaskoro; Rizqi Aji Pratama; Shafa Aulia Hazim Darmawan
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 7 No 1 (2025): Volume: 7 | Nomor: 1 | April 2025
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v7i1.193

Abstract

This research develops a handwriting recognition system using OCR based on DB_Resnet and CRNN_VGG16 pre-training architecture integrated with hardware such as Arduino Uno, stepper motor, infrared sensor, camera, and aluminum frame. The system is equipped with a web-based interface developed using Flask. The test shows an average text recognition accuracy of 51.70% with validation using the KBBI dataset. The results show the successful integration of OCR technology with hardware and software, which is expected to increase the efficiency of handwriting data processing.
IOT-ENABLED CYBER-PHYSICAL ASSEMBLY SYSTEM FOR SEAL DETECTION IN ELECTRIC VEHICLE CHARGING CONNECTORS Yuliadi Erdani; Susetyo Bagas Bhaskoro; Ahmad Fahrurozi
Scientechno: Journal of Science and Technology Vol. 5 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i3.3988

Abstract

The reliability of electric vehicle charging connectors depends on correct seal installation, since the seal protects the interface from dust and moisture. This study developed an IoT-enabled cyber-physical assembly system integrating fiber-optic and photoelectric sensors, a programmable logic controller, pneumatic actuation, a Raspberry Pi edge gateway, and a human-machine interface. The system was evaluated on an industrial production line using the same connector model, machine, operating procedure, and inspection criteria as the baseline. The baseline rejection rate, drawn from routine production records before implementation, was 19.18%. After calibration, 600 consecutive connectors were assembled during a continuous five-hour validation run, with manual inspection by experienced quality personnel serving as the reference standard. The post-implementation rejection rate fell to 2.80%, an 85.41% relative reduction (16.38 percentage points), yielding a production yield of 97.17%. The detection system achieved 99.50% accuracy, 88.89% precision, 94.12% recall, 99.66% specificity, and a 91.43% F1-score. Average cycle time was 30 seconds per unit, with a repeatability error of ±0.10 mm across 30 repeated press-depth measurements. These results indicate improved defect prevention, assembly consistency, and traceability under the evaluated conditions, with remaining defects mostly outside the seal-sensing scope.
Co-Authors Abdur Rohman Harits Martawireja Abyanuddin Salam Adhitya Sumardi Sunarya Afaf Fadhil Rifa'i Afiq Hasydhiqi Agus Setiawan Setiawan Ahmad Fahrurozi Akbar, Saiful ANANDHA A. ALAMSYAH, ANANDHA A. Aris Budiyarto Atika Nur Rahmawati AZIZ, MUHAMMAD AZHAR ABDUL Berlian Bayu Aji Budi Pamungkas Dewi Nur Fauzia Ega Mardoyo Ega Mardoyo Erliansyah, Muhammad Firza Eugenia Angela Salsabillah Fitria Suryatini Hadi Supriyanto Inkreswari Retno Hardini Inkreswari Retno Hardini Inkreswari Retno Hardini IRNA ARIANI, IRNA M. Pribadi Lukman Maulana Hakim Swistiawan MELITA, RAHMI AGUS Meta Bara Berutu Muharman Lubis MULJONO, PUTRI MUTHIA SARASWATI Nijam, E. Nashrul Nuryanti Nuryanti Nuryanti Pancono, Suharyadi Pipit Anggraeni, Pipit Ridwan Ridwan Rifa'i, Afaf Fadhil Rizqi Aji Pratama Sandy Bhawana Mulia Sarosa Castrena Abadi Shafa Aulia Hazim Darmawan Siti Aminah SUBEKTI, RUMINTO Suhono Harso Supangkat Syamsul Falah Tivani Shakilla Ervi Yuliadi Erdani, Yuliadi