 Subjects -> ELECTRONICS (Total: 207 journals)
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- Analisis Kinerja Penggabungan Logika Fuzzy dan PID pada Penjejak Matahari
Dua Sumbu
Authors: Muhammad Nur Hasan, Yuwaldi Away, Suriadi Suriadi, Andri Novandri
Abstract: Pemanfaatan energi terbarukan dari sistem panel surya semakin banyak diaplikasikan, tapi hingga kini pemanfaatannya tidak secara maksimal. Perpindahan matahari yang disebabkan oleh rotasi bumi dan kondisi berawan patut diperhitungkan untuk memaksimalkan energi listrik pada panel surya. Pada penelitian ini diusulkan sebuah konsep yang memperhitungkan pergerakan sun tracker dua sumbu dengan menggunakan penggabungan dua metode yaitu PID dan logika Fuzzy (F-PID). Untuk mengikuti pergerakkan matahari maka digunakan sensor LDR sebagai input terhadap cahaya dan output yang digunakan untuk menggerakkan dua servo untuk sumbu X dan sumbu Y. Sun tracker yang digunakan berbasis geometri tetrahedron dan menggunakan tiga LDR sebagai input. Komponen input dan output saling terhubung ke ATmega328P dengan menggunakan program gabungan logika Fuzzy dan PID. Pemrograman logika Fuzzy terlebih dahulu dilakukan pada aplikasi Matlab menggunakan FIS (Fuzzy Inference System), kemudian dikonversikan menjadi bahasa pemrograman berbasis Arduino. Pergerakkan sun tracker dan tegangan yang diterima panel surya akan disimpan kedalam SD card menggunakan modul data logging. Mengatur pergerakkan sun tracker menggunakan metode F-PID ini bermaksud untuk memaksimalkan energi listrik yang diterima oleh panel surya. Hasil yang diharapkan dari penelitian ini adalah sun tracker lebih tepat dan cepat mengikuti pergerakan matahari berdasarkan intensitas cahaya yang tinggi guna memaksimalkan produksi energi listrik pada panel surya.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.15128
Issue No: Vol. 19, No. 1 (2023)
- Identification of Power Quality Disturbances Based on Fast Fourier
Transform and Artificial Neural Network
Authors: dimas okky anggriawan, Endro Wahjono, Indhana Sudiharto, Anang Budikarso
Abstract: This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.27120
Issue No: Vol. 19, No. 1 (2023)
- Sistem Kontrol pada Automated Guided Vehicle Beroda Mekanum menggunakan
Sliding Mode Controller
Authors: Muhammad Faiz, Bambang Sumantri, Bima Sena Bayu Dewantara
Abstract: Proses produksi pada industri tidak terlepas dari distribusi barang. Dalam perkembangannya, distribusi barang telah menggunakan robot tanpa awak yaitu Automated Guided Vehicle (AGV). Dalam prakteknya, AGV menghadapi lingkungan dengan ketidaklinieran dan kompleksitas tinggi. Hal ini menjadi pertimbangan diterapkannya robust kontrol. Sliding Mode Control (SMC) merupakan salah satu metode dengan kemampuan dalam menghadapi gangguan dan ketidaklinieran tinggi. SMC memiliki kekurangan yaitu adanya chattering yang berakibat adanya vibrasi pada motor yang pada akhirnya dapat merusak motor. Penelitian ini mengurangi chattering pada SMC, serta meningkatkan performa kontrol dengan menggunakan modifikasi fungsi signum pada SMC menjadi fungsi saturasi. Penelitian dilakukan dengan menggunakan ROS, V-Rep dan mikrokontroler. Mikrokontroler bertindak sebagai pengolah algoritma sistem. Lebih lanjut, fungsi saturasi mampu mengurangi rise time sebesar 30%, overshoot 16 %, dan RMSE sebesar 0,21%.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.28127
Issue No: Vol. 19, No. 1 (2023)
- Model dan Kendali Modular pada Pendulum Terbalik tipe Rotary
Authors: Erwin Susanto
Abstract: Pendulum terbalik tipe rotary (Rotary Inverted Pendulum, RIP) merupakan sistem fisik yang seringkali dijadikan sebagai platform teori dan aplikasi kendali pada sistem yang tak linier, tidak stabil dan underactuated sehingga memberikan tantangan untuk desain dan realisasi kendalinya. Konstruksi mekanik sistem RIP meliputi lengan pendulum yang berputar horisontal pada poros dudukan dan lengan pendulum vertikal yang bergerak mengayun dari posisi bawah ke posisi setimbang tegak keatas. Makalah ini menyajikan skema model dan kendali pada RIP secara modular, dimana tiga bagian kendali disusun dan direalisasikan menggunakan Simscape Multibody. Tiga bagian kendali meliputi: kendali mengayun (swing-up) menggunakan kendali Proporsional Derivatif (PD) umpan balik positif, kendali mode pensaklaran yang bekerja mengubah kendali swing-up menjadi kendali stabilisasi saat lengan pendulum vertikal mencapai posisi disekitar kesetimbangan tegak, dan kendali stabilisasi untuk mempertahankan kesetimbangan lengan vertikal menggunakan kendali PD. Trayektori pergerakan lengan pendulum dan visualisasi pergerakan tiga dimensi sistem pendulum yang disajikan menggunakan Simscape Multibody Matlab menunjukkan keberhasilan metode yang digunakan.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.28262
Issue No: Vol. 19, No. 1 (2023)
- Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic
Algorithm for Mobile Robot Navigation Control
Authors: Alif Wicaksana Ramadhan, Bima Sena Bayu Dewantara, Setiawardhana Setiawardhana
Abstract: The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, , and impact range, , must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the parameter adaptive. The distance, , and angle, , concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic Coppelia simulator demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly and swiftly from the starting point to the goal.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.28330
Issue No: Vol. 19, No. 1 (2023)
- Water Quality Level for Shrimp Pond at Probolinggo Area Based on Fuzzy
Classification System
Authors: Fithrotul Irda Amaliah
Abstract: Since several years ago, vaname shrimp (Litopenaeus vannamei) has been extensively cultivated in Indonesia because it has good business opportunities. In aquaculture activities, water quality is an important factor that dramatically impacts the survival and quality of shrimp in the pond. Therefore, information of water quality must be known by the farmer for obtaining a satisfactory harvest. This study aims to develop a water quality monitoring system based on information of temperature, pH, salinity, and dissolved oxygen. The data from sensors are sent to the cloud utilizing Internet of Things (IoT) technology and then classified by a fuzzy logic system. In order to help farmers easily know the water quality of their shrimp pond, four sensor data including the result of classification from fuzzy logic are sent to the phone. After a trial of the system, 100% of the data are successfully sent to the cloud (google spreadsheet). The system also successfully classified the level of water quality as the expectation of the farmer. With this system, it is hoped that it can assist farmers in monitoring the water quality of shrimp pond to improve the quality and quantity of shrimp.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.28631
Issue No: Vol. 19, No. 1 (2023)
- DEFECT DETECTION SYSTEM ON STAMPING MACHINE USING THE IMAGE PROCESSING
METHOD
Authors: Nur Wisma Nugraha, Gun Gun Maulana, Suharayadi Pancono
Abstract: Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Therefore, to implement an automatic product defect detection system we will use Image Processing and RFID technology, image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). while RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.29111
Issue No: Vol. 19, No. 1 (2023)
- Multi-Class Heart Abnormalities Detection Based on ECG Graph Using
Transfer Learning Method
Authors: Sugondo Hadiyoso, Suci Aulia, Indrarini Dyah Irawati
Abstract: The heart is one of the vital organs in the circulatory system. Regular checkups are very important to prevent heart disease. The most basic examination is blood pressure then further examination is related to the evaluation of the electrical activity of the heart using an electrocardiogram (ECG). The ECG carries important information regarding various abnormalities of heart function. Several automated classification techniques have been proposed to facilitate diagnosis. However, not all digital ECG devices provide raw data for analysis. ECG classification method based on images can be an alternative in classification. Therefore, in this study, it is proposed to classify ECG based on signal images. The proposed classification method uses transfer learning with VGG, AlexNet, and DenseNet architectures. The method used for the classification of multi-class ECG consists of normal, PVC, Atrial Fibrilation, AFL, Bigeminy, LBBB, and APB. The simulation results generate the best accuracy of 92% and F1-score of 92%. Best performance is achieved using DenseNet architecture at 60 epochs. This study is expected to be a new reference technique in the classification of ECG signals.
PubDate: 2023-03-27
DOI: 10.17529/jre.v19i1.28637
Issue No: Vol. 19, No. 1 (2023)
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