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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Matematika dan Ilmu Pengetahuan Alam Jurnal Biosains Pascasarjana Journal Of Vocational Health Studies Jurnal Matematika Sains dan Teknologi Communications in Science and Technology Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Magister Pendidikan IPA Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Khusnul Ain
Departemen Fisika, Fakultas Sains dan Teknologi, Unair
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Real-time military person detection and classification system using deep metric learning with electrostatic loss Suprayitno, Suprayitno; Fauzi, Willy Achmat; Ain, Khusnul; Yasin, Moh.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4284

Abstract

This study addressed a system design to detect the presence of military personnel (combatants or non-combatants) and civilians in real-time using the convolutional neural network (CNN) and a new loss function called electrostatic loss. The basis of the proposed electrostatic loss is the triplet loss algorithm. Triplet loss’ input is a triplet image consisting of an anchor image (xa), a positive image (xp), and a negative image (xn). In triplet loss, xn will be moved away from xa but not far from both xa and xp. It is possible to create clusters where the intra-class distance becomes large and does not determine the magnitude and direction of xn displacement. As a result, the convergence condition is more difficult to achieve. Meanwhile, in electrostatic loss, some of these problems are solved by approaching the electrostatic force on charged particles as described in Coulomb's law. With the inception ResNet-v2 128-dimensional vectors network within electrostatic loss, the system was able to produce accuracy values of 0.994681, mean average precision (mAP) of 0.994385, R-precision of 0.992908, adjusted mutual information (AMI) of 0.964917, and normalized mutual information (NMI) of 0.965031.
Online PID-neural network for tracking lower limb rehabilitation exoskeleton angular position Hanifah, Ummi; Adinda, Aura; Rahmatillah, Akif; Sapuan, Imam; Ain, Khusnul; Septanto, Harry; Chai, Rifai
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9395

Abstract

Gait trajectory tracking control is an essential component of a lower limb rehabilitation exoskeleton (LLRE). Meanwhile, the proportional-integral-derivative (PID) controller remains popular for a variety of applications, including LLRE. Nonetheless, employing PID presents a significant issue, namely determining how to choose or tune the parameters. This paper addresses the LLRE’s hipknee angular position tracking system based on an online PID-NN controller, i.e., a PID controller, whose parameters are online modified by a trained neural network (NN). A proposed framework for designing the PID-NN controller is elaborated. Numerical verifications are carried out by comparing the performance of the PID-based control system, whose parameters have been tuned using Ziegler-Nichols (ZN), without and using NN. Performance comparisons involving the presence of external disturbance are also carried out. The simulation results show that the proposed PID-NN-based control system provides better performance with lower mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) values.
Co-Authors Adinda, Aura Aisya, Rohadatul Alhaq, Elmira Rofida Alma, Ahmad Faizin Amma, Fadli Amun Amri Arfensia, Danny Sanjaya Arifianto, Deni Arifianto, Deny Assylzhan, Mazhibayev Chai, Rifai Chandra Satria Arisgraha, Franky Dayyana, Nimas Rokhmatik Deddy Kurniadi, Deddy Dwi Setyo Pambudi, Yoyok Elmira, Kambarova Endah Purwanti Fauzi, Willy Achmat Firdhausi Wardhani, Inten Fitriyatul Qulub Hendri, Yola Bertilsya Imam Sapuan Ittaqilah, Sayyidul Istighfar Jumbri, Khairulazhar Kartikasari, Amilia Moh Yasin Nur Rahma, Osmalina Osmalina Nur Rahma Permatasari, Perwira Annissa Dyah Pramono Pramudita, Alfian Prihartini Widiyanti Rahmatillah, Akif Riries Rulaningtyas Rohman, Muchammad Nurur Salwa, Umaimah Mitssalia Umi Samatha, Syifa Candiki Septanto, Harry Siswanto Solly Aryza Sosialita, Tiara Dyah Suhariningsih SUNARNO Suprayitno Suprayitno Suryani Dyah Astuti Susilo, Yunus Tao Jiang, Zhong Ummi Hanifah Varyan, Richa Wibowo, R Arif WINARNO Yang, Chun-Chen Yaqubi, Ahmad Khalil Yonatan Yunardi, Riky Tri