Sistem Presensi Karyawan Berbasis Pengenalan Wajah Dengan Metode Support Vector Machine

Sistem presensi saat ini yang ada pada instansi ataupun perusahaan masih banyak yang menggunakan sistem manual. Disisi lain, perusahaan-perusahaan tersebut juga telah memiliki aplikasi pengelolaan SDM online. Oleh karena itu, untuk efektifitas dan pengembangan sistem, perlu dilakukan pengembangan sistem presensi manual tersebut menjadi sebuah sistem yang dapat diintegrasikan dengan sistem pengelolaan SDM. Untuk itu, penelitian ini mengusulkan pengembangan sistem presensi berbasiskan pengenalan wajah yang diintegrasikan dengan aplikasi pengelolaan SDM. Sistem yang dibangun merupakan sistem deteksi dan pengenalan menggunakan Support Vector Machine yang di kombinasikan dengan metode Histogram of oriented gradient. Hasil pengujian sistem presensi menunjukkan hasil recall sebesar 77,78%, nilai spesifitas 32,22%, akurasi sistem 72,78%, dan kepresisian sistem mencapai 70,71%.

Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

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%.

Pengenalan ASL Menggunakan Metode Ekstraksi HOG dan Klasifikasi Random Forest

Bahasa isyarat memiliki banyak jenis salah satunya yaitu American Sign Language (ASL). Pada Penelitian ini digunakan citra handshape alfabet ASL yang diekstraksi menggunakan fitur Histogram of Oriented Gradient (HOG) selanjutnya fitur yang dihasilkan digunakan untuk klasifikasi Random Forest. Hasil pengujian menunjukan bahwa dengan menggunakan fitur HOG dan metode klasifikasi Random Forest untuk pengenalan ASL memberikan tingkat accuracy yang baik, dengan nilai accuracy overall sebesar 99.10%, nilai rata - rata accuracy per class 77.43%, nilai rata - rata precission 88.81%, dan nilai rata - rata recall 88.65% .

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

Apple Quality Identification and Classification by Computer Vision Based on Deep Learning

This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on Convolutional Neural Networks (CNN) which aimed at accurate and fast grading of apple quality. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. Two other methods, which were Google Inception v3 model and traditional imaging process method, were also used for apple quality classification. The greatest training accuracy of the Google Inception v3 model was 92% with 91.2% validation accuracy. The 78.14% accuracy was obtained by traditional method based on histogram of oriented gradient (HOG) and gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The three models were tested using independent 300 apples testing set, getting accuracy of 95.33%, 91.33%, and 77.67%, respectively. The results showed that the proposed model was more helpful and accurate for classification of apple quality. Furthermore, the training times of three methods were 27, 51, and 287 minutes, respectively. The proposed model can be considered a cost-effective method for fast grading of apple quality.