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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Bhakti Yudho Suprapto
Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Indonesian load prediction estimation using long short term memory Erliza Yuniarti; Siti Nurmaini; Bhakti Yudho Suprapto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1026-1032

Abstract

Prediction of electrical load is important because it relates to the source of power generation, cost-effective generation, system security, and policy on continuity of service to consumers. This paper uses Indonesian primary data compiled based on data log sheet per hour of transmission operators. In preprocessing data, detrending technique is used to eliminate outlier data in the time series dataset. The prediction used in this research is a long-short-term memory algorithm with stacking and time-step techniques. In order to get the optimal one-day forecasting results, the inputs are arranged in the previous three periods with 1, 2, 3 layers, 512 and 1024 nodes. Forecasting results obtained long short-term memory (LSTM) with three layers and 1024 nodes got mean average percentage error (MAPE) of 8.63 better than other models.
Deep learning with focal loss approach for attacks classification Yesi Novaria Kunang; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.18772

Abstract

The rapid development of deep learning improves the detection and classification of attacks on intrusion detection systems. However, the unbalanced data issue increases the complexity of the architecture model. This study proposes a novel deep learning model to overcome the problem of classifying multi-class attacks. The deep learning model consists of two stages. The pre-tuning stage uses automatic feature extraction with a deep autoencoder. The second stage is fine-tuning using deep neural network classifiers with fully connected layers. To reduce imbalanced class data, the feature extraction was implemented using the deep autoencoder and improved focal loss function in the classifier. The model was evaluated using 3 loss functions, including cross-entropy, weighted cross-entropy, and focal losses. The results could correct the class imbalance in deep learning-based classifications. Attack classification was achieved using automatic extraction with the focal loss on the CSE-CIC-IDS2018 dataset is a high-quality classifier with 98.38% precision, 98.27% sensitivity, and 99.82% specificity.
Optimum Switching Angle Of Switched Reluctance Motor Using Response Surface Methodology Agus Adhi Nugroho; Muhammad Khosyi'in; Bustanul Arifin; Bhakti Yudho Suprapto; Muhamad Haddin; Zainuddin Nawawi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3321

Abstract

Switched Reluctance Motor has numerous advantages compared to another electric motor. Simple structure, low-cost production, robustness, and high fault tolerance have been remarkable milestones. Still, the problem of excitation angle at power converter becomes crucial, especially for traction use, requiring higher torque at low speed for starting and acceleration. Therefore, this research emphasized finding the optimum excitation angle at low speed using Response Surface Methodology, a practical application to achieve the highest torque, as indicated by the best speed in the constant torque region. As a result, using Matlab simulation, the adaptive combination of optimum angles reached 2691 rpm quicker than a single excitation angle with 2568 rpm, an increase of 4.79% higher speed using RSM optimization. According to the experimental data, the adaptive combination of optimum angle achieved 2475 rpm better than the single excitation angle reached 2340 rpm, an increase of 5.77% higher speed using the Response Surface Methodology.
Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle Aulia Ghaida; Hera Hikmarika; Suci Dwijayanti; Bhakti Yudho Suprapto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 6, No 1 (2020): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v16i1.16949

Abstract

Nowadays, an autonomous vehicle is one of the fastest-growing technologies. In its movements, the autonomous vehicle requires a good navigation system to run on the specified lane. One sensor that is often used in navigation systems is the camera. However, this camera is constrained by the process and its reading, especially to detect roads that are suitable for the vehicle's position. Thus, this research was conducted to detect the road and distance of nearby objects using the HSV color space method. From the test results, this research succeeded in detecting roads with an accuracy of 78.012 %, and an accuracy of 80% for the safe/unsafe area detection. The results also showed that the method achieved an accuracy of 80% and 74.76%for object detection and object distance detection, respectively. The results of this research implied that the HSV method wasquite good with fairly high accuracy to detect roads and vehicles.
The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm Bhakti Yudho Suprapto; A. Wahyudin; Hera Hikmarika; Suci Dwijayanti
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.20684

Abstract

The challenge with using the Unmanned Aerial Vehicle (UAV) is when the UAV makes a landing. This problem can be overcome by developing a landing vision through helipad detection. This helipad detection can make it easier for UAVs to land accurately and precisely by detecting the helipad using a camera. Furthermore, image processing technology is used on the image produced by the camera. You Only Look Once (YOLO) is an image processing algorithm developed to detect objects in real-time, and it is the result of the development of one of the Convolutional Neural Network (CNN) algorithm methods. Therefore, in this study the YOLO method was used to detect a helipad in real-time. The models used in the YOLO algorithm were Mean-Shift and Tiny YOLO VOC. The Tiny YOLO VOC model performed better than the Mean-Shift method in detecting helipads. The test results obtained a confidence value of 91.1%, and the system processing speed reached 35 frames per second (fps) in bright conditions and 37 fps in dark conditions at an altitude of up to 20 meters.
Implementation of Facial Landmarks Detection Method for Face Follower Mobile Robot Ahmad Zarkasi; Fachrudin Abdau; Agung Juli Anda; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto; Huda Ubaya; Rizki Kurniati
Generic Vol 14 No 1 (2022): Vol 14, No 1 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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

Abstract

This paper presents a new technique for facesrecognition based on auto-extracted facial marks. Our landmarks are those related to the outer corner of the nose. With extracted landmarks, a triplet of areas and their associated geometric invariance are formed. Where later the points on the outer corners of the eyes and nose will be connected with lines that will form a triangle. Later the line length will be calculated using the Euclidean Distance formula so that the area value of the triangle can be obtained. Then the data obtained will be trained using the Support Vector Machine algorithm so that they can recognize faces. And later the system will be implanted into a mobile robot with raspberry.
Neural network training for serial multisensor of autonomous vehicle system Eka Nuryanto Budisusila; Sri Arttini Dwi Prasetyowati; Bhakti Yudho Suprapto; Zainuddin Nawawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5415-5426

Abstract

This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.
Robot movement controller based on dynamic facial pattern recognition Siti Nurmaini; Ahmad Zarkasi; Deris Stiawan; Bhakti Yudho Suprapto; Sri Desy Siswanti; Huda Ubaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp733-743

Abstract

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).
The Impact of Telemetry Received Signal Strength of IMU/GNSS Data Transmission on Autonomous Vehicle Navigation Muhammad Khosyi'in; Sri Arttini Dwi Prasetyowati; Bhakti Yudho Suprapto; Zainuddin Nawawi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3901

Abstract

This paper presents the effect of received signal strength on IMU/GNSS sensor data transmission for autonomous vehicle navigation. A pixhawk 2.1 flight controller is used to build the navigation system. Straight lines with back-and-forth routes were tested using two types of SiK telemetry: Holybro and RFD. The results of the tests show that when the RSSI value falls close to the receiver's sensitivity value, the readings of the gyro sensor data, accelerometer, magnetometer, and GNSS compass data are disturbed. When the RSSI signal collides with noise, the radio telemetry link is lost, affecting the accuracy of speed data and the orientation of autonomous vehicles. According to Cisco's conversion table, the highest RSSI on Holybro telemetry is -48 dBm, and the lowest is -103 dBm, with a receiver sensitivity of -117 and data reading at a distance of about 427 meters. While the highest RSSI value on RFD telemetry is -17 dBm and the lowest is -113 dBm, even the lowest value is above the receiver's sensitivity limit of -121 dBm with data readings at a distance of approximately 749.4 meters. RFD outperforms Holybro in terms of RSSI and sensitivity at low data rates. When reading distance data to reference distance data using Google Earth and ArcGIS, RFD telemetry has a higher accuracy, with an average accuracy of 98.8%.
Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3957

Abstract

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32,768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process
Co-Authors A. Wahyudin Agung Juli Anda Agus Adhi Nugroho Ahmad Zarkasi Aulia Ghaida Bustanul Arifin Deris Stiawan Eka Nuryanto Budi Susila Erliza Yuniarti Fachrudin Abdau Hera Hikmarika Huda Ubaya Muhamad Haddin Muhammad Khosyi'in Muhammad Khosyi'in Muhammad Ridho Ramadhan Osvari Arsalan Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Suci Dwijayanti Yesi Novaria Kunang Zainuddin Nawawi