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All Journal CommIT (Communication & Information Technology) Computatio : Journal of Computer Science and Information Systems Humaniora ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Alexander A. S. Gunawan, Alexander A. S.
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : CommIT (Communication

 1 
Face Recognition Performance in Facing Pose Variation Gunawan, Alexander Agung Santoso; Prasetyo, Reza A
CommIT (Communication and Information Technology) Journal Vol 11, No 1 (2017): CommIT Vol. 11 No. 1 Tahun 2017
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i1.1847

Abstract

There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.
Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks Qur'ania, Arie; Harsani, Prihastuti; Triastinurmiatiningsih, Triastinurmiatiningsih; Wulandhari, Lili Ayu; Gunawan, Alexander Agung Santoso
CommIT (Communication and Information Technology) Journal Vol 14, No 1 (2020): CommIT Vol. 14 No. 1 Tahun 2020
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.5952

Abstract

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.
Co-Authors Anderies Anderies Arie Qur’ania Hakim, Jerry Pratama Heri Ngarianto Heri Ngarianto, Heri Jalaludin Ar Raniry William Tchin Jamhari Jamhari Jimmy Linggarjati, Jimmy Prambudi Herbowo Putro Prasetyo, Reza A Prihastuti Harsani Senjaya, Benny Triastinurmiatiningsih, Triastinurmiatiningsih Viska Noviantri Wulandhari, Lili Ayu Yudha Putra Darmawan Yuwono, Febrian