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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Mikro Tiga Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Rekayasa Sriwijaya Sinergi JURNAL NASIONAL TEKNIK ELEKTRO Proceeding of the Electrical Engineering Computer Science and Informatics Science and Technology Indonesia Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTERA (Jurnal Teknologi Rekayasa) JRST (Jurnal Riset Sains dan Teknologi) Jurnal Teknik Kimia JURNAL SURYA ENERGY Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Jurnal Pengabdian UNDIKMA MDP Student Conference Jurnal Rekayasa elektrika SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Jurnal Akademika Baiturrahim Jambi Jurnal Elektronika dan Telekomunikasi
Suci Dwijayanti
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Motion Control of 5-Degree of Freedom Humanoid Robot Arm System Using Fuzzy Logic Algorithm Ike Bayusari; Darma Sandi; Rahmawati Rahmawati; Suci Dwijayanti; Bhakti Yudho Suprapto
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 1 (2024): Jurnal Ecotipe, April 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i1.4482

Abstract

Humanoid robots are the evidence of the rapid advancement of technology in robotics. One of their applications is replacing humans in certain tasks, such as moving goods. For this type of activity, humanoid robots need arms; however, their arm system is not effective enough because of the material used. Thus, this study proposes the use of filament as the material of frames in robot arms. A 5-degree of freedom (DoF) robot arm system was implemented, and the motor worked as the driving force. The movement of this robotic arm was based on proximity and camera sensor readings. Then, the movement control used fuzzy logic with the Sugeno method. During experimental testing, the humanoid robot arm could grip and move objects from one place to another at varying times according to the object type. The length of time obtained depends on the reading of the proximity sensor on the gripper. In another experiment, the humanoid robot arm could shake hands with humans in real time within 36 sec. In conclusion, the results verified the effectiveness of the proposed fuzzy logic controller with the Sugeno method.
Comparative study of CNN techniques for tuberculosis detection using chest X-ray images from Indonesia Dwijayanti, Suci; Agam, Regan; Suprapto, Bhakti Yudho
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.018

Abstract

Convolutional neural networks (CNNs) represent a popular deep-learning approach for image classification tasks. They have been extensively employed in studies aimed at classifying tuberculosis (TB), coronavirus disease 2019 (COVID-19), and normal conditions on chest X-ray images. However, there is limited research utilizing Indonesian data, and the integration of CNN models into user-friendly interfaces accessible to healthcare professionals remains uncommon. This study addresses these gaps by employing three CNN architectures—AlexNet, LeNet, and a modified model—to classify TB, COVID-19, and normal condition images. Training data were sourced from both a local hospital in Indonesia (RSUP dr. Rivai Abdullah) and an additional online dataset. Results indicate that AlexNet achieved the highest accuracy, with rates of 97.52%, 64.45%, and 92.43% on the Kaggle dataset, the RSUP Dr. Rivai Abdullah dataset, and the combined dataset, respectively. Subsequently, this model was integrated into a user interface and deployed for testing using new data from the RSUP Dr. Rivai Abdullah dataset. The web-based interface, powered by the Gradio library, successfully detected 7 out of 10 new cases with 70% accuracy. This implementation may enable medical professionals to make preliminary diagnoses.
Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm Pohan, Immanuel Morries; Dwijayanti, Suci; Suprapto, Bhakti Yudho; Hikmarika, Hera; Hermawati, Hermawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5376

Abstract

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements.
Implementasi Teknologi Keramba Jaring Apung Otomatis Untuk Meningkatkan Budidaya Ikan Di Desa Ekowisata Burai Dwijayanti, Suci; Suprapto, Bhakti Yudho; Hikmarika, Hera; Irmawan, Irmawan; Rendyansyah, Rendyansyah; Fitria, Syarifa; Herlina, Herlina; Agustina, Sri
Suluh Abdi Vol 7, No 1 (2025): SULUH ABDI
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/sa.v7i1.9752

Abstract

Desa Burai, Ogan Ilir, merupakan desa ekowisata dengan potensi sumber daya alam berupa Sungai Kelekar. Permasalahan utama yang dihadapi masyarakat mitra Kelompok Sadar Wisata (Pokdarwis) Burai Indah adalah pengelolaan keramba jaring apung (KJA) yang masih dilakukan secara tradisional, yang mengarah pada rendahnya produktivitas ikan. Pengabdian masyarakat ini bertujuan untuk meningkatkan produktivitas budidaya ikan di Desa Burai dengan solusi teknologi yang ditawarkan adalah KJA pintar. KJA yang dikembangkan ini dilengkapi dengan teknologi pemantauan kualitas air menggunakan sensor suhu dan pH yang kemudian juga akan dilengkapi sistem pemberian pakan otomatis. Kegiatan pengabdian ini mencakup analisis situasi, pembuatan dan pemasangan KJA, serta pelatihan dan pendampingan kepada kelompok pengelola wisata air. Penerapan KJA pintar diharapkan dapat meningkatkan produktivitas ikan, mendukung keberlanjutan ekowisata, dan memberdayakan masyarakat. Selain itu, kegiatan ini juga melibatkan mahasiswa yang mendapatkan pengalaman praktis dalam bidang teknik elektro. Hasil pelaksanaan menunjukkan bahwa KJA pintar mampu meningkatkan efisiensi dalam pengelolaan budidaya ikan dan meningkatkan kapasitas masyarakat dalam memanfaatkan teknologi untuk kelestarian lingkungan.
Positioning Control System on the Movement of Wheeled Humanoid Robot Using Swerve Drive Model Based on Fuzzy Logic Controller Harry, Caroline; Amirulsyah, Ahmad Rizky; Hermawati, Harmawati; Dwijayanti, Suci; Suprapto, Bhakti Yudho
Jurnal Rekayasa Elektrika Vol 21, No 1 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i1.36393

Abstract

Technology in robotics has developed rapidly in the last few decades, as evidenced by the increasing number of robots created, such as humanoid robots and mobile robots. In this study, a wheeled humanoid robot is designed to move from one place to another using a swerve drive model, a holonomic type of drive wheel. This model uses a combination of DC motors and gears to ensure smooth movement of the humanoid robot. The swerve drive allows the robot to move freely in all directions. Therefore, the humanoid robot requires a control system to manage and automatically regulate the state of the system. The fuzzy logic control system can perform mathematical calculations based on human knowledge, serving as a controller without requiring a mathematical model of the controlled process. The results obtained from this study demonstrate the robots ability to move stably and accurately, based on the response to the rules provided by the fuzzy logic control system. The more membership functions used, the more stable and accurate the results will be, while using fewer membership functions will result in faster response times to reach the setpoint.
Optimalisasi Promosi Ekowisata Desa Burai dengan Teknologi Perahu Hybrid dan Website Suprapto, Bhakti Yudho; Dwijayanti, Suci; Juliantina, Ika
Jurnal Pengabdian UNDIKMA Vol. 6 No. 2 (2025): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v6i2.15487

Abstract

This community service activity assists tourism awareness groups Pokdarwis in developing ecotourism through marketing and production to increase income. The methods used in this community service activity are participatory and collaborative by conducting training and demonstrations. The target partners of this activity are Tourism Awareness Groups (Pokdarwis). While the evaluation instrument is a questionnaire used to assess the understanding and satisfaction of partners Pokdarwis with the training and technology provided and semi-structured interviews were conducted with Pokdarwis members and tourists who have used the service. Then, the data analysis technique is quantitative descriptive analysis, used for the results of the questionnaire by displaying percentages to show the level of understanding, satisfaction, and effectiveness of the training. While for interviews and observations, qualitative thematic analysis is used to identify patterns, challenges, and opportunities felt by partners during the program. The results of this community service show that the level of understanding of Pokdarwis is quite good, reaching 82% who understand and are able to fill in content on the website, and are also able to control hybrid boats, participant satisfaction 81% and readiness for independent implementation 74%.
A Hybrid Wavelet Scattering and Mel Spectrogram Feature with Deep Convolution Neural Network for Robust Spoken Digit Recognition irmawan, Irmawan; Dwijayanti, Suci; Suprapto, Bhakti Yudho
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1310.2025

Abstract

Spoken digit recognition (SDR) plays a critical role in biometric authentication and human–computer interaction, yet existing approaches often rely on small datasets, limited feature representations, or architectures prone to overfitting. To address these limitations, this study proposes a robust end-to-end pipeline that integrates Wavelet Time Scattering (WTS), Mel-Frequency Cepstral Coefficients (MFCC), and a 2D Deep Convolutional Neural Network (2D-CNN) to enhance the accuracy and generalization of SDR systems in realistic environments. The Free-Spoken Digit Dataset (FSDD), consisting of 3000 audio samples from speakers with diverse accents, was pre-processed using zero-padding normalization and transformed into high-resolution time–frequency spectrograms via WTS. The proposed CNN architecture, optimized through systematic experimentation on batch size and learning rate, demonstrated stable convergence and superior discriminative capability. Using a learning rate of 0.001 and a batch size of 50, the model achieved the highest performance with 99.2% accuracy, outperforming established methods including SVM, MFCC-LSTM, and Multiple RNN architectures. Comparative evaluations further revealed that the combined WTS–MFCC feature extraction significantly enhances spectral–temporal representation quality, contributing to improved classification precision across all digit classes. These findings demonstrate that the proposed WTS-MFCC-CNN framework not only advances SDR accuracy but also provides a scalable and computationally efficient approach suitable for real-world biometric, financial, and voice-controlled applications. The results highlight the potential of hybrid time–frequency representations integrated with deep architectures to set a new benchmark for robust spoken digit recognition.
Designing Human-Robot Communication in the Indonesian Language Using the Deep Bidirectional Long Short-Term Memory Algorithm Suci Dwijayanti; Ahmad Reinaldi Akbar; Bhakti Yudho Suprapto
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.595

Abstract

Humanoid robots closely resemble humans and engage in various human-like activities while responding to queries from their users, facilitating two-way communication between humans and robots. This bidirectional interaction is enabled through the integration of speech-to-text and text-to-speech systems within the robot. However, research on two-way communication systems for humanoid robots utilizing speech-to-text and text-to-speech technologies has predominantly focused on the English language. This study aims to develop a real-time two-way communication system between humans and a robot, with data collected from ten respondents, including eight males and two females. The sentences used adhere to the standard rules of the Indonesian language. The speech-to-text system employs a deep bidirectional long short-term memory algorithm, coupled with feature extraction via the Mel frequency cepstral coefficients, to convert spoken language into text. Conversely, the text-to-speech system utilizes the Python pyttsx3 module to translate text into spoken responses delivered by the robot. The results indicate that the speech-to-text model achieves a high level of accuracy under quiet-room conditions, with noise levels ranging from 57.5 to 60 dB, boasting an average word error rate (WER) of 24.99% and 25.31% for speakers within and outside the dataset, respectively. In settings with engine noise and crowds, where noise levels range from 62.4 to 86 dB, the measured WER is 36.36% and 36.96% for speakers within and outside the dataset, respectively. This study demonstrates the feasibility of implementing a two-way communication system between humans and a robot, enabling the robot to respond to various vocal inputs effectively. 
Comparative Performance of Fuzzy Logic and PID Steering Control for Improved Swerve Autonomous Vehicles Bhakti Suprapto; Suci Dwijayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7594

Abstract

This study presents a performance comparison of a fuzzy logic controller with a proportional-integral-derivative (PID) controller in an autonomous vehicle steering controller based on an improved swerve drive. The advantage of this swerve drive system is that it provides high maneuverability in tight spaces by utilizing nonlinear kinematic behavior and strong coupling between translational and rotational motions. This is a challenge for conventional control strategies. To overcome this problem, a fuzzy logic controller is used, which has the ability to work in more dynamic conditions. To support the control system for precision, a good structural design is required. The feasibility of the proposed improved swerve drive mechanical design is verified through finite element-based structural analysis to ensure that the control performance is not limited by mechanical constraints. Testing results show that the configuration of seven membership functions in the fuzzy logic controller provides the best performance, with an overshoot value of 7.33% and a steady-state error of 0.0324. Real-time testing of this electric vehicle prototype was conducted in five scenarios: straight road, 90° turn, parallel parking, obstacle avoidance, and on-the-spot maneuvering. The testing results also show that the fuzzy logic controller consistently outperforms the PID controller by reducing tracking error, minimizing overshoot, and achieving faster settling times, especially under complex motion conditions. Structural validation also confirms that this improved swerve drive, operating within the elastic limits of the material, supports the implementation of reliable control strategies.
Real-time object detection and distance measurement for humanoid robot using you only look once Suci Dwijayanti; Bhakti Yudho Suprapto; Mutiyara Mutiyara; Rendyansyah Rendyansyah
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.v13i6.7476

Abstract

Humanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4’s efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human–robot interaction capabilities via data transmission.
Co-Authors A. Wahyudin Afnizar Azmi Agam, Regan Ahmad Reinaldi Akbar Akbar, Ahmad Reinaldi Al Hafiz, Dimsyiar M. Aldi Ramadhani Aldi Ramadhani Althaf Taufiqurrahman Alvina Suryadinata Alvina Suryadinata Alvio Yunita Putri Amirulsyah, Ahmad Rizky Amri, Djulil Aulia Ghaida Bazlina Dawami Afrah Bhakti Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Capelo, Febyo Caroline Caroline Charisma Ayu Pramuditha Cundari, Lia Darma Sandi Darma Sandi Dimsyiar M Al Hafiz Djulil Amri Farhan Abie Ardandy Febby Nora Fitria, Syarifa Fortuna Sinulingga , Regita Harry, Caroline Hera Hikmarika Hera Hikmarika Hera Hikmarika, Hera Herlina Herlina Hermawati Hermawati Hermawati Hermawati Hermawati Hermawati, Hermawati Hermawati, Harmawati Ika Juliantina Ike Bayusari Irmawan Irmawan Javen Jonathan Luthfiyah A. Sayyidah Muhammad Iqbal Muhammad Iqbal Muhammad Ridho Ramadhan Muhammad Yusup Muhammad Yusup Mutiyara Mutiyara Otong, Muhammad Pohan, Immanuel Morries Rahmattullah, Muhammad Syukron Rahmawati Rahmawati Regita Umami Rendyansyah Rendyansyah Rendyansyah Retno Widowati Rudyanto Thayeb Sandi, Darma Sri Agustina, Sri Suprapto, Bhakti Yudho Thayeb, Rudyanto Wirawan Dwi Harsanto Yenny Aulya Yogta, Abeng