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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Simantec Journal of ICT Research and Applications ELINVO (Electronics, Informatics, and Vocational Education) Journal of Data Science and Its Applications Journal of Soft Computing Exploration Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Makara Journal of Technology Bulletin of Social Informatics Theory and Application
Arif Muntasa
Universitas Trunojoyo
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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KEAMANAN CITRA DENGAN WATERMARKING MENGGUNAKAN PENGEMBANGAN ALGORITMA LEAST SIGNIFICANT BIT Kurniawan, Kurniawan; Siradjuddin, Indah Agustien; Muntasa, Arif
Jurnal Informatika Vol 13, No 1 (2015): MAY 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (667.819 KB) | DOI: 10.9744/informatika.13.1.9-14

Abstract

Image security is a process to save digital. One method of securing image digital is watermarking using Least Significant Bit algorithm. Main concept of image security using LSB algorithm is to replace bit value of image at specific location so that created pattern. The pattern result of replacing the bit value of image is called by watermark. Giving watermark at image digital using LSB algorithm has simple concept so that the information which is embedded will lost easily when attacked such as noise attack or compression. So need modification like development of LSB algorithm. This is done to decrease distortion of watermark information against those attacks. In this research is divided by 6 process which are color extraction of cover image, busy area search, watermark embed, count the accuracy of watermark embed, watermark extraction, and count the accuracy of watermark extraction. Color extraction of cover image is process to get blue color component from cover image. Watermark information will embed at busy area by search the area which has the greatest number of unsure from cover image. Then watermark image is embedded into cover image so that produce watermarked image using some development of LSB algorithm and search the accuracy by count the Peak Signal to Noise Ratio value. Before the watermarked image is extracted, need to test by giving noise and doing compression into jpg format. The accuracy of extraction result is searched by count the Bit Error Rate value.
SEGMENTASI OBYEK PADA CITRA DIGITAL MENGGUNAKAN METODE OTSU THRESHOLDING Syafi?i, Slamet Imam; Wahyuningrum, Rima Tri; Muntasa, Arif
Jurnal Informatika Vol 13, No 1 (2015): MAY 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.347 KB) | DOI: 10.9744/informatika.13.1.1-8

Abstract

Digital image has size and object in the form of foreground and background. To separate it, it is necessary to be conducted the image segmentation process. Otsu thresholding method is one of image segmentation method. In this research is divided into five processes, which are input image, pre-processing, segmentation, cleaning, and accuracy calculation. First process was input color images which consists of multiple objects. Second process was conversion from color image to grayscale image. Third process was automatically calculated threshold value using Otsu thresholding method, followed by binary image transformation. The fourth process, the result of third process is changed into negative image as the segmentation results, noise removal with a threshold value of 150, and morphology. The last accuracy calculation is conducted to measure proposed segmentation method performance. The experimental result have been compared to the image of Ground Truth as the direct user observation to calculate accuracy. To examine the proposed method, Weizmann Segmentation Database is used as data set. It conconsist of 30 color images. The experimental results show that 93.33% accuracy were achieved.
Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image Arif Muntasa; Indah Agustien Sirajudin; Moch Kautsar Sophan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 3: September 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i3.97

Abstract

Diabetic Retinopathy is one of the diseases that have the effect of a high mortality rate after heart disease and cancer.  However, the disease can be early detected through blood vessels and the optic nerve head in Fundus images. Blood vessels separation of the optic nerve head required high effort when it is conducted manually, therefore it is necessary that the appropriate method to perform segmentation of the object. Level Set method is well-known as object segmentation method based on object deformable. However, the methods have the disadvantage; it requires initialization before the segmentation process. In this research, segmentation method without initialization process is proposed. The segmentation is conducted by using the maximum value selection results of convolution 8 directions. Experimental results show that, proposed method has obtained 89.48% accuracy. Segmentation errors are caused by small branches, where they are not connected, so that the objects are supposed as noises
Appearance Global and Local Structure Fusion for Face Image Recognition Arif Muntasa; Indah Agustien Sirajudin; Mauridhi Hery Purnomo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 1: April 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i1.678

Abstract

Principal component analysis (PCA) and linear descriminant analysis (LDA) are an extraction method based on appearance with the global structure features. The global structure features have a weakness; that is the local structure features can not be characterized. Whereas locality preserving projection (LPP) and orthogonal laplacianfaces (OLF) methods are an appearance extraction with the local structure features, but the global structure features are ignored. For both the global and the local structure features are very important. Feature extraction by using the global or the local structures is not enough. In this research, it is proposed to fuse the global and the local structure features based on appearance. The extraction results of PCA and LDA methods are fused to the extraction results of LPP. Modelling results were tested on the Olivetty Research Laboratory database face images. The experimental results show that our proposed method has achieved higher recognation rate than PCA, LDA, LPP and OLF Methods.
Multi-Criteria in Discriminant Analysis to Find the Dominant Features Arif Muntasa; Indah Agustien Siradjuddin; Rima Tri Wahyuningrum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3472

Abstract

A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra.  Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP). 
Contradictory of the Laplacian Smoothing Transform and Linear Discriminant Analysis Modeling to Extract the Face Image Features Arif Muntasa; Indah Agustien Siradjuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6576

Abstract

Laplacian smoothing transform uses the negative diagonal element to generate the new space. The negative diagonal elements will deliver the negative new spaces. The negative new spaces will cause decreasing of the dominant characteristics. Laplacian smoothing transform usually singular matrix, such that the matrix cannot be solved to obtain the ordered-eigenvalues and corresponding eigenvectors. In this research, we propose a modeling to generate the positive diagonal elements to obtain the positive new spaces. The secondly, we propose approach to overcome singularity matrix to found eigenvalues and eigenvectors. Firstly, the method is started to calculate contradictory of the laplacian smoothing matrix. Secondly, we calculate the new space modeling on the contradictory of the laplacian smoothing. Moreover, we calculate eigenvectors of the discriminant analysis. Fourth, we calculate the new space modeling on the discriminant analysis, select and merge features. The proposed method has been tested by using four databases, i.e. ORL, YALE, UoB, and local database (CAI-UTM). Overall, the results indicate that the proposed method can overcome two problems and deliver higher accuracy than similar methods. 
New Modelling of Modified Two Dimensional Fisherface Based Feature Extraction Arif Muntasa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 1: March 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i1.20

Abstract

Biometric researches have been interesting field for many researches included facial recognition. Crucial process of facial recognition is feature extraction. One Dimensional Linear Discriminant Analysis is one of feature extraction method is development of Principal Component Analysis mostly used by researches. But, it has limitation, it can efficiently work when number of training sets greater or equal than number of dimensions of image training set. This limitation has been overcome by using Two Dimensional Linear Discriminant Analysis. However, search value of matrix identity R and L by using Two Dimensional Linear Discriminant Analysis takes high cost, which is O(n3). In this research, the seeking of “Scatter between Class” and “Scatter within Class” by using Discriminant Analysis without having to find the value of R and L advance are proposed. Time complexity of proposed method is O(n2). Proposed method has been tested by using AT&T face image database. The experimental results show that maximum recognition rate of proposed method is 100%.
Ekstraksi Fitur Berbasis 2d-Discrete Cosine Transform dan Principal Component Analysis untuk Pengenalan Citra Wajah Arif Muntasa; Mochamad Kautshar Sophan
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2009
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Penelitian tentang pengenalan wajah telah mendapat perhatian banyak para peneliti, ekstraksi fiturmenggunakan basis sinyal telah banyak dilakukan, begitu pula dengan ekstraksi fitur yang berbasis statistikjuga telah banyak dilakukan. Pada penelitian ini penulis mengusulkan pendekatan ekstraksi fitur denganmenggabungkan metode yang berbasis sinyal dan berbasis statistik, untuk sinyal penulis menggunakan TwoDimensional-Discrete Cosine Transform (DCT-2D) dan untuk basis statistiknya penulis menggunakanPrincipal Component Analysis. Untuk Data pelatihan diekstraksi menggunakan DCT 2D, hasil ekstraksikemudian disusun menjadi matrik satu baris dan dinormalisasi. Hasil ekstraksi fitur selanjutnya direduksidimensinya menggunakan Principal Component Analisys (PCA). Untuk mengukur kemiripan hasil reduksidimensi, digunakan Euclidian Distance dan sudut antara dua vektor. Eksperimen pada citra wajah basisdataYALE, menghasilkan rata-rata akurasi pengenalan untuk 6 sampel masing-masing adalah 95.153%menggunakan Euclidian Distance dan 95.03% menggunakan sudut antara dua vektor. Sedangkan untuk 7sampel data pelatihan akurasinya adalah 95.57% menggunakan euclidian distance dan 95.62% menggunakansudut antara dua vektor. Usulan metode yang penulis usulkan juga dibandingkan dengan metode lain, yaituMarkov Random Field (MRF) dan Segmentasi 2D-DCT. Hasil perbandingan menunjukkan, untuk 6 dan 7sampel, metode yang penulis usulkan lebih rendah akurasinya dibandingkan metode MRF. Dibandingkandengan metode Segmentasi 2D-DCT, untuk 6 sampel data pelatihan metode yang penulis usulkan lebih tinggiakurasinya, sedangkan untuk 7 sampel data pelatihan metode Segmentasi 2D-DCT lebih tinggi akurasinya.Kata Kunci : 2D-Discrete Cosine Transform, Principal Component Analysis, Euclidian Distance.
Enhancement of the Adaptive Shape Variants Average Values by Using Eight Movement Directions for Multi-Features Detection of Facial Sketch Arif Muntasa; Mochammad Kautsar Shopan; Mauridhi Hery Purnomo; Kondo Kunio
Journal of ICT Research and Applications Vol. 6 No. 1 (2012)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2012.6.1.1

Abstract

This paper aims to detect multi features of a facial sketch by using a novel approach. The detection of multi features of facial sketch has been conducted by several researchers, but they mainly considered frontal face sketches as object samples. In fact, the detection of multi features of facial sketch with certain angle is very important to assist police for describing the criminal's face, when criminal's face only appears on certain angle. Integration of the maximum line gradient value enhancement and the level set methods was implemented to detect facial features sketches with tilt angle to 15 degrees. However, these methods tend to move towards non features when there are a lot of graffiti around the shape. To overcome this weakness, the author proposes a novel approach to move the shape by adding a parameter to control the movement based on enhancement of the adaptive shape variants average values with 8 movement directions. The experimental results show that the proposed method can improve the detection accuracy up to 92.74%.
Identification of Pedestrians Attributes Based on Multi-Class Multi-Label Classification using Convolutional Neural Network (CNN) Wrida Adi Wardana; Indah Agustien Siradjuddin; Arif Muntasa
Journal of Data Science and Its Applications Vol 3 No 1 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.43

Abstract

The usage of computer vision in identifying pedestrians attributes has received a great attention, especially in the visual surveillance systems. For instance, searching for system based on the attributes. Attributes Identification using Convolutional Neural Network architecture is presented in this article, since the architecture can perform feature learning. CNN consist of convolution layer, ReLU, Pooling, and Fully-connected. There are three experiment scenarios are conducted based on the number of convolution layers, to determine the effect of layers on CNN performance. Three different CNN architectures were trained and tested using a PETA dataset with 35 attributes. The highest accuracy achieved is 75.66% based on number of convolutional layers. The conducted experiments showed that more numbers of convolution layers used would produce the better CNN's performance.
Co-Authors Abd. Rasyid Syamsuri Alamsyah, Nurwahyu Ardi Hamzah Hakam Muzakki Husni Husni Husni, H. Imam Wahyudi Indah Agustien Sirajudin Indah Agustien Sirajudin Kondo Kunio Kurniawa Eka Permana Kurniawan Kurniawan Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mochamad Kautshar Sophan Mochammad Kautsar Shopan Mochammad Kautsar Sophan Muhammad Yusuf Nakkas, Haythem Osman, Zulfi Permana, Kuniawan Eka Putro, Sigit Susanto Rahmat, Abdullah Basuki Rima Tri Wahyuningrum Risky Mezi Muria Sari, Putri Pradnyawidya Savitri, Vina Angelina Sa’id, Moh. Siradjuddin, Indah Agustien Syafi?i, Slamet Imam Syarif, Muh. Wrida Adi Wardana