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All Journal TEKNIK Jurnal Pendidikan Teknologi dan Kejuruan Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JST ( Jurnal Sains Terapan ) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Jurnal Teknik Mesin dan Mekatronika (Journal of Mechanical Engineering and Mechatronics) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
Hary Oktavianto
Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS) Kisron Kisron; Bima Sena Bayu Dewantara; Hary Oktavianto
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1282.865 KB) | DOI: 10.17529/jre.v17i2.20805

Abstract

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.
Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS) Kisron Kisron; Bima Sena Bayu Dewantara; Hary Oktavianto
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i2.20805

Abstract

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.
PENGENDALIAN MOTOR INDUKSI 3 FASA DENGAN BEBAN DINAMIS KONTROL PID FUZZY MENGGUNAKAN METODE FOC-TAK LANGSUNG (INDIRECT FIELD ORIENTED CONTROL) PADA LABVIEW Hendra, R. Oktav Yama; Purwanto, Era; Oktavianto, Hary; Muntashir, Abdillah Aziz; Setiawan Suda, Kadek Reda
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 19 No. 1 (2022): Edisi Januari 2022
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (836.275 KB) | DOI: 10.23887/jptk-undiksha.v19i1.45351

Abstract

This research includes the design of a PID speed control simulation combined with Fuzzy Logic Control as a control, and increasing the speed performance of an induction motor in LabView. The control method used is a vector control induction motor, namely Field Oriented Control. This method can set up an induction motor as easily as a separate amplifier DC motor. Fuzzy Logic Control with its advantages acts as a scheduler for the PID value with the advantage of increasing the dynamic performance of the induction motor against changes in load and speed changes. From several simulations carried out on LabView with 5nm and 9nm dynamic loads using the FOC method, the average risetime result is 80% fast. When testing the dynamic load control performance, the results of the PID-Fuzzy method are better than conventional PID, especially at high motor speeds and nominal loads. In dynamic load testing, PID-Fuzzy is also better than conventional PID. With a conventional PID controller when the load is 9nm with a set point of 1500 RPM, the risetime is 10.0 ms and the steady error is 1.8%. With the PID-Fuzzy method, a risetime of 6.6 ms is obtained and a steady error of 0.7.
PEMODELAN SISTEM FOC KENDALI KECEPATAN MOTOR INDUKSI 3 FASA MENGGUNAKAN PI CONTROLLER Sri Muntiah Andriami; Era Purwanto; Hary Oktavianto; Kadek Reda Setiawan Suda; Abdullah Aziz Muntashir
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 20 No. 1 (2023): Edisi Januari 2023
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptkundiksha.v20i1.53986

Abstract

The speed regulation on this 3-phase induction motor uses a PI Controller by applying the Field Oriented Control (FOC) method, one part of the vector control method. One of the systems developed in this study is the speed control of a 3-phase induction motor by modeling the Field Oriented Control (FOC) system with a speed controller using a PI Controller, the results obtained from speed regulation are that to achieve steady state it only takes 1 to 2 seconds with Stedy error average is 1.175% with not too big rise time and overshoot. By modeling the FOC system using the PI Controller, it is able to overcome the weakness in the speed of the induction motor and provide high control system performance criteria, by suppressing the overshoot and steady state error to close to zero, and the rise time and settling time are relatively fast compared to the open loop controller.
Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung FAKHRUDDIN, HANIF HASYIER; TOAR, HANDRI; PURWANTO, ERA; OKTAVIANTO, HARY; BASUKI, GAMAR; APRIYANTO, RADEN AKBAR NUR; MUNTASHIR, ABDILLAH AZIZ
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 4: Published October 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i4.786

Abstract

ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView
Aplikasi Direct Matrix Converter pada Pengendali Kecepatan Motor Induksi 3 Fase menggunakan Modulasi Venturini BASUKI, GAMAR; PURWANTO, ERA; OKTAVIANTO, HARY; JATI, MENTARI PUTRI; NUGROHO, MOCHAMAD ARI BAGUS
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3: Published September 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.518

Abstract

ABSTRAKMotor induksi yang paling banyak digunakan juga memiliki kekurangan seperti losses yang cukup tinggi, power factor correction, dan efisiensi yang rendah. Oleh karena itu, dibutuhkan pengendali motor induksi yang memiliki performa dan efisiensi yang tinggi. Salah satu jenis AC – AC konverter yang mempunyai efisiensi, lifetime, kekompakan dan faktor daya mendekati unity yang akan digunakan sebagai pengendali motor induksi adalah matrix converter. Metode venturini digunakan sebagai modulasi pada matrix converter. Untuk itu dalam penelitian ini dilakukan pembuatan simulasi menggunakan simulink MATLAB dan hardware matrix converter. Pengujian matrix converter menggunakan modulasi venturini sebagai pengendali motor induksi telah dilakukan dengan motor dapat berputar mencapai kecepatan nominal sebesar 1440 Rpm sesuai nameplate dan motor juga dapat berputar dibawah frekuensi nominal. Dengan penelitian ini, pengendalian motor induksi dapat lebih efisien dalam penggunaannya di berbagai bidang.Kata kunci: Matrix converter, metode venturini, motor induksi. ABSTRACTThe most widely used induction motors also have disadvantages such as fairly high losses, power factor correction, and low efficiency. From this disadvantages, we need an induction motor controller that has high performance and efficiency. One type of AC-AC converter that has efficiency, lifetime, compactness and power factor approach to unity that will be used as an induction motor controller is a matrix converter. The Venturini method is used as modulation in the matrix converter. For this reason, in this study, simulation was made using MATLAB simulink and hardware matrix converter. Matrix converter testing using venturini modulation as an induction motor controller has been done with the motor can be rotate reaching a nominal speed of 1440 Rpm according to nameplate and the motor can also rotate below the nominal frequency. It is expected that induction motor controller can be more efficient in their use in various fields.Keywords: Matrix converter, venturini method, induction motor
Kendali Kecepatan Motor Induksi 3 Fase Berbasis Particle Swarm Optimization (PSO) FAKHRUDDIN, HANIF HASYIER; TOAR, HANDRI; PURWANTO, ERA; OKTAVIANTO, HARY; APRIYANTO, RADEN AKBAR NUR; ADITYA, ANGGA WAHYU
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3: Published September 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.477

Abstract

ABSTRAKMotor induksi secara struktur dan kendali standarnya dirancang untuk bekerja pada kecepatan nominal, sehingga sulit mengendalikan kecepatan sesuai kebutuhan karena akan mengubah konstruksi motor. Penelitian tentang pengendalian motor induksi agar semudah mengendalikan motor DC sudah banyak dilakukan oleh peneliti, salah satunya adalah dengan kendali skalar. Kendali skalar banyak digunakan karena memiliki keunggulan sederhana, biaya murah, mudah didesain dan diimplementasikan, serta yang paling penting tidak memerlukan parameter dari motor induksi. Penggunaan kendali skalar yang telah dilengkapi pengendali PID penalaan otomatis, dengan parameter yang telah dioptimalkan algoritma Particle Swarm Optimization (PSO), akan memudahkan pengendalian kecepatan motor induksi tiga fase pada kecepatan beragam. Simulasi penalaan otomatis PID menggunakan PSO telah dilakukan dengan LabView, dengan karakteristik maksimal 10% overshoot, 1% error steady state dan rise time kurang dari 2 milidetik. Sementara dalam pengujian real time dengan MyRIO hasilnya tanpa overshoot, 5.5% error steady state maksimal dan rise time maksimal 5 detik.Kata kunci: Kendali skalar, PID, Particle Swarm Optimization, LabView ABSTRACTInduction motor is designed at nominal speed as default, we have to change its stucture to obtain dessired speed. Many researchers developt method how to control induction motor as simple as DC motor, one of the methods is scalar control. Scalar control has several benefits, such as simply, low cost, easily designed and implemented, and the main banefit is no necessary motor parameters. Using scalar control with PID controller that optimized Partical Swarm Optimization (PSO) algoritm, will ease to control 3 phase induction motor variant speed. Simulation auto tunning using PSO has done on LabView, it has some characteristic, they are 10% overshoot, 1% steady state error, and rise time within 2ms. In other hand, real time test using MyRIO got no overshoot, 5.5% steady state error maximal, and rise time maximal 5 s characteristic.Keywords: Scalar control, PID, Particle Swarm Optimization, LabView
Comparative Analysis of Human Detection using Depth Data and RGB Data with Kalman Filter: A Study on Haar and LBP Methods Aulia, Fira; Oktavianto, Hary; Dewantara, Bima Sena Bayu
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2739

Abstract

Accurate human detection in video streams with occlusions, illumination variances, and varying distances is crucial for various applications, including surveillance, human-computer interaction, and robotics. This study investigates the performance of two widely used object detection features, Haar-like and Local Binary Pattern (LBP), for detecting human upper bodies in color and depth images. The algorithms are combined with Adaptive Boosting Cascade classifiers to leverage the discriminative power of Haar-like features and LBP texture features. Extensive experiments were conducted on a dataset comprising color images and depth data captured from a Kinect camera to evaluate the algorithms' performance in terms of precision, recall, accuracy, F1-score, and computational efficiency measured in frames per second (fps). The results indicate that when tested on color images, the Haar-Cascade method outperforms LBP-Cascade, achieving higher precision (27.4% vs. 7.8%), recall (49.2% vs. 7.8%), accuracy (21.4% vs. 4.1%), and F1-score (35.2% vs. 7.8%), while maintaining a comparable computational speed (19.07 fps vs. 19.26 fps). However, when applied to depth data, the Haar-Cascade method, coupled with Kalman filtering, demonstrates significantly improved performance, achieving precision (79.3%), recall (79.3%), accuracy (65.8%), and F1-score (79.3%) above 70%, with a computational time of approximately 19.07 fps. The integration of Kalman filtering enhances the robustness and tracking capabilities of the system, making it a promising approach for real-world applications in human detection and monitoring. The findings suggest that depth information provides valuable cues for accurate human detection, enabling the Haar-Cascade algorithm to overcome challenges faced in color image analysis. 
Data acquisition of flow sensor based to measure water flow in underground drainage Wirama, I Made Adiswara; Widodo, Rusminto Tjatur; Al Rasyid, Muhammad Udin Harun; Oktavianto, Hary
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 1 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i1.9-20

Abstract

This research focuses on water flow sensors in underground drainage. In Indonesia, the use of sensors and actuators to control and measure water velocity and water discharge in underground drainage is not very popular, although one of the causes of flooding is caused by clogged drainage or workers forgetting to turn on the pump at a certain height and water flow. This paper will explain how to find out the speed and discharge of water in an underground drainage without checking it directly on site. The water flow sensor is used to measure the water velocity and water discharge entering a drainage system. The data acquisition conducted with 3 section, data preparation, data collecting, and data processing. Data preparation is preparing the microcontroller, sensor, and data display to get the data. Data collecting is about collecting the data from sensor in this research the data collected is in pulse that can be calculated to water discharge and water velocity in the data processing. From the experiment, it can be seen that the highest error rate on the sensor is 7.64% and the lowest at 0% or no error with the average amount of error is 2.46%. The data obtained from the sensor can be converted into water velocity and water discharge depending on the shape of the drainage. The length, width and height of the drainage are assumed to be 10x4x3 m respectively, with a constant water level of 2 m, so the water flow sensor is placed at a height of 1 m because the safe height is 1 m. From the case study conducted the error rate of the water flow sensor error is 0%, because the case study conducted using imaginary data. Based from this, the knowledge gained from the research, it can be used to determine the water velocity and water discharge value of underground drainage using water flow sensor, which is expected to be applied to pump houses to more easily determine the state of drainage.
LoRa-Based IoT Recommendations for Surabaya City Drainage Channel Using Multi-Node Multi-Hop Communication Izzulhaq, Muhammad Arya; Widodo, Rusminto Tjatur; Oktavianto, Hary
Jurnal Rekayasa Elektrika Vol 21, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i2.45095

Abstract

This paper focuses on the development of a multi-hop LoRa (Long-Range) communication network for real-time monitoring of urban drainage Internet of Things (IoT), specifically simulating the flood-prone area along the drainage channel of Jalan Jawa, Surabaya City. The novelty of this research lies in the selection of the optimal communication environment through path loss and shadowing analysis prior to implementing a multi-node, multi-hop, sensor medium access control (S-MAC) method. The selected environment at the first location demonstrated a lower path loss exponent of 1.55, typical of "in-building line-of-sight," compared to the second location with a loss exponent of 2.82, which resembled "urban area cellular radio." Applying the multi-hop technique successfully extended the data transmission range up to 750 meters with nodes placed at 250 meter intervals while maintaining a high data transfer rate. The experiments showed that increasing distance significantly reduced the received signal strength indicator (RSSI), with values dropping from -52.75 dBm at 150 meters to -98.25 dBm at 750 meters. This paper demonstrates the feasibility of using multi-hop communication rather than the conventional multi-node technique to ensure reliable data transmission and wider range, offering a solid foundation for building a robust communication network in urban drainage monitoring systems.
Co-Authors Abdillah Aziz Muntashir Abdullah Aziz Muntashir Achmad Purnomo Wijaya Achmad Rizky Ramadhani Amang Sudarsono, Amang angga wahyu aditya Apriyanto, R. Akbar Nur APRIYANTO, RADEN AKBAR NUR Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri BASUKI, GAMAR Bima Sena Bayu Dewantara Dadet Pramadihanto Era Purwanto Fakhruddin, Hanif Hasyier GAMAR BASUKI Gamar Basuki Gigih Prabowo Handri Toar HANIF HASYIER FAKHRUDDIN Hanif Hasyier Fakhruddin Hendriawan, Akhmad Idris Winarno Ilahi, Ahmada Haiz Zakiyil Irwansyah, Arif Izzulhaq, Muhammad Arya Kadek Reda Setiawan Suda Kisron Kisron M Udin Harun Al Rasyid, M Udin Harun Mentari Putri Jati MOCHAMAD ARI BAGUS NUGROHO Niam Tamami Niam Tamami R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Oktav Yama Hendra Ramadhan, Ibnu Cipta Ridwan Ridwan Rifqi Amalya Fatekha RIFQI NABILA ZUFAR Rusli, Muhammad Rizani Rusminto Tjatur Widodo Setiawan Suda, Kadek Reda Sri Muntiah Andriami Wahjoe Tjatur Sesulihatien Wirama, I Made Adiswara