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All Journal TEKNIK PROtek : Jurnal Ilmiah Teknik Elektro Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Jurnal Rekayasa elektrika
Florentinus Budi Setiawan
Universitas Katolik Soegijapranata
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Peningkatan Efisiensi Kinerja Switched Reluctance Motor dengan Metode Pergeseran Sudut Fasa Wardani, Agata Dita; Riyadi, Slamet; Pratomo, Leonardus Heru; Setiawan, Florentinus Budi
TEKNIK Vol 42, No. 3 (2021): December 2021
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v42i3.33970

Abstract

Penggerak listrik banyak digunakan pada kendaraan listrik, sebagai contoh adalah switched reluctance motor (SRM). SRM memiliki keunggulan seperti: kontruksi sederhana, perawatan mudah, performa tinggi, dan ramah lingkungan. Pengoperasian SRM membutuhkan kendali dan sensor. Deteksi posisi rotor umumnya menggunakan sensor hall effect yang akan menentukan interval eksitasi. Proses ini memiliki banyak kelemahan salah satunya adalah kepresisian. Peletakan sensor hall effect secara geometris memiliki kelemahan mencakup akurasi serta membatasi dalam pemberian eksitasi. Kelemahan peletakan sensor hall effect diantisipasi dengan rotary encoder sebagai deteksi posisi rotor. Alat tersebut memiliki tingkat kepresisian yang tinggi dan dapat mengatur sudut yang diperlukan agar lebih akurat dalam operasi SRM. Pemberian sudut eksitasi dapat dilihat dari karakteristik induktansi SRM. Sudut eksitasi yang tepat dapat menghasilkan torka optimum dan memaksimalkan kinerja SRM. Tujuan penilitian ini guna meningkatan efisiensi SRM menggunakan pergeseran sudut fasa eksitasi stator. Untuk mendukung hasil kajian dilakukan simulasi dan divalidasi dengan pengujian laboratorium. Hasil kajian menunjukkan kondisi optimal pada sudut θon= 5° θoff =20° menghasilkan arus puncak 1,2 A dan kecepatan 1650 RPM. Dari hasil tersebut diperoleh peningkatan efisiensi kinerja dengan pergeseran sudut fasa SRM terhadap torka, kecepatan dan arus yang dihasilkan.
Pattern Recognition untuk Deteksi Posisi pada AGV Berbasis Raspberry Pi Florentinus Budi Setiawan; Franciska Amalia Kurnianingsih; Slamet Riyadi; Leonardus Heru Pratomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 1: Februari 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1656.303 KB) | DOI: 10.22146/jnteti.v10i1.738

Abstract

The development of technology in automation and robotics is overgrowing because of its high-efficiency level in terms of labor and time. In the warehousing system, one of the robots used is Automated Guided Vehicle (AGV). AGV is a transportation device in the form of a robot that can be controlled automatically, which functions as a carrier of goods using a navigation system to move in a predetermined direction. One of the existing AGV navigation systems is by following a line pattern on the floor. The system is inefficient because, gradually, the line pattern will wear out and can not be detected again due to the friction force of the AGV wheels itself. Therefore, it is necessary to develop an AGV navigation system to minimize these obstacles. This pattern recognition system uses a pattern placed on the building ceiling and camera as a sensor facing upwards, so that AGV can freely detect patterns. Then, the detected pattern was processed through a programmed Raspberry Pi 4 Model B. The test results show that this system can detect the position and successfully displays the coordinate point (x, y) of the AGV and will continue to run at any time until the program is changed as ordered.
Segmental Sinusoidal Model for Speech Signal Coding Setiawan, Florentinus Budi; Soegijoko, Soegijardjo; Sugihartono, Sugihartono; Tjondronegoro, Suhartono
Makara Journal of Technology Vol. 10, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Segmental Sinusoidal Model for Speech Signal Coding. Periodic signal can be decomposed by sinusoidal component with Fourier series. With this characteristic, it can be modeled referring by sinusoidal form. By the sinusoidal model, signal can be quantized in order to encode the speech signal at the lower rate. The recent sinusoidal method is implemented in speech coding. By using this method, a block of the speech signal with 20 ms to 30 ms width is coded based on Fourier series coefficients. The new method proposed is quantization and reconstruction of speech signal by the segmental sinusoidal model. A segment is defined as a block of the speech signal from certain peak to consecutive peak. The length of the segment is variable, instead of the fixed block like the recent sinusoidal method. Coder consists of the encoder and the decoder. Encoder works to code speech signal at variable rate. Then coded signal will be transmitted to receiver. On the receiver, coded signal will be reconstructed, so that the reconstruction signal has the near quality compared with the original signal. The experimental results show that the average of segmental SNR is more than 20 dB.
Design of Audiosonic Frequency Wave Therapy Tool With Arduino Mega-Based Spectrum Analyzer Monitoring Florentinus Budi Setiawan; Daniel Danin Prasetyo; Leonardus Heru Pratomo
PROtek : Jurnal Ilmiah Teknik Elektro Vol 10, No 1 (2023): PROtek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v10i1.5536

Abstract

As technology develops, more and more devices can produce or record sound waves. There are currently a lot of sound wave generators in use; however, the frequency output range of these generators is highly constrained. This study is being done to help doctors or the general public treat specific illnesses or disorders such as neurological disorders, headaches, and stomach digestion issues. This prototype was created using experimental or lab techniques. by running tests on the variables being utilized. The Arduino Mega 2560 microcontroller is used to operate this prototype. The Arduino Mega 2560 employs the C and C+ programming languages for its code. The Arduino Mega 2560 may be used to modify the keypad's output frequency, amplitude, and data input. This tool is designed to be able to output frequencies of 20 Hz to 20,000 Hz (human sound). In this study, it has an output in the form of frequencies produced from speakers with test frequencies, namely 200 Hz, 10 kHz, and 17 kHz
Pengenalan Tanda Arah untuk Navigasi Automatic Guided Vehicle berbasis Raspberry Pi Florentinus Budi Setiawan; Rachmat Hidayat; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.4959

Abstract

The development of modern times in robotics and mechanization technology has increased significantly in the past few decades due to their high efficiency in time and energy. In the goods mobilization system for companies’ use, particularly the industrial and warehousing divisions, one of the robots that are used for transporting goods is an automatic guided vehicle (AGV). One of the old navigation methods in AGV is the use of a sensor to follow the line pattern on the detected object, namely the line on the floor. This method is rather ineffective because, gradually, these line pattern objects on the floor will fade caused by the effect of AGV wheels’ frictional forces, causing the camera sensor can no longer detect them. Therefore, it is necessary to improve the AGV navigation method so that it can be a sustainable innovation. This navigation method used four image objects positioned in the area traversed by the AGV robot and the camera served as a forward-facing sensor so that the AGV could detect the pattern of image objects with the help of computer vision using the OpenCV software library. The pattern of the detected image object was processed by a program designed on the Raspberry Pi 4 Model B minicomputer. The test results prove that this method can detect image objects within the camera’s field of view and successfully display the output of the image object. The system managed to recognize objects quite accurately, with parameters of 10–95 cm, and through several experiments. The analysis of the rotational speed of the front and rear wheels of the AGV was carried out using an oscilloscope and tachometer as a means of measuring wheel speed or rotation.
Perancangan Automated Guided Vehicle Menggunakan Penggerak Motor DC dan Motor Servo Berbasis Raspberry Pi 4 Florentinus Budi Setiawan; Yosia Yovie Christian Wibowo; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1383.179 KB) | DOI: 10.17529/jre.v18i2.25863

Abstract

The influence of the industrial revolution 4.0 resulted in very significant changes. Many companies compete to produce robots that facilitate human work, in terms of energy and time in the process of producing goods. One of the robots being developed is the Automated Guided Vehicle (AGV), a vehicle with automatic control. AGV has high accuracy, easy maintenance, and a long operating time. This study discusses the design and implementation of AGV using 2 motors. The front motor using a servo motor is used for steering to turn right and turn left, while the rear motor in the form of a DC motor is used to regulate the speed of the AGV. The AGV movement system is controlled by computer vision. The AGV problem encountered is that the camera reading distance is close, which makes it less efficient in industrial use. This problem can be solved with a camera connected to a raspberry pi capable of capturing text and images from a distance of 100 cm. The use of computer vision makes the AGV robot easy to move. In this study, the accuracy of the movement of the AGV robot to the trajectory pattern has an average angle difference of 3.09°. The difference in the angle indicates a small error so that the AGV can operate optimally. Infield applications, this AGV is used in the manufacturing industry to move goods. Therefore, the use of AGV is needed because it has high accuracy and small error.
Penerapan Algoritma HSV pada Autonomous Car untuk Sistem Self-Driving Berbasis Raspberry Pi 4 Florentinus Budi Setiawan; Padang Ufqi Sutrisno; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.212 KB) | DOI: 10.17529/jre.v18i4.27495

Abstract

Perkembangan teknologi di sektor transportasi di masa ini semakin krusial. Sehingga perusahaan berinovasi menciptakan mobil yang dapat berjalan sendiri dengan tingkat keamanan yang tinggi. Pada penelitian ini, kami merancang sistem self-driving untuk mobil RC skala 1:10 menggunakan komponen utama berupa Raspberry Pi 4 sebagai pengolahan citra untuk kendali otomatis pada autonomous car. Untuk mengatur pergerakan roda belakang dan steering menggunakan motor DC. Penelitian ini menerapkan computer vision yang dipakai untuk sistem navigasi agar dapat berjalan sesuai dengan lintasan. Permasalahan yang dijumpai pada penelitian sebelumnya adalah masih mengambil sampel lintasan terlebih dahulu yang dirasa kurang efisien karena pada jalan yang belum diambil sampelnya tidak dapat dilalui robot tersebut. Untuk memecahkan permasalahan ini maka peneliti menerapkan algoritma HSV agar dapat mengikuti lintasan secara real-time. Algoritma HSV(hue, saturation, value) merupakan sistem untuk mendeteksi tepi garis lintasan dengan memproses gambar dari kamera Raspberry Pi.  Dari hasil kalibrasi nilai threshold yang digunakan adalah sebesar Hmin = 135 dan Hmax = 179, Smin = 70 dan Smax = 255, dan nilai V sebesar Vmin = 53 dan Vmax = 106 agar dapat mendeteksi jalur lintasan secara jelas, baik di dalam ruangan maupun diluar ruangan,  dan HSV toleran terhadap perubahan intensitas cahaya. Itulah keuntungan dari algoritma HSV. Berdasarkan hasil pengujian dan implementasi robot ini dengan menggunakan kecerdasan buatan dapat bekerja sesuai dengan algoritma yang sudah dibuat dengan tingkat akurasi deteksi jalur yang cukup tinggi.
Perancangan Automated Guided Vehicle Menggunakan Penggerak Motor DC dan Motor Servo Berbasis Raspberry Pi 4 Florentinus Budi Setiawan; Yosia Yovie Christian Wibowo; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.25863

Abstract

The influence of the industrial revolution 4.0 resulted in very significant changes. Many companies compete to produce robots that facilitate human work, in terms of energy and time in the process of producing goods. One of the robots being developed is the Automated Guided Vehicle (AGV), a vehicle with automatic control. AGV has high accuracy, easy maintenance, and a long operating time. This study discusses the design and implementation of AGV using 2 motors. The front motor using a servo motor is used for steering to turn right and turn left, while the rear motor in the form of a DC motor is used to regulate the speed of the AGV. The AGV movement system is controlled by computer vision. The AGV problem encountered is that the camera reading distance is close, which makes it less efficient in industrial use. This problem can be solved with a camera connected to a raspberry pi capable of capturing text and images from a distance of 100 cm. The use of computer vision makes the AGV robot easy to move. In this study, the accuracy of the movement of the AGV robot to the trajectory pattern has an average angle difference of 3.09°. The difference in the angle indicates a small error so that the AGV can operate optimally. Infield applications, this AGV is used in the manufacturing industry to move goods. Therefore, the use of AGV is needed because it has high accuracy and small error.
Penerapan Algoritma HSV pada Autonomous Car untuk Sistem Self-Driving Berbasis Raspberry Pi 4 Florentinus Budi Setiawan; Padang Ufqi Sutrisno; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i4.27495

Abstract

Perkembangan teknologi di sektor transportasi di masa ini semakin krusial. Sehingga perusahaan berinovasi menciptakan mobil yang dapat berjalan sendiri dengan tingkat keamanan yang tinggi. Pada penelitian ini, kami merancang sistem self-driving untuk mobil RC skala 1:10 menggunakan komponen utama berupa Raspberry Pi 4 sebagai pengolahan citra untuk kendali otomatis pada autonomous car. Untuk mengatur pergerakan roda belakang dan steering menggunakan motor DC. Penelitian ini menerapkan computer vision yang dipakai untuk sistem navigasi agar dapat berjalan sesuai dengan lintasan. Permasalahan yang dijumpai pada penelitian sebelumnya adalah masih mengambil sampel lintasan terlebih dahulu yang dirasa kurang efisien karena pada jalan yang belum diambil sampelnya tidak dapat dilalui robot tersebut. Untuk memecahkan permasalahan ini maka peneliti menerapkan algoritma HSV agar dapat mengikuti lintasan secara real-time. Algoritma HSV(hue, saturation, value) merupakan sistem untuk mendeteksi tepi garis lintasan dengan memproses gambar dari kamera Raspberry Pi.  Dari hasil kalibrasi nilai threshold yang digunakan adalah sebesar Hmin = 135 dan Hmax = 179, Smin = 70 dan Smax = 255, dan nilai V sebesar Vmin = 53 dan Vmax = 106 agar dapat mendeteksi jalur lintasan secara jelas, baik di dalam ruangan maupun diluar ruangan,  dan HSV toleran terhadap perubahan intensitas cahaya. Itulah keuntungan dari algoritma HSV. Berdasarkan hasil pengujian dan implementasi robot ini dengan menggunakan kecerdasan buatan dapat bekerja sesuai dengan algoritma yang sudah dibuat dengan tingkat akurasi deteksi jalur yang cukup tinggi.
Co-Authors Daniel Danin Prasetyo Erdhi Widyarto Franciska Amalia Kurnianingsih Hengky Adi Wijaya Leonardus Heru Pratomo Leonardus Heru Pratomo Leonardus Heru Pratomo Padang Ufqi Sutrisno Rachmat Hidayat Slamet Riyadi Slamet Riyadi Soegijardjo Soegijoko Sugihartono Sugihartono Suhartono Tjondronegoro Wardani, Agata Dita Yosia Yovie Christian Wibowo Yulianto Tejo Putranto