Articles
<![CDATA[Algoritma Ekstraksi Video Frame Berdasarkan Analisis Histogramwarna Hcl ]]>
<![CDATA[Ire Puspa Wardhani]]>;
<![CDATA[Sarifuddin Madenda]]>
<![CDATA[ Jurnal Ilmiah KOMPUTASI Vol 15, No 2 (2016): Desember ]]>
Publisher : <![CDATA[STMIK JAKARTA STI&K ]]>
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<![CDATA[Video digital merupakan representasi informasi melalui sekumpulan citra yang diakuisisi dan ditampilkan sesuai dengan standar scanning system, frame rate, dan frame size yang digunakan oleh teknologi video tersebut. Kumpulan citra yang direkam secara sekuensial akan dapat memberikan informasi tentang semua objek yang terkandung dalam video tersebut dan pergerakannya menurut fungsi waktu. Untuk itu analisis video sering juga disebut sebagai motion analysis. Citra video digital dapat diperoleh dengan menggunakan camera video digital atau perangkat yang dilengkapi camera video digital. Komputer yang dilengkapi dengan webcam dapat pula digunakan untuk merekam citra video digital.Saat ini melihat dan mengakses video berdasarkan konten tertentu merupakan penelitian yang penting, karena kebutuhan analisa data yang begitu besar dalam bentuk digital. Dengan banyaknya frame video dalam penyimpanan data menyebabkan beberapa permasalahan muncul seperti waktu yang dibutuhkan dalam proses membaca video dan memecahkan menjadi frame, mengelompokkan frame dan menemukan serta mengenali frame berdasarkan keyframe tertentu. Untuk mengatasi isu tersebut diperlukan metoda akses dalam mengenali frame yang dibutuhkan berbasis konten yang efektif dan efisien.  Kata Kunci : ekstraksi, video frame, histogram warna HCLJurnal Ilmiah KOMPUTASI, Volume 15 Nomor : 2, Desember 2016 ISSN : 1412-9434]]>
<![CDATA[DESAIN SKEMATIK ALGORITMA HISTOGRAM UNTUK KEBUTUHAN ANALISIS TEKSTUR CITRA BERBASIS FPGA (Field Programmable Gate Array) ]]>
<![CDATA[Pertiwi, Atit]]>;
<![CDATA[Madenda, Sarifuddin]]>;
<![CDATA[Sudiro, Sunny Arief]]>
<![CDATA[ Prosiding KOMMIT 2014 ]]>
Publisher : <![CDATA[Prosiding KOMMIT ]]>
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<![CDATA[Makalah ini menyajikan desain skematik algoritma histogram denganmenggunakan FPGA (Field Programmable Gate Array) untuk analisis tekstursecara real time. Desain algoritma histogram dibangun oleh komponen digitaldecoder dan counter. Secara khusus komponen digital decoder yangdigunakan adalah 8 to 256 decoder yang berfungsi untuk menentukan nilaiderajat keabuan suatu citra dan komponen digital counter 16 bit yangdirancang khusus untuk menghitung jumlah kejadian kemungkinan munculnyanilai intensitas derajat keabuan suatu citra yang berkisar antara 0 – 255.Pemrosesan algoritma secara parallel di dalam FPGA dapat meningkatkankecepatan proses dan penggunaan sumberdaya yang lebih efisien. Denganperforma yang tinggi dapat digunakan dalam aplikasi seperti diagnose medisdan deteksi target. Pembuatan desain algoritma histogram ini menggunakanperangkat lunak xilinx ISE 9.2i yang compatible dengan FPGA Spartan 3e.]]>
<![CDATA[AN OPTIMAL EDGE DETECTOR FOR AUTOMATIC SHAPE EXTRACTION IN CBIR APPLICATIONS ]]>
<![CDATA[Madenda, Sarifuddin]]>
<![CDATA[ Jurnal Ilmiah Informatika Komputer Vol 11, No 3 (2006) ]]>
Publisher : <![CDATA[Universitas Gunadarma ]]>
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<![CDATA[Detecting edges in digital images is a trickly operation in image processing since images may contain areas with different degrees of noise, blurring and sharpeness. This operation represents an important step of the whole process of similarity shape analysis and retrieval. There is a variety of edge detectors and each detector has different performances depending on image properties. Several edge detectors are very effective for sharp images but sensitive to noise while some detectors are well adapted to both sharp and noisy images (depending on the filter parameters) but are sensitive to blurring. This article presents two smoothing and detection filters which are well adapted to the detection of blurred or/and noisy edges. Their development is based on a model of blurred contours. These filters can be implemented in a third-order recursive form and offer advantages in the analysis of different edge types (sharp, noisy and blurred). Experimental analysis shows that the results obtained by these filters give a definitely better quality of the edge detection with respect to the existing filters. They also provide a better detection and good edge localization.Keywords : edge detector, automatic shape extraction, CBIR application]]>
<![CDATA[A NEW HCL COLOR SPACE WITH ASSOCIATED COLOR SIMILARITY MEASURE FOR COLOR-BASED IMAGE RETRIEVAL ]]>
<![CDATA[Madenda, Sarifuddin]]>
<![CDATA[ Jurnal Ilmiah Informatika Komputer Vol 11, No 2 (2006) ]]>
Publisher : <![CDATA[Universitas Gunadarma ]]>
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<![CDATA[Color analysis is frequently used in image/video retrieval. However, many existing color spaces and color distances fail to capture correctly color differences usually perceived by the human eye. The objective of this paper is first to highlight the limitations of existing color spaces and similarity measures in representing human perception of colors, and then to propose (i) a new perceptual color space model called HCL, and (ii) an associated color similarity measure denoted DHCL. Experimental results show that using DHCL on the new color space leads to a solution very close to human perception of colors and hence to a potentially more effective content-based image/video retrieval. Moreover, the application of the similarity measure DHCL to other spaces like HSV leads to a better retrieval effectiveness. A comparison of HCL against L*C*H and CIECAM02 spaces using color histograms and a similarity distance based on Dirichlet distribution illustrates the good performance of HCL for a collection of 3500 images of different kinds.Key words : HCL color space, color analysis]]>
<![CDATA[NEW APPROACH OF SIGNED BINARY NUMBERS MULTIPLICATION AND ITS IMPLEMENTATION ON FPGA ]]>
<![CDATA[Madenda, Sarifuddin]]>;
<![CDATA[Harmanto, Suryadi]]>
<![CDATA[ Jurnal Ilmiah Teknologi dan Rekayasa Vol 26, No 1 (2021) ]]>
Publisher : <![CDATA[Universitas Gunadarma ]]>
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DOI: 10.35760/tr.2021.v26i1.3703
<![CDATA[This paper proposes a new model of signed binary multiplication. This model is formulated mathematically and can handle four types of binary multipliers: signed positive numbers multiplied by signed positive numbers (SPN-by-SPN); signed positive numbers multiplied by signed negative numbers (SPN-by-SNN); signed negative numbers multiplied by signed positive numbers (SNN-by-SPN); and signed negative numbers multiplied by signed negative numbers (SNN-by-SNN). The proposed model has a low complexity algorithm, is easy to implement in software coding and integrated in a hardware FPGA (Field-Programmable Gate Array), and is more powerful compared to the modified Baugh-Wooley's model.]]>
<![CDATA[Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations ]]>
<![CDATA[Dewi Putrie Lestari]]>;
<![CDATA[Sarifuddin Madenda]]>;
<![CDATA[Ernastuti Ernastuti]]>;
<![CDATA[Eri Prasetyo Wibowo]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v7i1.pp383-391
<![CDATA[Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.]]>
<![CDATA[Cursive Handwriting Segmentation using Ideal Distance Approach ]]>
<![CDATA[Fitrianingsih Fitrianingsih]]>;
<![CDATA[Sarifuddin Madenda]]>;
<![CDATA[Ernastuti Ernastuti]]>;
<![CDATA[Suryarini Widodo]]>;
<![CDATA[Rodiah Rodiah]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v7i5.pp2863-2872
<![CDATA[Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.]]>
<![CDATA[Wood Classification Based on Edge Detections and Texture Features Selection ]]>
<![CDATA[Achmad Fahrurozi]]>;
<![CDATA[Sarifuddin Madenda]]>;
<![CDATA[Ernastuti Ernastuti]]>;
<![CDATA[Djati Kerami]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v6i5.pp2167-2175
<![CDATA[One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.]]>
<![CDATA[Optic Disc and Macula Localization from Retinal Optical Coherence Tomography and Fundus Image ]]>
<![CDATA[Rodiah Rodiah]]>;
<![CDATA[Sarifuddin Madenda]]>;
<![CDATA[Diana Tri Susetianigtias]]>;
<![CDATA[Dewi Agushinta Rahayu]]>;
<![CDATA[Ety Sutanty]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v8i6.pp5050-5060
<![CDATA[This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of window’s filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema.]]>
<![CDATA[Detection of Proximal Caries at The Molar Teeth Using Edge Enhancement Algorithm ]]>
<![CDATA[Jufriadif Na'am]]>;
<![CDATA[Johan Harlan]]>;
<![CDATA[Sarifuddin Madenda]]>;
<![CDATA[Julius Santony]]>;
<![CDATA[Catur Suharinto]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v8i5.pp3259-3266
<![CDATA[Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries. ]]>
