10 research outputs found
Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System
Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets
PEMBUATAN MIKRODENSITOMETER OPTIK
Telah dibuat mikrodensitometer optik yang dapat
~ membedakan skala keabuan (8reytone) dari film dalam orde .yang keeil. Mikrodensitometer optik ini mempunyai
" resol usi 12.7 J-lm. Prinsi p ker ja dari alat i ni adal ah membaadingkan intensitas eahaya yang ditransmisikan film dengan i ntensi tas eahaya yClng datang. Dengan m.::-ngetahui perbandingan intensitas tersebut maka dapat diketahui ereytone pada setiap lokasi dari film. Mikrodensitometer yang tel'ah dibuat ini mempunyai spesifikasi sebagai ber1kut.
1). Resolusi = 12.7 /-lm .. 2). Keeepatan seannin& = 40 step/detik. 3). Step pergerakkan = 12.7 J-lm .
Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle
This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14×14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset
Peningkatan Aktivitas dan Prestasi Belajar Siswa melalui Metode Student Teams Achievement Divisions (STAD) pada Pembelajaran IPA Siswa Kelas VII F SMP Negeri 1 Bulu
Penelitian tindakan ini dilatarbelakangi oleh permasalahan yang timbul dalam pembelajaran IPA, kususnya pada materi atau kompetensi dasar “ Klasifikasi makhluk hidup di kelas VII F Semester 1 SMP Negeri 1 Bulu. Subjek penelitian tindakan kelas ini adalah siswa kelas VII F SMP Negeri 1 Bulu tahun pelajaran 2019/ 2020 sebanyak 28 siswa sebagai subjek penerima tindakan, sedangkan untuk subjek pelaku tindakan adalah guru IPA kelas VII F selaku guru, teman sejawat selaku subjek yang melakukan observasi proses pembelajaran, Kepala Sekolah selaku subjek sumber data .Metode pengumpulan data dilakukan melalui teknik tes, observasi dan dokumentasi. Penelitian Tindakan ini dilakukan dalam dua siklus, tiap-tiap siklus terdiri dari: perencanaan, tindakan, pengamatan dan refleksi. Hasil penelitian ini menunjukkan bahwa penggunaan model pembelajaran kooperatif tipe Student Teams Achievement Divisions (STAD) dapat meningkatkan prestasi belajar IPA siswa kelas VII F SMP Negeri 1 Bulu tahun pelajaran 2019/ 2020. Hal ini dapat dilihat dari nilai rata-rata prestasi belajar IPA siswa juga mengalami peningkatan yaitu sebelum tindakan sebesar 68,96, pada siklus I sebesar 75,14 dan pada siklus II sebesar 81,00.Kata-kata Kunci: pembelajaran kooperatif tipe STAD dan prestasi belajar IPA siswa
Landing Pad Detection and Computing Direction of Motion for Autonomous Precision Landing Quadcopter
This paper presents an algorithm for an autonomous quadcopter to perform autonomous precision landing. This research focuses on designing the quadcopter so that it can land precisely on the landing pad using image processing algorithms. First, the captured image will be converted to grayscale, then the thresholding method is carried out and followed by a morphological process to eliminate noise and produce a clear image. The detected image will be displayed in a frame that will calculate the distance to the middle point. It will be used as Pulse Width Modulation (PWM) input to adjust the direction of motion of the quadcopter. so that it can land autonomously. The algorithm was tested in several color pads which are located in the grass, sand and cluttered ground. Testing is carried out to test the accuracy and precision of the designed algorithm. The results of the experiment show an accuracy rate of 94.76% and a precision level of 96.59% with an average landing time of 19 seconds and an average detection time is 8.55 milliseconds
Design and Challenges on mmWave Antennas: A Comprehesive Review
Researchers are keen to continue developing and investigating millimeter wave spectrum bands because these bands potentially provide broadband bandwidth for extremely high data transfer rates. This paper presents a comprehensive review of the recent millimeter wave antenna development, especially for 30-40 GHz, 60 GHz, and 140 GHz, with their characteristics, limitations, and challenges. Several previous millimeter wave antennas are introduced, including their broadband bandwidth, gain, feeding technique, substrate, and beamwidth. Based on their measured performances show that the millimeter wave antenna has great potential to support realizing the extremely high transfer data rate in wireless communication systems
Web-Based Health Service Management Information System Development With The Linear Sequential Model Method
The clinic has several health facilities such as outpatient, inpatient, dental clinic, laboratory, family planning/MCH, pharmacy, and pharmacy. However, at the clinic, administrative processes are still carried out manually and are not computerized properly, making it difficult for staff because the process of storing and integrating data has not been carried out effectively and efficiently. Therefore we need a systematic and automatic information system to assist the administrative and managerial processes of the clinic. The information system developed in this study is based on a web application using the Laravel 8 framework. The method used for system development is the linear sequential model commonly known as the classic life cycle or waterfall development model. The system that has been made is tested using the black box texting method combined with the UAT (User Acceptance Testing) method to find out whether the system meets functional requirements and is by the design. Based on testing using the UAT method, the average value of 6 different indicators is 91%. Therefore it can be concluded that the web-based information system for this clinic has an easy-to-understand way of working and attractive features so that it can provide convenience in the patient treatment process and clinical managerial processes
Food Image Detection System and Calorie Content Estimation Using Yolo to Control Calorie Intake in the Body
Excess calories in the body can cause obesity and several degenerative diseases, such as diabetes mellitus, heart disease, stroke, hypertension, and others. This system helps people maintain a balanced calorie content that enters the body. The research designs this system using the YOLO algorithm model to detect the type of food which is then developed using the Python programming language to estimate the calories of the detected food. YOLO uses the principle of feature extraction in images that are processed through filters as arrays to perform detection. This system calculates the food calories estimation by multiplying the calories for each food by the amount according to the type of food detected. The calorie value of the food provided is based on the number of calories for each portion of food taken from FatSecret Indonesia. The result is that food detection performance is quite good with average precision, recall, and F1-score values of 0.94, 0.90, and 0.91 respectively, when testing the model. However, when tested on Hugging Face, the performance decreased with the average values of precision, recall, and F1-score respectively, namely 0.84, 0.32, and 0.41. This decrease in performance is because of poor CPU usage and a decrease in image quality when uploaded to the Hugging Face