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All Journal ComEngApp : Computer Engineering and Applications Journal JTEV (Jurnal Teknik Elektro dan Vokasional Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Emitor: Jurnal Teknik Elektro Annual Research Seminar JURNAL NASIONAL TEKNIK ELEKTRO Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jusikom : Jurnal Sistem Komputer Musirawas Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology and Computer Engineering KOMPUTIKA - Jurnal Sistem Komputer GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Terapan Jurnal Teknologi Elekterika Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) J-Innovation Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Rekayasa elektrika Nuansa Informatika ABDIKOM Emitor: Jurnal Teknik Elektro
Kemahyanto Exaudi
Fakultas Ilmu Komputer Universitas Sriwijaya
Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Assistance in Designing Brand Guidelines to Develop a Professional Identity for Young Generation B. Azhar, Iman Saladin; Sari, Winda Kurnia; Exaudi, Kemahyanto; Prasetyo, Aditya Putra Perdana; Putra, Pacu; Firnando, Ricy
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 7, No 1 (2026): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v7i1.30-39

Abstract

This community service programme aims to support the youth participants of Rumah Cahaya Indonesia (RCI) Palembang in designing a comprehensive Brand Guideline to strengthen their professional identity. The main problem identified is the inconsistency of visual identity and the limited understanding of branding principles among the participants. To address this issue, the programme implements workshops and technical mentoring sessions focusing on fundamental branding concepts, the structure and components of a Brand Guideline, and the importance of maintaining consistent visual communication. The activities include preparation, development of learning materials, workshop implementation, hands-on practice, and evaluation. Participants are trained to use Adobe Illustrator and Adobe After Effects to produce visual and motion-based brand assets. The programme received positive feedback, indicating an improvement in participants’ understanding and skills in branding. The results show that this initiative contributes to enhancing digital creative literacy and supports young people in developing a more structured and professional brand identity.
Implementation of Fisherface Algorithm for Eye and Mouth Recognition in Face-Tracking Mobile Robot Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Ades Harafi Duri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29266

Abstract

Facial recognition is an artificial intelligence algorithm that distinguishes one face from another by capturing facial patterns visually. This recognition specifically detects and identifies individuals based on facial features by scanning the entire face. Several methods are used for facial detection, including facial landmarks points, Local Binary Patterns Histograms (LBPH), and Fisherface. In the context of this research, Fisherface is used to reduce the dimensionality of facial space in order to obtain image features. The method is insensitive to changes in expression and lighting, leading to better pattern classification and making it suitable for implementation on mobile devices such as robot vision. Therefore, this research aimed to measure the response time speed and accuracy level of pattern recognition when implemented on mobile robot devices. The results obtained from the accuracy testing showed that the highest accuracy for face detection process was 90%, while the lowest was 78.3%. In addition, the average execution time (AET) for the fastest process was 1.63 seconds and the slowest was 1.72 seconds. For pattern recognition, the statistics showed 90% accuracy, 100% precision, 81.81% recall, and F-1 score of 89.5%. Meanwhile, the longest execution time was 0.084 seconds and the fastest was 0.064 seconds. In face tracking process, the mobile robot movement was based on real-time pixel sizes, determining x and y values to produce the center of face region.
TinyML-Based Stress Detection Using Time-Domain HRV Features and a Lightweight DNN on ESP32: Deteksi Stres Berbasis TinyML Menggunakan Fitur HRV Domain Waktu dan DNN Ringan pada ESP32 Sarmayanta Sembiring; Kemahyanto Exaudi; Abdurahman -; Jorena; Hadir Kaban; M. Buffon Prima; Rahmat Fadli Isnanto
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.624

Abstract

Stress is a psychophysiological condition that requires continuous and objective monitoring. However, existing wearable stress detection systems often rely on cloud-based processing or computationally intensive algorithms, limiting their applicability for real-time inference on resource-constrained embedded devices. This study presents a TinyML-based framework for real-time stress detection using the MAX30102 sensor and an ESP32 microcontroller. The proposed framework integrates four time-domain Heart Rate Variability (HRV) features (BPM, SDNN, RMSSD, and pNN50), a lightweight Deep Neural Network (DNN), full INT8 TensorFlow Lite quantization, and on-device inference to enable efficient edge-based stress classification. The DNN model was trained and evaluated using the WESAD dataset. Experimental results showed that a decision threshold of 0.70 yielded the best classification performance, achieving an accuracy of 82% and an F1-score of 0.63 for the stress class. The quantized TensorFlow Lite INT8 model preserved 100% prediction compatibility between the Python and ESP32 implementations. Furthermore, the MAX30102 sensor achieved a BPM measurement accuracy of 98.74%, while the HRV feature extraction implemented on the ESP32 produced results consistent with the reference calculations. These findings demonstrate that the proposed end-to-end TinyML framework enables accurate and computationally efficient HRV-based stress detection on resource-constrained microcontrollers, providing a practical foundation for real-time wearable edge-health monitoring
Performance Evaluation of a Cloud-Integrated IoT Hydroponic System Using Firebase Realtime Database and Netlify Web Dashboard Ricy Firnando; Prita Salma; Dwi Aurelia Rahmadani; Kemahyanto Exaudi; Rahmat Fadli Isnanto; Andre Hardoni
Journal of Electrical, Electronic, Information, and Communication Technology Vol 8, No 1 (2026): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.8.1.118597

Abstract

Smart hydroponic monitoring often relies on third-party mobile applications and faces hardware limitations regarding analog pins on microcontrollers when acquiring data from multiple sensors. Furthermore, technical evaluations regarding the stability of data transmission to the cloud and the response speed of simultaneous sensor readings are rarely discussed. This study aims to evaluate the functionality and responsiveness of hardware integration with the cloud in a hydroponic smart showcase prototype. The NodeMCU ESP8266 microcontroller is used as the central processing unit. To overcome the analog pin limitation for water quality sensors (pH and TDS), the system is integrated with a CD4051BE IC multiplexer. Environmental and nutritional data are transmitted using the API protocol to the Firebase Realtime Database and visualized through a Netlify-hosted web dashboard interface, eliminating mobile application dependency. The high-speed multiplexing mechanism with a 40 ms recording interval was tested over 30 repeated trials per buffer solution. The transient response test recorded a mean error of 0.58 % (SD = 0.27 %) for pH 4.01 solution and 1.21 % (SD = 0.52 %) for pH 6.86 solution. Furthermore, a 10‑hour network stability test (100 samples per sensor) proved that logging transmission to the database ran persistently without packet loss, with average environmental reading errors below 1 % and Wi‑Fi RSSI fluctuations between –45 and –62 dBm. This system demonstrates that a cloud‑based pure web architecture can provide more independent, responsive, and stable IoT control monitoring.
Co-Authors Abdul Wahid Sempurna Abdul Wahid Sempurna Abdul Wahid Sempurna Abdurahman Abdurahman Abdurahman - Ades Harafi Duri Adi Hermansyah, Adi Aditya P.P. Prasetyo Aditya PPP Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Alif Almuqsit Almuqsit, Alif andre hardoni B Azhar, Iman Saladin B. Azhar, Iman Saladin Bagus Prasetyo Bangun Sudrajat Bangun Sudrajat Barzan Trio Putra Brema Alfaretz Tarigan Buchari, M. Ali Dedy Kurniawan Deris Stiawan Desyandri Desyandri Dody Firmansyah Dudifa, Aldi Dwi Aurelia Rahmadani Fadli Isnanto, Rahmat Fakhrurroja, Hanif Fatimah, Sayyidatina Fitriyanto, Megi Hadir Kaban Hanifah, Izzati Millah Huda Ubaya Huda Ubaya Huda Ubaya Ichsan Mahjud Jorena Jorena Jorena M. Buffon Prima M. Dimas Firmansyah Mileandira, Leviarta Monica Ayu Amaria Muhammad Ajran Saputra Muhammad Furqon Rabbani Nabillah Selva Setiawan Nadhira, Wardha Osvari Arsalan P.P Prasetyo, Aditya Pacu Putra Perdani, Tharisa Antya Pingki Pingki Prasetiyo, Bagus Prasetyo, Aditya P P Prasetyo, Aditya P.P. Prasetyo, Aditya PP Prasetyo, Aditya Putra Perdana Prita Salma Purwita Sari Purwita Sari Purwita Sari Purwita Sari, Purwita Purwoko, Agus Putra Perdana Prasetyo, Aditya R Rendyansyah Rahmad Fadli Isnanto Rahmat Budiarto Rahmat Fadli Isnanto Rahmat Fadli Isnanto Rahmatullah, Ikang Rendyansyah Rendyansyah Rendyansyah Rendyansyah Rendyansyah Ricy Firnando Ricy Firnando Rido Zulfahmi Riyuda, Rafki Sahasika Romadhona, Londa Arrahmando Rony, Zahara Tussoleha Rossi Passarella Roswitha Yemima Tiur Mediswati Sari, Komang Mita Sarmayanta Sembiring Sarmayanta Sembiring Sarmayanta Sembiring Sayyidatina Fatimah Sri Desy Siswanti Sri Desy Siswanti Sry Desy Siswanti Sukemi Sutarno Sutarno Sutarno Sutarno Tri Wanda Septian Tri Wanda Septian, Tri Wanda Vindriani, Marsella Wahyu Gunawan Winda Kurnia Sari