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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Sinergi JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Elektronika dan Telekomunikasi JURNAL SURYA ENERGY Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Jurnal Rekayasa elektrika SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Teknik Sipil LATERAL
Bhakti Yudho Suprapto
Sriwijaya University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Transportation Other

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PERBANDINGAN PENGENDALI PI, PD DAN PID PADA PENGENDALIAN KECEPATAN MOTOR INDUKSI TIGA FASA DENGAN MEMANFAATKAN SUPERVISORY CONTROL AND DATA ACQUISITION (SCADA) Sariman, Sariman; Padarid, Manlahima; Mahfie, Dindi Hamamie; Suprapto, Bhakti Yudho
JURNAL SURYA ENERGY Vol. 3 No. 2 2019
Publisher : UNIVERSITAS MUHAMMADIYAH PALEMBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jse.v3i2.1546

Abstract

Motor Induksi tiga fasa merupakan salah satu jenis motor yang paling banyak digunakan pada industri. Motor ini biasanya yang menjadi perhatian khusus adalah pengendalian kecepatan. Pengendali yang digunakan biasanya hanya pengendali Proportional Integral Derivative (PID). Namun terkadang pada proses kontrol, pengendali lain seperti Proportional Integral (PI) dan Proportional Derivative (PD) juga sering digunakan. Penelitian ini akan membandingkan pengendali-pengendali tersebut dalam proses pengendalian kecepatan pada motor induksi tiga fasa saat berbeban dan tanpa beban. Untuk memudahkan proses pengendaliannya, digunakan Supervisory Control and Data Acquisition (SCADA) yang memiliki kemampuan dalam mengakuisisi data dan monitoring. Dalam pengujiannya didapatkan bahwa pada pengendali terbaik yakni PID dimana error steady state kecil, settling time cepat dan tetap stabil meskipun terdapat perubahan beban.  Pengendali PID ini memiliki parameter KP  = 2,  Ti = 1, Td =3. Sedangkan data yang ditampilkan oleh sistem SCADA dapat dikatakan baik dan valid, dimana persentase penyimpangan untuk data kecepatan sebesar 0,1%.
Identifikasi Jalan Kampus Universitas Sriwijaya Berbasis Fully Convolutional Networks Caroline, Caroline; Yogta, Abeng; Thayeb, Rudyanto; Hermawati, Hermawati; Dwijayanti, Suci; Suprapto, Bhakti Yudho
JURNAL SURYA ENERGY Vol. 4 No. 1 2019
Publisher : UNIVERSITAS MUHAMMADIYAH PALEMBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jse.v4i1.2057

Abstract

Perkembangan mobil listrik sangat pesat seiring dengan semakin berkurangnya sumber energi fosil. Untuk mampu bergerak otomatis identifikasi dan deteksi jalan sangat diperlukan. Namun proses ini sulit dikarenakan jalan yang ada tidak memiliki garis sebagai acuan. Banyak metode yang telah digunakan salah satunya dengan menggunakan Fully Convolutional Networks (FCNs). Metode ini berhasil dalam melakukan identifikasi terhadap jalan yang ada pada kampus Universitas Sriwijaya. Berdasarkan hasil pengujian didapatkan nilai Intersection over Union (IoU) 90%. Sehingga, model yang dihasilkan oleh FCNs dapat digunakan untuk identifikasi jalan yang dilalui. Selain itu parameter lain yang diperhitungkan yaitu nilai akurasi 98,12% pada data latih dan 97,87% pada data uji. Sedangkan error yang dihasilkan sebesar 6 % pada data latih dan 7% pada data uji. Kata kunci: Fully Convolutional Networks (FCNs), Intersection over Union, Jalan kampus, Mobil ListrikABSTRACTThe development of electric cars is very rapid along with the decreasing source of fossil energy. To move automatically, the electric car is needs identification and detection of roads. But this process is difficult because the existing road does not have a line as a reference. Many methods have been used, one of them is using Fully Convolutional Networks (FCNs). This method is successful in identifying existing roads on the Sriwijaya University campus. Based on the results of testing, it obtained Intersection over Union (IoU) value of 90%. So, the model produced by FCNs can be used to identify the path traveled. In addition, other parameters taken into account are the accuracy value of 98.12% in the training data and 97.87% in the test data. While the resulting error of 6% in training data and 7% in test data.
Kontrol Attitude Unmanned Ground Vehicle (UGV) menggunakan Backpropagation Neural Network Bayusari, Ike; Suprapto, Bhakti Yudho
JURNAL SURYA ENERGY Vol. 5 No. 1 2020
Publisher : UNIVERSITAS MUHAMMADIYAH PALEMBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jse.v5i1.2712

Abstract

Unmaned Ground Vehicle (UGV) merupakan teknologi kendaraan darat tanpa awak yang berguna untuk mempermudah pekerjaan manusia dalam berbagai bidang seperti transportasi, aktivitas logistik industri, search and resque, pertahanan dan keamanan, juga beberapa bidang lainnya. Pengendalian attitude menjadi permasalahan karena membutuhkan ketelitian akibat adanya pengaruh kecepatan. Selain itu, bagaimana UGV tersebut mengikuti jalur yang ditentukan juga memerlukan pengendalian attitude yang optimal. Penelitian ini bertujuan untuk merancang dan menguji performa serta untuk mengatahui tingkat keberhasilan dan keakuratan pengendalian UGV menggunakan algoritma Backpropagaion Neural Network. Dari hasil pengujian didapatkan bahwa algoritma ini berhasil mengikuti data uji yang diberikan dengan nilai MSE yang kecil.
Desain Pengembangan Sistem Pembangkit Listrik Tenaga Gelombang Laut Berbasis Keseimbangan Gyroscope Agustina, Sri; Yusup, Muhammad; Dwijayanti, Suci; Otong, Muhammad; Suprapto, Bhakti Yudho
JURNAL SURYA ENERGY Vol 5 No. 2 2021
Publisher : UNIVERSITAS MUHAMMADIYAH PALEMBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jse.v5i2.3328

Abstract

Indonesia sebagai negara maritim yang luas memiliki potensi sumber energi terbarukan yang berasal dari laut. Dengan memanfaatkan gelombang laut sebagai energi, pembangkit listrik tenaga gelombang laut (PLTGL) dapat menghasilkan tegangan listrik yang cukup untuk memberikan suplai ke peralatan listrik. Selama ini gelombang merupakan permasalahan karena sangat tergantung pada besar kecilnya angin sehingga mempengaruhi tenaga listrik yang dihasilkan. Oleh karena itu pada penelitian ini dikembangkan Sistem Pembangkit Listrik Tenaga Gelombang Laut yang berbasis keseimbangan gyroscope. Pada artikel ini fokus pembahasan pada pengaruh gerakan rotasi gimbal terhadap power take off (PTO) generator. Pergerakan dari flywheel yang berputar akan memberikan momentum kepada gimbal untuk dapat berotasi. Berdasarkan simulasi numerik interaksi antara gimbal dan PTO generator akan menghasilkan tegangan listrik. Hasil yang didapatkan dari percobaan akan mendukung analisa teoritis dan simulasi, serta sebagai acuan untuk membuat desain PLTGL yang memiliki pengendali berkinerja tinggi.
Real-time object detection and distance measurement for humanoid robot using you only look once Dwijayanti, Suci; Suprapto, Bhakti Yudho; 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.
Designing Human-Robot Communication in the Indonesian Language Using the Deep Bidirectional Long Short-Term Memory Algorithm Dwijayanti, Suci; Akbar, Ahmad Reinaldi; Suprapto, Bhakti Yudho
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. 
Implementasi greenhouse untuk mendukung agropark di Desa Tanjung Pinang II Kecamatan Tanjung Batu Kabupaten Ogan Ilir Dwijayanti, Suci; Suprapto, Bhakti Yudho; Hermawati, Hermawati; Hikmarika, Hera; Rendyansyah, Rendyansyah
Jurnal Komunitas : Jurnal Pengabdian kepada Masyarakat Vol 6, No 2: Januari 2024
Publisher : Institut Ilmu Sosial dan Manajemen Stiami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31334/jks.v6i2.3535

Abstract

Tanjung Pinang II Village is one of the districts in Tanjung Batu, Ogan Ilir Regency, South Sumatra, which is currently developing its potential to become a tourist village through an agro-park. However, the process of transforming the village into a tourism and educational area for the community has not yet implemented technology. Therefore, in this community service, a smart greenhouse was developed and implemented in Tanjung Pinang II Village. The methods used in this community service included situational analysis, greenhouse construction, counseling and training, as well as evaluation, analysis, and conclusion. The constructed greenhouse is equipped with temperature and humidity sensors to measure the conditions inside the room. The greenhouse is built with a steel frame and uses UV plastic for its roof and walls. The results of the community service showed that the village residents and the community benefited from the greenhouse in the process of developing Tanjung Pinang II Village as an ecotourism area.
Speed Control of An Autonomous Electric Vehicle Using Fuzzy Logic With Computer Vision-Based Input Fortuna Sinulingga , Regita; Suprapto, Bhakti Yudho; Dimsyiar M Al Hafiz; Farhan Abie Ardandy; Javen Jonathan; Dwijayanti, Suci
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 1: March 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n1.1165.2025

Abstract

A robust speed control mechanism ensures safety in an autonomous electric vehicle system. Such a system must dynamically adjust the vehicle's speed based on its surrounding environment. This research employs computer vision for object and road detection to measure the distance between the car and nearby objects. Fuzzy logic methods—specifically Mamdani and Sugeno—are utilized to automatically and stably regulate the speed of autonomous electric vehicles from their starting point to their destination. The control system considers various road conditions, including left-slanting, straight, and right-slanting roads, and the real-time presence or absence of objects. Testing is conducted across three real-world scenarios using distance and steering angle inputs. The servo angle represents the output, which ranges from 0 to 1800 and corresponds to the vehicle's speed. The results indicate that the Mamdani method provides greater speed control accuracy than the Sugeno method, which relies on a singleton output. For conditions involving left-slanting, straight, and right-slanting roads with objects within a 10-meter range, the Mamdani method produced outputs of 1370, 1800, and 1370, respectively, aligning well with predefined speed control rules. In contrast, the Sugeno method yielded 880, 1470, and 650 outputs for the same conditions, which did not adhere to the predefined rules for slow, medium, and fast speeds. In conclusion, the Mamdani method demonstrates superior accuracy and suitability for speed control in autonomous electric vehicles compared to 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.
Co-Authors Agam, Regan Agustina, Resti Ahmad Zarkasi Akbar, Ahmad Reinaldi Akhirini, Akhirini Amirulsyah, Ahmad Rizky Bochori Bochori Deris Stiawan Dimsyiar M Al Hafiz Farhan Abie Ardandy Fitria, Syarifa Fortuna Sinulingga , Regita Harry, Caroline Hera Hikmarika, Hera Herlina Herlina Hermawati Hermawati, Hermawati Hermawati, Harmawati Huda Ubaya Ika Juliantina Ike Bayusari Irmawan Irmawan Javen Jonathan Kurniati, Rizki Mahfie, Dindi Hamamie Muhammad Yusup Mutiyara, Mutiyara Natassia, Karina Otong, Muhammad Padarid, Manlahima Pohan, Immanuel Morries Rendyansyah Sariman Sariman Siti Nurmaini Sri Agustina, Sri Suci Dwijayanti Thayeb, Rudyanto Yogta, Abeng